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Format for human body modelling from 3-D body scanning Peter R.M. Jones, Peng Li, Katherine Brooke-Wavell and Gordon M. West
Format for human body modelling 7
HUMAG Research Group, Department of Human Sciences, University of Loughborough, Loughborough, UK Introduction Computerized three-dimensional (3-D) models of the human body have recently attracted considerable attention in the clothing industry. It has been recognized that the clothing industry needs 3-D human body models to improve the manufacture of design and display stands (manikins) which should represent the average shape and size of the population[1]. The 3-D computer-aided clothing design (CACD) systems, although still in the development stage, require the 3-D body models as a basic element in design and evaluation of clothing[2-4]. Commercial 3-D CACD systems, for example Concept3D (Computer Design Inc., Grand Rapids), require real 3-D body models to enhance their applicability. The 3-D human body model could be built on the basis of surface data from human beings. However, data collection from the human body surface was a very difficult task before the 3-D body scanner (or digitizer) became available. The 3-D scanner can provide accurate surface co-ordinates of a body in two to three minutes or less and is a novel and useful tool in computer-aided clothing production. The Loughborough Anthropometric Shadow Scanner (LASS) was developed by our research group to allow digitization of the human body[5]. It is an automated, computerized television 3-D measurement system based on triangulation. The subject being scanned stands on a turnable platform and is rotated through 360 degrees in measured angular increments. A slit of light is projected on to the body in a vertical plane which passes through the centre of rotation. A column of cameras is used to read the image of projected light. From the camera image of the edge of the light slit, the height (h) and horizontal radii (r) of the body at the vertical plane can be easily calculated (as shown in Figure 1). Therefore measured data are 3-D surface co-ordinates of a body in cylinder co-ordinate form. The resolution of measurements in the vertical and the radial This research was funded by the ACME Directorate of the SERC and by Marks & Spencer plc, Courtaulds (Daintyfit) plc, Kennett & Lindsell Ltd, Fermark, Celestion, Bentwood and Bairdwear. The authors are grateful to Miss Louise M. Deamer for her initial contribution to this work.
International Journal of Clothing Science and Technology, Vol. 7 No. 1, 1995, pp. 7-16. © MCB University Press, 0955-6222
IJCST 7,1 Slit of light
8 θ
r
h
Figure 1. Principle of LASS scanner
Centre of rotation
directions are 1mm and 1.6mm respectively, according to the camera resolution. For a person of average height about 30,000 data points are acquired by LASS after initial data reduction from 315,000 raw data points. Until recently, only a few commercial automated 3-D body scanners covering the whole body have been reported[6,7]. Although these work on different principles, the amount of data produced by them is also large. Further reduction was required to assemble data into a form suitable for specific applications. This article addresses these issues and their applications in human body modelling for the clothing industry. Data reduction: LASS shape matrix Criteria for data reduction Tasks in body modelling based on 3-D scanned data are basically a surface fitting problem. Usually the objective of data reduction is to achieve an optimal fitting using the smallest number of data points. However, there are some further considerations in practice. For a surface model of the human body to be used in the clothing industry, there are several factors which should be taken into consideration:
Reduced data sets should be easy to output to a geometric modeller for computer-aided design (CAD) systems. ● Reduced data sets should contain major anthropometric information used in the clothing industry. ● Reduced data sets from a group of scanned individuals should allow comparison and averaging. ●
Data format suitable for CAD packages. Most modern CAD packages can represent a 3-D surface in many data forms, most commonly polygon patches and blend splines. Among different techniques of surface blending, the skinning technique is a suitable candidate for representation of the human body surface since it interpolates a family of cross-sectional curves. Polygon form, if not related to an efficient reconstruction algorithm, would be inaccurate or uneconomic. The format of the data file for exchange between different hardware and software platforms should be written in plain text. In the CAD domain, for example, two widely used text file formats are Initial Graphics Exchange Specification (IGES) and AutoCAD’s DXF. Unfortunately, they represent too many geometric entities and are unnecessarily complicated for our application. The body can be more simply represented using only data for one surface entity and the necessary auxiliary information. Anthropometric information needed in clothing industry. Anthropometry, as required for clothing design, mainly consists of circumferences, length and width measurements. The circumferences related to the human trunk are usually taken at the levels of hips, waist, underbust, maximum bust, chest and neck[8]. It is desirable that some of the anthropometric measurements can be easily extracted from the data file without interrogating surfaces generated from the data file. Comparison and averaging of body shapes. Size surveys from sample populations play an important role in obtaining standards for the sizing and the grading of garments. Three-dimensional scanners can now be used to conduct size and shape surveys of sample populations for the clothing industry. However, the data collected would be of limited use as a collection of individual measurements, so some means of describing the shape of the group as a whole is required. This might be achieved by taking an average which describes the size and shape of a particular sample. Calculation of a mean from the raw data is not sensible, as a particular element in the data file is unlikely to correspond to the same anatomical point on different people, because of the biological variation in the anthropometric measurement. To achieve correspondence of data points between files it is necessary to normalize data in such a way that individual anatomical points are coincident from one file to the next, which effectively standardizes for height. The choice of sections The cross-sections selected should ideally correspond to major body surface landmarks used in the clothing industry and sample population size surveys.
Format for human body modelling 9
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10
Moreover, some cross-sections should be located at the peaks and valleys of the body curve, since, if the heights of the sections are selected arbitrarily, some of the detail may be smoothed out in surface interpolation. It is also desirable for the cross-sectional slices of one body to correspond with those of another. Although proportional heights were generally similar, there are sufficient differences between individuals to indicate that a particular proportional height would not always correspond to the same anatomical site. To ensure that particular sections correspond with major anatomical features, the interactive selection of cross-sections was chosen. The major cross-sections chosen to represent a human torso are shown in Figure 2. They are at the level of the crotch, hips, waist, underbust, maximum bust, underarm, shoulder, base of neck and neck. Some middle cross-sections, identified by numbers, are inserted between any two major cross-sections in order to improve accuracy. A total of 32 cross-sections are used to describe a human torso. In this way, a particular slice will always correspond with a particular anatomical location; for instance, the underbust slice will always be slice 16 and the underarm slice 24 on every subject. Interpolation from the 32 cross-sections is used to create the 3-D body surface. Editing and fitting of cross-sectional data The raw data output from the LASS scanner must be edited and manipulated for the intended applications. For instance, the arms conceal the chest and abdominal region, while many of the required measurements (e.g. chest, waist, hips, etc.) must be measured inside the arms. For some applications, especially in the clothing industry, the shape of the human body is usually required to be
2 2 3
Line of textfile 31 Chin 29 Shoulder 27 Bideltoid 24 Underarms
5
3
19 Maxbust 16 Underbust
4 12 Waist 9
Number of interpolated slices 3 Max hips
Figure 2. Arrangement of slices in the shape matrix file
3 0 Crotch
symmetrical about the vertical axis. The raw data must be edited in some way to remove arms and to achieve symmetry. The editing is performed by fitting cubic splines to horizontal cross-sections. Software has been written to allow semi-automatic cubic spline curve fitting which accurately matches the original data, with the exception of the parts requiring editing, such as the arms. This software allows the manual manipulation of the cubic spline control points, and moves the cubic spline rather like stretching a rubber band. All the 32 cross-sections, as defined previously, may require manipulating in this way. The choice of the number of control points of the cubic spline is based on a compromise between ease of editing and the accuracy of fitting one slice of the body. As the human body is generally a rounded shape, eight control points were initially tried. Unfortunately these were insufficient to provide the required accuracy. Accordingly, 16 control points were used which satisfactorily defined the cubic spline representing one half of the body’s horizontal cross-section. The other half of the cross-section is a mirror image of the first half. It is then possible to create a family of slices where each is described by 16 control points. Shape matrix After the fitting of 32 slices, a shape matrix is produced. The shape matrix is a text (ASCII) file containing 16 x, 16 y and one z (height) co-ordinate values on each line. Eight of the 32 lines correspond to the anatomical landmarks as defined, but additional middle lines can be used to improve the accuracy of interpolation. The shape matrix thus contains 512 co-ordinate points to describe a torso. The space occupied by this file is now only 4 per cent of the size of the raw data. The shape matrix can be extended easily to allow representation of the leg, arm or an asymmetric body by adding a header to describe the number of data lines and a format flag. Figure 3 shows the format of the shape matrix. Uniforming control point spacing and standardizing posture The control points arising from cubic spline curve fitting do not necessarily occur at equal angles around the vertical axis of the body and their distribution is uneven. This results in distortion in surface interpolation, especially in the longitudinal (vertical) direction where resampling and uniformity of cross-sections are required. Again, the cubic spline is used to fit and to generate a cross-section. Resampling is then automatically performed so that the data points are defined at equal angular spacings around the vertical axis. Since there are substantial differences in posture between individuals, and the same individual may stand differently between one scan and another, the shape matrix requires further normalization if comparison of bodies is required. This normalization procedure is to remove the effects of posture before comparison. It is achieved by translating the centre of each slice to the geometric centre of y (anteropostero) axis, as shown in Figure 4.
Format for human body modelling 11
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N M RY DX DY X(1,1) Y(1,1) X(1,2) Y(1,2)
●
●
●
X(1,16) Y(1,16) Z(1)
X(2,1) Y(2,1) X(2,2) Y(2,2)
●
●
●
X(2,16) Y(2,16) Z(2)
●
12
● ●
X(N,1) Y(N,1) X(N,2) Y(N,2)
●
●
●
X(N,16) Y(N,16) Z(N)
Notes N = the number of row Figure 3. Format of LASS shape matrix
M = the mode of file, usual 0 RY, DX and DY = transformation information of cross-section in X-Y plane
a
b
To recentre the section, the radius vectors a and b are replaced by two vectors equal to (a + b)/2
Figure 4. Section showing re-centring methods
The application of LASS shape matrix 3-D surface modelling of the human body According to the LASS shape matrix, the surface of the human torso can be acquired by surface interpolation. The representation of the surface is based on the tensor-product form of surface[9,10]: m
n
S (u , v ) = ∑ ∑ Pi , j ⋅ N i , p (u ) ⋅ N j , q ( v )
(1)
i =0 j =0
where 0 < u, v < 1. Ni,p(u) and Nj,q (v) are basic blending functions of degree p and q in the u and v directions respectively. The vector control points, Pi, j , contain x, y and z co-ordinate values. With a set of known control points, a complete surface can be generated as parameters u and v vary from 0 to 1. If the blending functions are uniform degree-3 cubic splines, equation (1) takes its matrix form:
S (u , v ) = U T • M T • P • N • V where U = [1 u u 2 u 3 ], V p pi , j + 1 i, j p pi + 1, j + 1 and P = i + 1, j pi + 2, j pi + 2, j + 1 pi + 3, j pi + 3, j + 1
(2 )
= [1 v v 2 v 3 ] pi , j + 2 pi + 1, j + 2 pi + 2, j + 2 pi + 3, j + 2
pi , j + 3 pi + 1, j + 3 0 ≤ i ≤ m − 3, pi + 2, j + 3 pi + 3, j + 3
0 ≤ j ≤ n − 3.
Equation (2) presents a sub-surface generated by a 4 × 4 patch and UT • MT and N •V form the blending functions in u and v directions respectively. When some data points of a body surface are given, as in the LASS shape matrix, a complete surface can be regenerated by the surface interpolation, which is a procedure to find out all control points Pi,j according to known data points. Based on the matrix form (2), if using cardinal splines as the blending functions, the surface of the body can be calculated by taking the LASS shape matrix as control points[11]. For a general blending function, a tensor product interpolant scheme could be used, which involves solving m + 1 order (n + 1) × (n + 1) linear systems and n + 1 order (m + 1) × (m + 1) linear systems[10,12]. A more popular scheme, which is based on non-uniform B-spline, is the skinning interpolation. The skinning method interpolates the surface over a family of cross-sectional data. These data vary at the same parametric direction of equation (1), and therefore are often known as isoparametric curves. Moreover, all isoparametric curves should be normalized so that they have the same number of data points and same parameter distribution. More details about the skinning technique have been described by Woodward[13] and Piegl[14]. Figure 5 shows a complete flowchart from 3-D scanning to surface regeneration of the human body. Averaging and comparison of bodies Average bodies can be created from the normalized shape matrix (with normalized centre points and uniform angle spacing) of a group of subjects. However, a pure mathematical average body is less useful in clothing design. Key anthropometric measurements should be used to control the selection of a sample population. The choice of control variables depends entirely on the application. For example, the circumference of the underbust and maximum bust may be the key measurements for underwear design, and the circumference of waist and hip may be the key measurements for trouser design. Once the sample population has been selected, an average body can be created by averaging the shape matrices of the sample. By means of surface interpolation, an averaged 3-D body model can be created. This technique thus allows production of averaged 3-D body models for particular garment sizes.
Format for human body modelling 13
Methodology
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2. Newly developed software permits selective data removal
14
➞ 1. Raw data have been collected on women of a wide range of sizes and ages
3. A standardized data format permits the normalization, comparison of, and the facility to average different body shapes of differing heights
➞
Line of textfile 35 Chin 32 Shoulder 30 Bideltoid 27 Underarms
22 Maxbust 19 Underbust 16 Waist
6 Max hips 1 Crotch
➞
4. Software allows the extraction of measurements in any linear or oblique plane from the edited data
➞ Figure 5. Complete flowchart of data reduction and editing
Regeneration of a 3-D body shape from anthropometric measurement There may well be instances where 3-D body shape data are required where there is no access to a whole-body scanner, e.g. for production of bespoke clothing or for manufacture of manikins as fitting stands in realistic shapes to specified measurements. However, with a database of average body shapes, it is possible to create a 3-D body shape from a few simple measurements. This regenerated body could be of a size and shape determined by the measurements, in conjunction with average shapes from the database. From our research a 3-D database is being assembled which, at present, contains the shape matrices of more than 180 women, aged 16 to 60 years, of a variety of shapes and sizes. A series of average bodies was created, each corresponding to close-fitting garment sizes. These average bodies constitute a shape database which contains a set of eight “masterfiles”, each made up from four average bodies in a range of sizes. Eight masterfiles were used to provide information on different bust sizes, as bust size may vary independently from body size. The appropriate masterfile was selected according to the relative size of underbust and maximum bust circumferences. The regenerated body can then be created, by interpolating between average bodies. Further details are reported by West[15]. Discussion It has been demonstrated that the shape matrix is a suitable method for the representation of 3-D shapes of the human torso. An important application of the method is the provision of the ability to reduce data from 3-D scans of a large population to an average body shape. It is also useful to be able to measure changes in a body size and shape when measured at different time intervals even though it was not measured in the same position or posture. The shape matrix has been used to describe data from surveys of 155 women and 50 children aged three to 14 years. These data have, to date, been used in a range of applications. For example, body surface area of normal and liverdiseased children have been calculated accurately and used for the estimation of drug dosage[16]. Three-dimensional surface distance plots have been produced to allow checking of grading rules for garment manufacture. Manikins have been produced from average bodies, again for applications in the clothing industry[17]. Bespoke manikins of any particular individual’s shape can be produced from data from a body scanner. Size and shape of individuals and average shapes have been compared and plots produced which summarize differences. Data have also been used to create a 3-D body shape to specified anthropometric measurements using different approaches[15,18]. These approaches allow production of 3-D shapes to specified sizes, using average body shapes. Regenerated body shapes may be of use for manufacture of realistically shaped manikins to specified measurements, or for approximation of an individual’s shape when a body scanner is not available. With suitably written computer software it is easy to express data using any number of cross-sections or any number of points to facilitate the transfer of
Format for human body modelling 15
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data. In future it may be possible to integrate digitized 3-D body shapes directly with CAD/CAM systems, to allow automated design and manufacture of garments. This would allow standard sizes and shapes to be represented in shape matrix form, rather than using pattern blocks. References 1. Taylor, P. and Shoben, M., Grading for the Fashion Industry: The Theory and Practice, 2nd ed., Stanley Thornes, Cheltenham, 1990. 2. McCartney, J. and Hinds, B., “Computer-aided design of garments using digitized threedimensional surfaces”, Proceedings of the Institution of Mechanical Engineers, Vol. 206, 1992, pp. 199-206. 3. Okabe, H., Imaoka, H., Tomiha, T. and Niwaya, H., “Three-dimensional apparel CAD system”, SIGGRAPH ’92, Computer Graphics, Vol. 26 No. 2, 1992, pp. 105-10. 4. Carignan, M., Yang, Y., Thalmann, N. and Thalmann, D., “Dressing animated synthetic actors with complex deformable clothes”, SIGGRAPH ’92, Computer Graphics, Vol. 26 No. 4, 1992, pp. 99-104. 5. Jones, P., West, G., Harris, D. and Read, J., “The Loughborough Anthropometric Shadow Scanner (LASS)”, Endeavour, Vol. 13 No. 4, New series, 1989, pp. 164-8. 6. NKK Corporation, Voxelan 3-D Scanner, 1992. 7. Cyberware, “3D development”, Cyberware Newsletter, No. 1, 1993. 8. Jones, P. and Hunt, M., British Women’s Size Survey Age 17 to 69 Years, HUMAG Research Group, University of Loughborough, Loughborough, 1987. 9. Faux, T. and Pratt, M., Computational Geometry for Design and Manufacture, Ellis Horwood, Chichester, 1979. 10. Farin, G., Curves and Surfaces for Computer-aided Geometric Design, Academic Press, Boston, MA, 1988. 11. Bartels, R., Beatty, J. and Barsky, B., An Introduction to Splines for Use in Computer Graphics and Geometric Modelling, Morgan Kaufmann, Los Altos, CA, 1987. 12. de Boor, C., A Practical Guide to Splines, Springer-Verlag, New York, NY, 1978. 13. Woodward, C., “Skinning techniques for interactive B-spline surface interpolation”, Computer Aided Design, Vol. 20 No. 8, 1988, pp. 441-51. 14. Piegl, L., “On NURBS: a survey”, IEEE Computer Graphics & Application, Vol. 11 No. 1, 1991, pp. 55-71. 15. West, G.M., “Automated shape anthropometry”, PhD Thesis, University of Loughborough, Loughborough, 1993. 16. Jones, P., Baker, A., Hardy, C. and Mowat, A., “Measurement of body surface area in children with liver disease by a novel 3-D body-scanning device”, European Journal of Applied Physiology, Vol. 68 No. 6, 1994, pp. 514-8. 17. Jones, P., West, G. and Brooke-Wavell, K., “Interrogation of 3D body data for applications in manufacturing industries”, in Directorate of the Science and Engineering Research Council, Application of Computers to Manufacturing Engineering, Research Conference Proceedings, Sheffield University, 1993, pp. 20-5. 18. Li, P. and Jones, P., “Anthropometry-based surface modelling of the human torso”, Computer in Engineering 1994: Proceedings of the 1994 ASME International Computer in Engineering Conference, The American Society of Mechanical Engineers, Minneapolis, 1994, pp. 469-74.
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Environmentally friendly dyeing of synthetic fibres and textile accessories Dierk Knittel and Eckhard Schollmeyer Deutsches Textilforschungszentrum Nord-West, Krefeld, Germany Introduction The costs of using water or treating waste in industrial processes like conventional dyeing of textiles will increase significantly in future. Therefore new concepts have to be evaluated. For dyeing of unmodified polyethylene terephthalate (PETP) fabrics and for some other synthetic material only disperse dyes can be employed. Because of the hydrophobicity of such dyestuffs and of the fibre a conventional aqueous dyeing liquor has to contain large amounts of dispersing agents and surfactants in order to obtain reasonable dyeing rates and useful shades[1]. Considering the dissolving power of supercritical systems (SC systems) research has been done at Deutsches Textilforschungszentrum Nord-West (DTNW), Krefeld FRG, for the evaluation of those systems as a dyeing medium for disperse dyestuffs. Some of the promising results are presented in this article. The new system has been termed SFD (supercritical fluid dyeing)[2-8]. General considerations on supercritical fluid systems On changing temperature and pressure certain systems change to a state of new phase behaviour with novel properties. So, surpassing critical data for pressure ( p) and temperature (T ), a liquid or gas can be transferred to a socalled supercritical fluid (SCF). As an example: in a closed system containing a liquid, on heating, more vapour will develop, raising the pressure inside the system; with a further temperature rise a state is achieved in which both liquid and vapour have identical density, i.e. there is no boundary between the liquid and gaseous state; thus a supercritical state, as it is called, is achieved. A further increase in pressure will increase density and the dielectric constant of the system. Thus a dissolving power will be established for hydrophobic molecules like disperse dyestuffs. One system which is broadly investigated is carbon dioxide (CO2). The critical data of CO2 are in a convenient technical range (critical temperature 31.3 bar, critical pressure 72.9ºC[9]). In particular, the viscosity data of a supercritical fluid are valuable for use in dyeing procedures. Low viscosity results in high diffusion properties of dissolved molecules like dyestuffs. It can be anticipated that a supercritical fluid
International Journal of Clothing Science and Technology, Vol. 7 No. 1, 1995, pp. 36-45. © MCB University Press, 0955-6222
We are grateful to Ciba-Geigy, Switzerland, for supporting part of this research and to the country of Nordrhein-Westfalen (FRG) for institutional support.
will penetrate easily, even into the smallest pores, without the need of vigorous Environmentally convection forces. Table I shows such typical data of supercritical fluids friendly dyeing compared either with well-known liquids or with gases. SC fluids are therefore good candidates for replacing water-based dyeing systems for certain textile materials. Conventional water-based dyeing of synthetics requires a lot of auxiliary agents to provide a suitable dyeing liquor 37 and results in a product which has to be dried, requiring much energy. Often, too, another chemicals-and-water-consuming finishing process (i.e. reductive after treating) is needed to remove adherent disperse dyestuff. Figure 1 shows the energy and chemical situation for the new dyeing system (supercritical fluid dyeing or SFD), which will be simple, without the need of auxiliaries, after treatment or drying. Basic experiments on a laboratory scale For the systematic investigation of the dyeing capabilities a high-pressure laboratory apparatus has been built[4,5]. It consists of a heatable autoclave of 300cm3 capacity fitted with a pressure sealed stirrer. The system is safe to 500
Density Diffusioncoefficient Viscosity
Unit
Gas
Liquid
Supercritical fluid phase
[g * cm–3]
10–3
1
0.3 – 0.8
[cm2 * s–1] [g * cm–1 * s–1]
10–1 10–4
5 • 10–6 10–2
10–3 10–4
Table I. Typical important properties of supercritical fluid systems
CO2 cycle
Liquid
Gaseous
Supercritical Dyestuff
➡
Fabric
➡
Dyeing process
➡ No waste
➡
Dyed product
Figure 1. Schematic of the new process using supercritical CO2 (SFD) indicating energy and chemical streams required
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bar and 350ºC. Pressure is applied from a CO2 gas cylinder via a membrane compressor. The sample to be dyed (in the case of a fabric usually 10 to 25cm, or a polyamide (PA) stocking) is wrapped round a perforated stainless steel tube and mounted inside the autoclave. Dyestuff without auxiliary chemicals is placed on the bottom of the vessel and the apparatus is closed and preheated. On reaching working temperature CO2 is compressed to the working pressure under constant stirring. Pressure is maintained for the dyeing period of 0 to 60 minutes and released afterwards. Dyestuff description numbers like DTNW are a code of Ciba-Geigy[10]. Examples and results In the following some examples are given of some basic features of SFD and of promising dyeing results. Plate 1 shows results on polyester (PETP) dyeing using different disperse dyestuffs. As a guide a dyeing period of 10 minutes at 130°C and 20 minutes at 100°C is required for obtaining an exhaustion level for the dye of 98 per cent, resulting in dyestuff uptake of about 20 µmoles of dye per gram of fabric (depending on the selection of dyestuffs). Even dyeing periods as short as one minute at 100°C resulted in a dyestuff uptake of up to 6 µmoles per gram of PETP fabric. For the experiments shown in Plate 1 dyestuff uptake is in the range 0.2 to 22 µmoles per gram of fabric using 1.5 per cent (per weight of fabric) dyestuff in the autoclave (see Table II). The dyeing process can easily be regulated by varying either temperature or pressure. In the temperature range of 80 to 120°C there is a steep rise of isobaric exhaustion curves using pressures of 250 bar which decreases on further heating. Dyeing of PETP resulting in moderate shades can even be achieved at
Plate 1. Examples of SFDdyeing, screening experiments with different disperse dyestuffs on PETP
120°C using pressures exceeding 180 bar. The influence of pressure starts at Environmentally about 180 bar when dye uptake strongly increases with rising pressure. Figure friendly dyeing 2 illustrates the pressure and temperature dependence of dyestuff uptake for a selected sample. Since a dyestuff is dissolved monomolecularily in a supercritical fluid no filtering effects on dye aggregates by the fabric are to be expected, as are 39 observed in aqueous dispersions. Thus almost no aftertreatment like reductive washings is required (cf. easy dyeing of microfibres). If there are deposits of dyestuff on the goods, unfixed dyestuff may be removed by purging with
Dye-no DTNW
Dye/fabric [µmole/g]
Dye-no DTNW
Dye/fabric [µmole/g]
5 6 8 8 10 11
13.4 6.0 10.3 10.3 8.2 20.2
12 14 15 16 17 18
13.4 22.0 10.8 16.1 12.0 4.0
Dyestuff uptake (mg/g)
12
Dyestuff no. DTNW 12 Isothermal 120˚C
10
● ●
8 6 ●
4 2 ●
0 80
Dyestuff uptake (mg/g)
Table II. Selective values of dyestuff uptake per gram of fabric, screening experiments for disperse dye performance in SFD
120
●
●
160 200 Pressure (bar)
Dyestuff no. DTNW 12 Isobaric: 250 bar
9 6
240
280
● ●
●
3 ● ●
0 40
60
80
100 (˚C)
120
140
Figure 2. Dyestuff uptake isobaric and isothermal; otherwise standard conditions with dyestuff DTNW12
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Plate 2. Dyestuff sortiment and exhaustion data and polychromic dyeing
supercritical CO2 at lower temperatures (below the glass transition temperature of PETP). The screening experiments on different dyestuffs (cf. Plate 1) led to the evaluation of a sortiment of disperse dyestuffs suitable for the SFD process by Ciba-Geigy, Basel[10]. Plate 2 shows a compilation of a dyestuff sortiment for use in supercritical CO2[10]. Almost all colours are available with a high degree of exhaustion. High compatibility of dyestuff mixtures is obtainable as is shown for the trichomic case for obtaining black dyeings. It must be mentioned that
some other dyestuffs, conventionally classified as pigments, are applicable Environmentally from SC CO2 for PETP dyeing too[7]. friendly dyeing Dyeing of PETP microfilament fabrics, which are difficult to dye conventionally, gave excellent degrees of levelness in the CO2 process, showing
41
Plate 3. Examples of SFD; photographs of PETP fabric and PA stockings dyed with selected disperse dyestuffs in supercritical CO2
Plate 4. Examples of SFD with textile accessories, PA zippers and PA velcro tapes
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42 Plate 5. Dyeing of PETP, PA and synthetic horn knobs; blue-black: PETP with dyestuff DTNW 19; red-violet: PA with dyestuff DTNW 19; red: synthetic horn knob with dyestuff ethylred
Plate 6. Simultaneous dyeing of fibrous materials in supercritical CO2; dyestuff DTNW 27 at standard conditions of 130ºC, 250 bar, 15 minutes
Environmentally friendly dyeing
43
Plate 7. Photograph of the first SFD machine for dyeing PETP yarn spools on a technical level
even better abrasion resistance than using conventional aqueous dyeing systems[5]. Similarly foils and even thick PETP wires can be dyed evenly. Plates 3-5 show examples of dyeing results using the laboratory equipment. The samples show high levelness and high rubbing and washing fastness, so that
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no reductive aftertreatment is required. Up to now no significant change of tensile properties of SFD-treated PETP or polyamide (PA) could be detected[11]. The examples of Plates 4 and 5 demonstrate the applicability of SFD for dyeing textile accessories[8], a field in which technically a quick response on market requirements exists, and in which the lots to be dyed will not require large autoclaves. Apart from the materials already discussed, other synthetic fibrous materials like cellulose acetates can be dyed successfully. They exhibit even deeper shades with excellent degrees of levelness, using the same dyeing procedure. Experiments on dyeing aramides are in progress[12]. Whereas there are no obvious restraints on using this new technology on synthetics, severe problems exist in dyeing natural fibres. Plate 6 shows a simultaneous dyeing experiment on different kinds of fibres. The results show that natural fibres like cotton or wool are not amenable to SFD at present. The promising results of SFD on synthetics led to co-operation between research institute DTNW, dyestuff manufacturer and textile machine constructor. As an outcome of this co-operation, SFD dyeing apparatus of about 801 capacity for the dyeing of PETP yarn spools has been built and has been in technical evaluation for several months in a southern German factory. An apparatus for dyeing PA stockings will follow. Plate 7 shows a photograph of a yarn-dyeing machine[13]. Conclusion For the whole process of SFD for dyeing synthetic material like PETP or PA (and some others) using carbon dioxide as a fluid medium, several benefits can be summarized as follows: ● There is complete elimination of water pretreatment and of water pollution. ● There is saving of energy costs for drying textiles. ● There is no need for auxiliary agents. ● Dyeing occurs with a high degree of levelness and dyestuff exhaustion. ● Dyeing in a supercritical system requires very little time, thus giving high flexibility and promoting “just-in-time” delivery. ● In the case of PETP and PA no aftertreatment like reductive washing is required if dyeing is done properly. ● Carbon dioxide is non-toxic; it can be gained from natural sources. ● Carbon dioxide can be recycled easily in a dyeing process. Regarding the ecological and economic benefits obtainable by the SFD process, and considering the ease of pressure and the temperature-tuning possibility on dyeing results, there are new high-tech opportunities open to the textile finishing industry. Since SC fluids have been used for a long time for extraction
processes (e.g. for the decaffeination of coffee beans and for the extraction of Environmentally hops[14-16]), the high pressure technique is already in existence and has only to friendly dyeing be adapted to the special requirements of textile dyeing, so that rather quick technical development for the non-aqueous, non-polluting process will be possible. Notes and references 1. Schollmeyer, E., Heidemann, G. and Treptau, G., “Zur Egalität beim diskontinuierlichen Färben von Polyethylentere phthalat-Fasern mit Dispersionsfarbstoffen 1. Mitt., Physikalisch-chemische Grundlagen”, Textilveredlung, Vol. 16 No. 4, 1981, pp. 147-51 and Vol. 20 No. 6, 1985, pp. 190-8. 2. Poulakis, K., Spee, M., Schneider, G.-M., Knittel, D., Buschmann, H.-J. and Schollmeyer, E., “Farbung von Polyester in überkritischem CO2”, Chemiefasern/Textilindustrie, Vol. 41 No. 93, 1991, pp. 142-7. 3. Scheibli, P., Schlenker, W. and Strahm, U., “Färben in überkritischem Kohlendioxid – Quantensprung in der Ökologie der Textilveredlung”, Chemiefasern/Textilindustrie, Vol. 43 No. 95, 1993, pp. 410, 414. 4. Saus, W., Knittel, D. and Schollmeyer, E., “Färben aus überkritischem Kohlendioxid – Eine Alternative zur HT – Färbung von Polyester”, Textil Praxis International, Vol. 47 No. 11, 1992, pp. 1052-4. 5. Saus, W., Knittel, D. and Schollmeyer, E., “Dyeing of textiles in supercritical carbon dioxide”, Textile Research Journal, Vol. 63 No. 3, 1993, pp. 135-42. 6. Knittel, D., Saus, W. and Schollmeyer, E., “Application of supercritical carbon dioxide in finishing processes”, Journal Textile Institute, Vol. 84 No. 4, 1993, pp. 534-52. 7. Knittel, D. and Schollmeyer, E., “Färben aus überkritischem CO 2 – Versuche mit verschiedenen Farbstoffklassen”, Chemiefasern/Textilindustrie, 1994, (submitted). 8. Knittel, D., Saus, W. and Schollmeyer, E., “Färben von Fasermaterial mit carboxylgruppenhaltigen Farbstoffen unter Verwendung von überkritischem CO 2”, Chemiefasern/Textilindustrie, 1994, (submitted). 9. Angus, S., Armstrong, B. and deReuck, K.M. (Eds), Carbon Dioxide, International Thermodynamic Tables of the Fluid State. III. IUPAC Commission on Thermodynamics and Thermochemistry, Pergamon Press, Oxford, 1976. 10. Scheibli, P. and Schlenker, W., report for Ciba-Geigy, Basel, 1992. 11. Saus, W., Hoger, S., Knittel, D. and Schollmeyer, E., “Färben aus überkritischem Kohlendioxid – Dispersionsfarbstoffe und Baumwollgewebe”, Textilveredlung, Vol. 28 No. 3, 1992, pp. 38-40. 12. Saus, W., “Das Färben von technischen Fastern aus überkritischem Kohlen dioxid”, 4th International Techtextil-Symposium, Vol. 227, Frankfurt, FRG, June 1992, pp. 1-4. 13. Jasper, J., textile machine constructor, D-463642 Velen, FRG. 14. McHugh, M.A. and Krukonis, V.J., Supercritical Fluid Extraction, Butterworth, New York, NY, 1986. 15. Squires, T.G. and Paulaitis, M.E. (Eds), Supercritical Fluids – Chemical and Engineering Principles and Applications, American Chemical Society, Symposium Series 329, Los Angeles, CA, 1987. 16. Vollbrecht, R., “Extraction of hops with supercritical CO2”, Chemistry & Industry, Vol. 6 No. 6, 1982, pp. 397-405.
45
The role of decouplers in JIT pull apparel cells
The role of decouplers
J.T. Black and Joseph C. Chen Department of Industrial Engineering, Auburn University, Alabama, USA
17 Received and accepted January 1995
Introduction The just-in-time (JIT) manufacturing system has been introduced to various industries to produce superior-quality products on time and at a lower cost. These changes have been very successful in many industries, particularly in the automobile industry. However, today the apparel industry remains one of the most complicated and labour-intensive industries, which urgently require the implementation of the JIT system[1]. By applying JIT, the apparel industry can have quick-response capability to meet the changing preferences of the consumers. For this reason, the methodologies of converting an existing apparel factory into a factory using a JIT system are major issues for apparel manufacturers. Background During the past few decades, existing manufacturing systems have been challenged by many problems such as wage increases, global competitive increases, high quality requirement increases, and increases in the number and variety of products, resulting in lot size decreases. For this reason, many industries are making major design changes in their manufacturing systems to include flexible and controllable systems that produce superior-quality products on time and at a lower cost. Flexibility is the primary design characteristic of linked-cellular manufacturing systems (L-CMSs). In the manned cell, flexibility can be maintained by the elimination of set-up, development of multifunctional workers, small lot movement (particularly onepiece flow), and a U-shaped layout. To be flexible, the system must be able to react quickly to changes in product design and to changes in customers’ (both internal and external) demand. To accomplish these needs, Black and Schroer have developed a new class of devices called decouplers. Decouplers are part of a strategy to convert an existing factory into a factory with a future[2]. There is a ten-step strategy for this: (1) Design or re-engineer (reconfigure) the manufacturing system. (2) Reduce set-up time by changing the methods and designs. (3) Integrate quality control into the manufacturing system. (4) Integrate preventive maintenance. (5) Level and balance the manufacturing system to smooth material flow.
International Journal of Clothing Science and Technology, Vol. 7 No. 1, 1995, pp. 17-35. © MCB University Press, 0955-6222
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(6) Integrate production control; link cells; pull material to final assembly. (7) Integrate inventory control; reduce the WIP level. (8) Integrate vendors; make vendors just-in-time manufacturers just like you. (9) Apply automation; autonomously control both quality and quantity within the manufacturing system. (10) Design new products concurrently with customers in mind. The first step of this ten-step strategy is to design or reconfigure the manufacturing system. In particular, an L-CMS is formed where a portion of a firm’s manufacturing system has been grouped into cells. There are manufacturing cells and assembly cells. The cells are typically manned by multifunctional operators trained to be able to perform several tasks and operate many machines. In the cells, machines are placed next to one another by a Ushaped design to permit one-piece part movement within the cell. Decouplers are placed between the machines in the cell to perform functions of production control, quality control, part transport, process delay, and so forth[2]. Decouplers are elements of manufacturing cells and are so-named because they physically decouple (separate) one machine from the other. The term “decoupler” is derived from an axiomatic design methodology for designing a manufacturing system proposed by Suh et al.[3]. The design axioms can be used for many different classes of problems: the design of manufacturing systems, design of manufacturing processes, product evaluation, process planning, improvement of design organization, design of products, and design for manufacturing[4]. A set of axioms to manufacturing system design are listed below[5]: ● Minimize the number of functional requirements and constraints. ● Satisfy the primary functional requirements first. ● Minimize the information content. ● Decouple or separate off a solution if the functional requirements are coupled or become interdependent within the processes proposed. ● Integrate functional requirements in a solution only if they can be independently satisfied. ● All other things being equal, conserve materials. ● There may be several optimum solutions. Space does not permit an elaboration of this design philosophy, but the relationship between decouplers in manned cells and the axiomatic design approach has been proposed by Suh[4]. The decoupler’s major role is to provide flexibility that increases the cell’s output while integrating production control. Decouplers enable the cell to “make one, check one, and pass one on” by decoupling the processes within the cell so that they are independent of one another for time and function[6]. The simplest
decouplers hold stock-on-hand (SOH) and have specific input and output locations. Black and Schroer[5,7] did the first research on decouplers as part of an LCMS approach. The first integrated pull manufacturing cells with decouplers were simulated, using physical and digital models, to examine the influence of machining time and quality levels on the cells[5,7-9]. Based on these studies, the functions of decouplers are summarized as follows[5,9]: (1) Decouplers, which are elements of manufacturing systems and are placed between processes or workstations, enhance the flexibility in the cell by reducing the functional dependency of the side-by-side processes. (2) Decouplers permit flexibility in worker (or robot) movement. Workers can travel with or against the part flow. (3) Decouplers can perform 100 per cent inspection of the parts after the part comes out of the process. The decoupler is usually equipped to pass only good parts on to the next process. The decoupler may be designed to feed back quality information to the upstream process so that corrections to the process are made automatically. (4) Decouplers can also perform piece-part manipulations, part transport, part reorientation, and part reregistration for both manned and robotic cells. (5) Decouplers control the level of the SOH within the cell, allowing it to be raised or lowered whenever needed. (6) Decouplers can branch or combine part flows within the cell, so one machine can feed two or more machines, or two machines can feed one. (7) Decouplers can be designed for handling the family of parts that the cell is producing, so that between each machine is one part that has been completely processed by all the machines up to that point in time. (8) Decouplers can be used for process delay, to change the state of product (heat, cool, core, degrease, etc.) before the next process. Obviously, decouplers are uniquely designed elements for manufacturing cells which make the cells capable of producing superior quality parts. The purpose of this article is to review and discuss some staffing methodologies in forming an apparel assembly cell with walking workers and decouplers. Additionally, using simulation models, another decoupler characteristic was found; that is, increasing decoupler capacity improves cell output as the processing time variation increases, but little improvement occurs after increasing the decoupler capacity beyond two. Overview of the apparel industry in the USA Apparel is a labour-intensive industry, in which the operator has incentive to outperform the piece rate, allowing earnings and productivity to exceed a predicted level[10]. In the apparel industry, most of the machines are not
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automatic and must be attended by workers during the process. The bundle system is a common method for passing the parts or the partially finished garments from workers to workers. Some problems of this system have been summarized as: ● garments are usually piled up; ● the throughput time is very long; ● production schedules cannot keep pace with the fast-changing fashion market; ● quality is low; and ● the unit cost of a garment is high. Thus, the apparel industry in the USA has been faced with intensive competition from abroad to produce high quality merchandise at lower unit costs. Therefore, the apparel manufacturers have begun to adopt JIT concepts to eliminate excess inventory, long lead time, high defect rates and large amounts of capital tied up in raw material. Among these concepts is the Toyota sewing system (TSS), which is also called the modular production system in the USA. TSS was developed from Toyota’s manufacturing system used in the company’s automobile factories and was adapted for sewn products in 1978. TSS was known in the West in 1985 as Toyota’s “standing up system” because workers work standing up[11]. The features of the TSS (or so-called modules) are similar to the manned assembly cells; U-shaped, cross-trained workers, team work, making garments one-at-a-time (Figure 1). TSS has advantages, compared with the traditional bundle system, including less floor space, less creasing in garments, and a better working environment. In a TSS system, bundling and bundle-handling time is eliminated. 13
12
11
10
9
8
7
6 Precut pieces 1
2
3
Precut pieces Workstation number
Decoupler
Figure 1. Modular layout for apparel assembly cell
2 Workstation
4
5
In 1987, the American Apparel Manufacturing Association’s Apparel Research Committee (ARC) selected the modular production system as a project to study to see how well it can fit into the apparel industries[12]. A number of US apparel manufacturers are also investigating this system and comparing it with the traditional bundle system. Gilbert[13] pointed out that the advantages of a modular production system are a minimum of work-in-process and maximum use of capital investments. There was no other evidence to the advantage of the modular production system until 1990, when Kulers and Dewitt elaborated the encouraging results[14]. These include a reduction of throughput time, a drop in labour turnover with absenteeism falling, and fewer defects without raising the unit cost. However, several problems arose in the study at the same time and are summarized as follows: (1) Most manufacturers that have experimented with modular units are using some kind of group incentive to pay the workers. Unfortunately, group incentives sometimes cause quality problems because some workers in the group (or cell) might mistakenly think that the requirement for accurate work is not important. (2) The resistance of workers to operating machines in a standing position instead of in a traditional sitting position. (3) The machine used in apparel cells should be the type that is more adaptable and easily adjusted for fabric difference as well as for the ergonomical variation of the workers. The machine should be designed uniquely to have automatic setting ability to ease return to previous fabric setting without adjustments. To apparel and textile manufacturers, “just-in-time” (JIT) means manufacturing goods in a very short time, keeping inventories low, and responding quickly to their customers. Benefits for apparel and textile manufacturers include having fewer unsold goods, increasing overall sales, selling the right kinds of goods, and preventing further capture of the market share by offshore producers[15]. Integrating decouplers into an apparel assembly cell Although the just-in-time pull system has been proved to be very beneficial to the apparel industry, some of the literature reports about the drawbacks of a pull system brought the authors’ attention to do further research in this area. Sarker and Harris[16] evaluated the effects of the imbalance of stage operation times in a JIT system. They set up a variation of operation times at different locations in the operation line in order to see the effects of controlling a JIT production system with imbalance. In their study, the results ensured that the maximum benefits can be obtained in a JIT production system where the line is well balanced and adjustment to different uncommon and unusual situations can be made accordingly. It also noted that the JIT production line would not encounter much variation in utilization of work centres and production rates of the line if the fluctuating ratio was within 10 per cent of the operation time[16].
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Sarker and Fitzsimmons[17] investigated the effects of breakdowns and buffers in the push and pull systems. Their study found that the output rate of a pull system was more sensitive to a high variability of processing time than that of a push system, and the efficiency of a pull system decreased drastically at a non-linear rate with high variability. They also observed that a pull system was less efficient than the push system, especially at higher coefficients of variation[17]. This study might lead one to conclude that a pull system can only be used when the system has a lower variability in operation times. In this research, decouplers were integrated into the JIT pull manufacturing cell to allow the apparel industries to “make one, check one, and pass one on”, and to reduce the impact of processing time variation[18]. Using simulation models, an additional characteristic of decouplers in an apparel assembly cell has been discovered: increasing the decoupler capacity increases the cell’s output even as the processing time variation increases. Model description Simulation analysis has been successfully applied to determine the effect that adding work cell operators has on the average improvement in throughput rate[19]. Therefore, a manned apparel assembly cell was simulated using ProModelPC[20], a simulation package with a visual, interactive system that is particularly useful for modelling manufacturing systems. Familiarity with programming languages is not required. With its U-shaped layout (Figure 1) and ergonomically identical sewingmachines, the cell can be operated by a variable number of workers, each crosstrained in all the different processes. Each of the cell’s 13 workstations had a different mean processing time (Table I). Between each workstation was a decoupler that can hold a certain number of garments, one being the minimum number. The garments in the cell are the SOH. Several staffing methods were available for this apparel assembly cell. Three are described here. Rabbit chase Assume that there are two or more workers in an assembly cell. They follow each other around in the cell, doing all the processes in sequence[21]. A rabbit chase is typically used in an assembly cell in which the workers carry the parts from workstation to workstation. The need for precise line balancing for the entire cell or the partial loops is eliminated[7]. An apparel assembly cell requires no decouplers because the garment is always with the worker. After completing a garment at station 13, the operator returns to station 1 directly without moving backward and may start making another garment (Figure 2). The major disadvantage of this method is that the slowest worker or the variability in processing time dictates the cell’s output. For example, workers may be blocked by station 5 which has the longest processing time in this apparel assembly model. This blocking time increases the idle time and decreases the output rate of the cell. Figure 3 shows the output of an apparel cell
Time Workstation
Operation
1 2 3 4 5 6 7 8 9 10 11 12 13
Sew pockets Attach pockets of legs Connect legs Sew legs Sew leg opening Turn inside out Hem bottom legs Attach elastic band Stay Buttonhole Topstitch elastic Inspect Package garment
(seconds) 30 70 60 40 95 5 40 30 5 10 15 20 80
Total mean processing time per garment
13
12
The role of decouplers
500
11
10
9
23
Table I. Mean processing time
8
Worker travels with garment 7
6 Worker travels with garment Precut pieces 1
2
3
4
5
Precut pieces Workstation number
Decoupler 2 Workstation
using rabbit chase method, and Figure 4 shows that there was almost no blocking when three or fewer workers worked in the cell. As the variation of the processing time increased, the blocking occurred more frequently[22]. Additionally, this method requires every worker to operate all the machines or processes, which usually increases the cycle time, the possibility of making errors, and the training time for new workers.
Figure 2. Modular layout for rabbit chase method
24
Output (garments every six hours)
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209
226 + 222
226 + 222
6
7
+ 201 170 + 166 128 + 128 86 + 86 43 + 43 1
2
3
4 5 Number of workers
Key: Standard deviation (%) = 10 + = 20
Figure 3. Output for apparel cell with rabbit chase method
Source: [22] 28 26
24.87 +
24 22
23.64
Idle time (%)
20 18 16 13.63 +
14 12
12.25
10 8
6.21 +
6 4 2 0
Figure 4. Idle time percentage apparel cell with rabbit chase method
0 + 1
1.38 +
1.76 +
2
2.93 + 0.96
3 4 Number of workers
2.652 5
6
7
Key: Standard deviation (%) = 10 + = 20 Source: [22]
Toyota sewing system (TSS) The TSS uses multifunctional workers in the cell, with typically three to five workers operating ten to 15 machines. The TSS permits workers to share processes and to pass work to one another just as runners in a relay race pass the baton to one another at ten-metre sections of the track. Thus, the TSS design has processes that are called relay zones.
Workers travel counterclockwise with the garments, assembling them as they go from workstation 1 to 13 (Figure 5), but the workers do not cross the aisle. As long as a worker has a garment to assemble in a succeeding workstation, that worker travels counter-clockwise. When the worker is blocked, that worker puts the garment in the decoupler between the workstations and travels clockwise until finding another unfinished garment to assemble. This unfinished garment may be either in a decoupler or at another workstation. For example, let us look at a three-worker cell (workers A, B, and C). Worker A completes a garment at workstation 13. With no more garments to assemble, worker A then walks clockwise. If worker A finds a decoupler that contains an unfinished garment, worker A travels counter-clockwise and assembles the garment. If worker A finds a workstation where a garment is being assembled, worker A takes over the job from that workstation’s worker (B) and assembles the garment. Worker B then travels clockwise. If worker B finds a decoupler that contains an unfinished garment, worker B travels counter-clockwise and assembles the garment. If worker B finds another workstation where a garment is being assembled, worker B then takes over the job from that workstation’s worker (C) and assembles the garment. Worker C travels clockwise back to workstation 1, and then, travelling counter-clockwise, starts assembling a new garment. Over time, the workers develop work patterns, with certain workers taking the entire responsibility for certain workstations, and other workers sharing the responsibility for some workstations. The five-worker model described later in this article has a worker arrangement that is similar to the TSS. Black and Schroer[23] investigated this method using the WITNESS model and Figure 6 shows the plot of the overall
13
12
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10
9
The role of decouplers
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8
Worker travels with garment Worker travels when out of work
7
Worker travels when out of work
6
Precut pieces
Worker travels with garment 1
2
3
4
5
Precut pieces Workstation number
Decoupler 2 Workstation
Figure 5. Modular layout for Toyota sewing system (TSS)
Figure 6. Output of Toyota sewing system (TSS)
Maximum output for 95-second processing time
250 227
✕
216 +
200
150
173 +
130 + 43.33
100
100
211 211 + + Output for six hours
211 +
80
60 43.25
43.2
Output per worker
35.17
40
30.14 26.38
50
Output per worker (number of garments)
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Output for six hours of operation (number of garments)
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4
5 6 Number of workers
7
8
Source: [23]
output per six hours and the output per operator versus number of workers in the cell. Working balance This third staffing method allows workers to cross the aisle. The cell can be separated into sections with approximately equal processing times. The number of sections depends on the number of workers. Each section can be considered as a subcell linked by decouplers. Thus, a pull manufacturing system is formed by adding decouplers. The garment is pulled through these subcells one at a time. The subcells start making a garment only when the garment in the decoupler has been removed by the next worker or withdrawn from this cell. In our simulation model, the apparel assembly cell was managed with this working balance method. With three, four, or five workers in the cell, a system with three, four, or five subcells was individually simulated and reviewed. Assumptions and Constraints Nof et al.[24] reported that a manual worker’s pace varies during the workday. The variation in apparel-processing time significantly affects the throughput rate. The larger the processing time variation, the lower the output. One of the decoupler’s major roles is to reduce the impact of processing time variation. To find the relationship between processing time variation and decoupler capacity, simulation models with decoupler capacities of 1 to 6 were developed and processing time variation was allowed to increase (Table II).
Some assumptions and constraints used in constructing the simulation models were as follows: ● All workers had the same average efficiency at each workstation. This is obviously not true in practice because different workers do tasks at different average speeds. ● The workers had different amounts of variation about the mean processing time. Therefore, the processing time at each workstation followed the normal distribution with a standard deviation equal to a percentage of the mean time (Table I). ● The machines had no down time. ● The time for the workers to move between workstations or decouplers was assumed to be one second. The workstations and decouplers were all close to one another. ● Precut pieces were always available at workstations 1 and 2; therefore, the system never had to wait for pieces.
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Three-, four-, and five-worker models were developed individually using the simulation package. Thirty experiments were run for each model. Each experiment had a 24-hour simulation time and ran six times. Each model generated 180 outputs (garments per 24 hours). In each experiment, the average output was based on garments produced per six hours. Experiments and results Three-worker model Table III lists the workstation divisions and total processing times of the threeworker model, and Figure 7 shows the worker/subcell arrangement. The total Range (%) × mean processing time (seconds)
Model (number of workers) 3 4 5
Worker A B C
5 5 5
10 10 10
15 15 15
20 20 20
30 30 30
Workstation
Total processing time (seconds)
1, 9-13 2-4 5-8
160 170 170
Total mean processing time per garment
500
Table II. Levels of standard deviation for simulation models
Table III. Workstation division and total processing time in three-worker model
13
12
11
10
9
Worker A (subcell A)
28
Worker B (subcell B) Precut pieces
1
2
3
8 Worker C (subcell C)
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Precut pieces Subcell arrangement with workstation number Subcell A: 1, 9-13
Figure 7. Processing arrangement for threeworker model
Worker B (subcell B)
Subcell B: 2-4
Worker travels with garment
Subcell C: 5-8 Workstation number
Decoupler 2 Workstation
processing times (Table III) were not precisely balanced. Note that each worker was in charge of a subcell’s input and output, thus controlling the SOH within the cell. Figure 8 shows the plot of output versus decoupler capacity, and Figure 9 shows the plot of output versus processing time variation. When the decoupler capacity was more than 3, the average output was 125 units per six hours (2.88 minutes per garment) versus a theoretical maximum of 127 units per six hours (the bottleneck processing time of 2.83 minutes per garment). As the processing time variation increased, the model’s output decreased. This confirms the results of previous studies of a cell manned by one worker[18]. Figures 8 and 9 show that increasing decoupler capacity improved the output, reducing the effect of processing time variation. Note that increasing the decoupler capacity from 1 to 2 had the greatest effect. That is, decouplers that hold two items can eliminate the degrading effect of considerable processing time variation. Four-worker model Adding another worker and dividing the workstations into four subcells (Figure 10) increased the output as expected. Table IV lists the workstation divisions and total processing times. Note that each worker was in charge of a subcell’s input and output, thus controlling the SOH within the cell. Again, the total processing times could not be precisely balanced.
Output (garments every six hours)
127 126 125 124 123 122 121 120 119 118 117 116 115 114 113 112 111 110 109
+
+
+
+
✕
+ ✕
✕ ✕
✕
+
29
✕
1
2
3 4 Capacity of decouplers
5
6
Output (garments every six hours)
Key: Standard deviation (%) =5 = 20 ✕ = 30 + = 10 = 15
127 126 125 124 123 122 121 120 119 118 117 116 115 114 113 112 111 110 109
The role of decouplers
+ ✕
✕
+
Figure 8. Plot of output versus capacity of decouplers for three-worker model
✕
+
✕
+
✕
+
5
Key: Decoupler capacity =4 =1 + =2 ✕ =5 =6 =3
10
15 Levels of variation (%)
20
30
Figure 11 shows the plot of output versus decoupler capacity, and Figure 12 shows the plot of output versus processing time variation. When the decoupler’s capacity was more than 3, the average output was 158 units per six hours (2.28 minutes per garment) versus a theoretical maximum of 160 units per six hours (the bottleneck processing time of 2.25 minutes per garment).
Figure 9. Plot of output versus variability for threeworker model
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ell A) A (subc Worker
13
30
10
9
8
Worker D (subcell D) 7
6 Worker B (B)
Worker C (subcell C)
Precut pieces 1
2
3
Precut pieces
5
4
Subcell arrangement with workstation number Subcell A: 1, 13
Worker B (subcell B)
Subcell B: 2, 3 Worker travels with garment
Subcell C: 4, 5 Subcell D: 6-12
2 Workstation
Worker
Table IV. Workstation division and total processing time in four-worker model
Workstation number
Decoupler
Figure 10. Processing arrangement for fourworker model
A B C D
Workstation
Total processing time (seconds)
1, 13 2, 3 4, 5 6-12
110 130 135 125
Total mean processing time per garment
500
As the processing time variation increased, the model’s output decreased. Figures 11 and 12 show that increasing the decoupler capacity improved the output, reducing the effect of processing time variation. Note that, in this model also, increasing the decoupler capacity from 1 to 2 had the greatest effect. Five-worker model Adding one more worker to the four-worker model and dividing the workstations into five subcells (Figure 13) again increased the cell’s output. Table V lists the workstation divisions and total processing times. Note that
The role of decouplers
Output (garments every six hours)
(Four-worker model, six replicants) 161 160 159 158 157 156 155 154 153 152 151 150 149 148 147 146 145 144 143 142 141 140 139
+
+
+
✕
✕
+
✕
+
✕
✕
+
31
✕
1
2 3 4 Capacity of decouplers Key: Standard deviation (%) =5 = 20 ✕ = 30 + = 10 = 15
5
6
Figure 11. Plot of output versus capacity of decouplers for four-worker model
Output (garments every six hours)
(Four-worker model, six replicants) 161 160 159 158 157 156 155 154 153 152 151 150 149 148 147 146 145 144 143 142 141 140 139
+ ✕
5
Key: Decoupler capacity =1 =4 ✕ =5 + =2 =3 =6
✕ +
10
✕ +
+ ✕
✕
15 Levels of variation (%)
20
30
+
each worker was in charge of a subcell’s input and output, thus controlling the SOH within the cell. The total processing times were perfectly balanced – each worker processed each garment in 100 seconds. Figure 14 shows the plot of output versus decoupler capacity, and Figure 15 shows the plot of output versus processing time variation. When the decoupler
Figure 12. Plot of output versus variability for fourworker model
13
12
11
Worker E (subcell E)
10
9
8
Worker D (subcell D)
32 Worker A (subcell A)
Worker B (subcell B)
Precut pieces 1
2
Precut pieces Worker B (subcell B)
3
Worker C (subcell C)
IJCST 7,1
7
6
5
4
Subcell arrangement with workstation number Subcell A: 1, 2 Subcell B: 3, 4
Worker travels with garment
Subcell C: 5, 6 Subcell D: 7-11
Decoupler
Figure 13. Processing arrangement for fiveworker model
Subcell E: 12, 13
3 Workstation
Worker
Table V. Workstation division and total processing time in five-worker model
Workstation number
A B C D E
Workstation
Total processing time (seconds)
1, 2 3, 4 5, 6 7-11 12, 13
100 100 100 100 100
Total mean processing time per garment
500
capacity was more than 3, the average output for five workers was 210 units per six hours (1.71 minutes per garment) versus a theoretical maximum of 216 units per six hours. As the processing time variation increased, the model’s output decreased. Figures 14 and 15 show that increasing the decoupler capacity improved the output, reducing the effect of processing time variation. Note here also that increasing the decoupler capacity from 1 to 2 had the greatest effect.
(Five-worker model, six replicants)
Output (garments every six hours)
215 210
+
+
205 200
+ ✕
+
+
✕
✕
The role of decouplers
✕
+
✕
33
195 190 185 180 175 170
✕
1
2
3 4 Capacity of decouplers
5
6
Key: Standard deviation (%) =5 = 20 ✕ = 30 + = 10 = 15
Figure 14. Plot of output versus capacity of decouplers for five-worker model
(Five-worker model, six replicants)
Output (garments every six hours)
215 210
✕ +
✕ +
✕
+
205
✕
+
200
✕
+
195 190 185 180 175 170 5
10
15
20
30
Levels of variation (%)
Key: Decoupler capacity =1 =4 ✕ =5 + =2 =3 =6
Conclusion Comparing the TSS method with the rabbit chase method, the output was found to be approximately the same (Figures 3 and 6). However, the TSS method is
Figure 15. Plot of output versus variability for fiveworker model
IJCST 7,1
34
shown to be more flexible than the rabbit chase method in case of blocking because workers can help one another instead of staying idle. In this article, a working balance method was examined and found to have an output approximately the same as the above two methods. If the assembly cell can be divided into several subcells with approximately the same mean processing time, the assembly cell has the same feature as a cellular manufacturing system. The decouplers separate or decouple each subcell from others and link these subcells together to allow the system to have a “make one, check one, and pass one on” capability. The practical simulation models were developed to study not only the output of the working balance method in apparel assembly cell, but also the relationship between decoupler capacity and processing time variation. This study showed that processing time variation decreases the output and that increasing decoupler capacity improves cell output. But, improvements are very marginal when the decoupler capacity is more than 2, even for large amounts of processing time variation. References 1. Lin S., Kincade, D.H. and Warfield C., “Productivity and production in the apparel industry”, International Journal of Clothing Science and Technology, Vol. 6 No. 1, 1994, pp. 20-7. 2. Black, J.T., The Design of the Factory with a Future, McGraw-Hill, New York, NY, 1991. 3. Suh, N.P., Bell, A.C. and Gossard, D.C., “On an axiomatic approach to manufacturing and manufacturing systems”, Transaction of ASME, Journal of Engineering for Industry, Vol. 100 No. 2, 1978, pp. 127-30. 4. Suh, N.P., “Design axioms and quality control”, Robotics & Computer-Integrated Manufacturing, Vol. 9 No. 4/5, 1992, pp. 367-76. 5. Black, J.T. and Schroer, B.J., “Decouplers improve flexibility in cellular manufacturing systems”, Proceedings of Workshop on Automation and Robotics for Military Applications, US Army Missile Command, Huntsville, AL, October 1986, 15 pages. 6. Chen, J.C. and Black, J.T., “Pokayoke stoplight control in unmanned manufacturing cells”, Proceedings of the Third IERC, Atlanta, GA, May 1994, pp. 106-11. 7. Black, J.T. and Schroer, B.J., “Decouplers in integrated cellular manufacturing systems”, Journal of Engineering for Industry, Vol. 110, February 1988, pp. 77-85. 8. Chen, F.L., Joo, D. and Black, J.T., “Design, implementation and simulation of decouplers in unmanned manufacturing cells”, Transactions of the North American Manufacturing Research Institution of SME, May 1989, pp. 339-43. 9. Black, J.T. and Sipper, D., “Decouplers for integrated pull manufacturing”, in Black, J.T., Jiang, B.C. and Wiens, G.J. (Eds), The Design, Analysis and Control of Manufacturing Cells, Production Engineering Division of the American Society of Mechanical Engineers, New York, NY, PED-Vol. 53, December 1991, pp. 243-53. 10. Hundson, P.B., Guide to Apparel Manufacturing, MEDIApparel, Greensboro, NC 988. 11. Disher, M., “Investing in operators”, Apparel Industry Magazine, May 1987, pp. 72-6. 12. Kron, P., “Pondering modular”, Apparel Industry Magazine, August 1987, pp. 70-80.
13. Gilbert, C.S., “Tracking modular production”, Apparel Industry Magazine, April 1988, pp. 72-4. 14. Kulers, G.B. and Dewitt, J.W., “Modular goes mainstream”, Apparel Industry Magazine, May 1990, pp. 44-52.
The role of decouplers
15. Huge, E.C., “Just-in-time in apparel”, Apparel Industry Magazine, October 1987, pp. 70-4. 16. Sarker, B.R. and Harris, R.D., “The effect of imbalance in a just-in-time production system: a simulation study”, International Journal of Production Research, Vol. 26 No. 1, 1988, pp. 1-18. 17. Sarker, B.R. and Fitzsimmons, J.A., “The performance of push and pull systems: a simulation and comparative study”, International Journal of Production Research, Vol. 27 No. 10, 1989, pp. 1715-31. 18. Lulu, M. and Black, J.T., “Analysis of manufacturing cells operated by single servers”, Transactions of the North American Manufacturing Research Institution of SME, May 1990, pp. 379-85. 19. Steudel, H.J., “The role and design of workcells for world-class manufacturing”, The Journal of Applied Manufacturing Systems, Vol. 4 No. 2, 1991, pp. 47-55. 20. PROMODEL Corporation, ProModelPC: Discrete Event Simulation Software for Manufacturing and Service Systems, Version 5.01 [Computer Program User Manual, 280 pp.], PROMODEL Corporation, 1875 South State, Suite 3400, Orem, UT, 1992. 21. Suzaki, K., The New Manufacturing Challenge, The Free Press, New York, NY, 1987. 22. Tsai, Y.S., “Simulation of an apparel-manufacturing cell”, unpublished master’s thesis, Auburn University, AL, 1992. 23. Black, J.T. and Schroer, B.J., “Simulation of an apparel assembly cell with walking workers and decouplers”, Journal of Manufacturing Systems, Vol. 12, 1993, pp. 170-80. 24. Nof, S.Y., Gershoni, H., Ansell, S.D. and Cohen, A., “Industrial worker pace variability – a study with real time and posterior analysis”, AIIE Transactions, Vol. 10 No. 3, 1978, pp. 321-30.
35
IJCST 7,1
A new approach to fabric assessment J. Amirbayat and M.J. Alagha
46 Received May 1992 Revised and accepted December 1994
Department of Textiles, UMIST, Manchester, England. Introduction Objective measurements these days refer to measuring fabric properties by KES and FAST testers and to many technologists the values obtained, or a combination of them, are the best tools available for fabric evaluation. A comprehensive survey of experts’ views about the subject together with discussions are given by Stylios[1]. The present work shows how, by means of simple tensile tests, one can estimate these properties without needing any special attachments. Principles of the method The approach is based on two behaviours of orthotropic sheets: the interrelations of in-plane properties; and buckling of flexible sheets under tension. These points are discussed in some detail in the following sections. In-plane properties There are six elastic constants which relate three in-plane strains to three stresses of orthotropic sheets. Values of these elastic constants along different directions can be obtained from equations of generalized properties[2] as functions of elastic constants along axes of symmetry and the bias angle. Equations for variation of the tensile modulus, Yθ , the shear modulus, Gθ , and the ratio between the Poisson’s ratio, µθ and the tensile modulus along different directions can be expressed as: 1/Yθ = Cos2 θ/Y1 + Sin2 θ/Y2 + KSin2 θ. Cos2 θ
(1)
1/Gθ = 1/G – 4KSin2 θ. Cos2 θ
(2)
µθ /Yθ = µ/Y + KSin2 θ. Cos2 θ
(3)
with µ1 /Y1 = µ2/Y2 International Journal of Clothing Science and Technology, Vol. 7 No. 1, 1995, pp. 46-54. © MCB University Press, 0955-6222
and K = 1/G – [(1 + µ1) /Y1 + (1+µ2)/Y2 ]
(4)
where G is the principal shear modulus and Ys and µs refer to the principal A new approach tensile moduli and Poisson’s ratios respectively. Since, because of symmetry to fabric µ1Y1= µ2/Y2, there are four elastic constants which determine the properties assessment along the given bias angle θ . For isotropic sheets K = 0 where the shear modulus can be expressed in terms of the tensile modulus and the Poisson’s ratio and the elastic constants reduce to two independent values. For general 47 orthotropic sheets, depending on the sign of K, the distribution of these constants along bias angles may have minima or maxima as noted by Lloyd[3]. For textile fabrics, although these four elastic constants are independent of each other, K has a positive value which can be attributed to the low value of the principal shear modulus compared with principal tensile moduli and relatively small µ/Y along these directions. As a result the variations of Yθ ,Gθ and µθ /Yθ along bias angles are as shown schematically in Figure 1 which agree with previous experimental works[4,5]. Equations (l), (2) and (3) show that measuring tensile properties along three different directions can supply enough information to calculate Y1, Y2 and K, which contains two elastic constants. These equations are exact only for Hookean materials and small strains and therefore applicable to textile fabrics during their initial deformations. The general shape of anisotropic behaviour, however, follows these trends with reasonable agreement. As an example, combining equations (1) and (3) gives: (5) (1 + µθ) /Yθ = µ/Y + Cos2 θ/Y1 + Sin2 θ/Y2 + 2KSin2 θ. Cos2 θ which for θ = 45° becomes: (6) (1 + µ45 ) /Y45 = 1/2G This shows that the principal shear modulus can be calculated from the tensile properties along 45° bias without need of a shear tester which is the principle used by the designers of one of the evaluation systems (FAST) who obviously assumed a value for µ45. (It can be easily shown that the value of µ45 cannot exceed unity.) Similarly, if one takes advantage of the negligible value of µ1/Y1 = µ2/Y2 compared with 1/G, measurements of the tensile moduli along three bias angles
Y1 µ1 µ = Y1 Y
G12
Yθ
θ
θ
Y2
0 (a)
Gθ
G12
0 (b)
θ
µθ Yθ 45˚ µ2 µ = Y2 Y
0 (c)
Figure 1. Schematic presentation of variations of in-plane properties of textile fabrics along different directions: (a) tensile modulus; (b) shear modulus; (c) ratio between Poisson’s ratio and the tensile modulus
IJCST 7,1
48
can lead to good estimation of the principal properties. At the same time, load cycling along these directions gives a good estimation of planar hysteresis and recovery of the samples tested. Buckling under tension When a non-uniform load, a concentrated, distributed over a short distance or asymmetrically distributed, is applied on the edge of a plate, the stresses distribution along the direction of the applied load is non-uniform and lateral stresses of opposite signs to the applied load will develop as shown in Figure 2[6,7]. If the applied force is tensile, the laterally developed compressive stresses cause wrinkles or bulges when the intensity of the load (or externally imposed strain) increases beyond a limit which depends on properties and dimensions of the plate. The problem has been tackled by researchers interested in wrinkles developed during sheet metal formation[8] and from a different point of view by Amirbayat[9]. Formation of bulge is a problem of instability and its occurrence depends on the balance between the membrane and bending strain energies. The critical load (or extension) and the form of load-deformation curve along three different bias directions give valuable information about the out-of-plane properties as previous experimental work shows[10,11]. Experimental work Choice of test piece With regard to the foregoing discussions, three bias samples, loaded and unloaded by a force applied over a small portion of the edge, can serve to supply information about both in-plane and out-of-plane properties. The directions
Y P σy
σx 0.75
L
2
Figure 2. Distributions of normalized stresses, within a rectangular plate under a concentrated load, (L/W = 2), (a) axial stress; (b) lateral stress
1
x
0
0.5
y/(L/2) = 0.75
P W
y/(L/2) = 0 0.5
0
0 0.2 0.4 0.6 0.8 1 x/(W/2) (a)
0 0 –0.5
0.5
0 0.2 0.4 0.6 0.8 1 x/(W/2) (b)
selected are 45° bias which is most useful for determination of the shear A new approach properties and 22.5° bias from each principal direction. Length/width ratio of to fabric the sample chosen to be 2:1. assessment Sample preparation and test procedure Twelve woven fabrics with a wide range of properties were chosen for the experimental work. Strips of 24cm length and 5cm width were cut from each fabric along 22.5°, 45° and 72.5° using a special template. Since bias samples develop shear strain under tensile stress, the strips were folded in half to form a double ply of face-to-face fabrics 12cm long. An eyelet was then punched 1cm from the ply ends opposite to the fold. The second eyelet was inserted 10cm from the first one after a 75g load was uniformly applied over the fold of the sample to remove any possible slack of either ply. The samples were then subjected to a single loading-unloading cycle at a rate of 10mm/min with 100g maximum force using a simple attachment to the jaws of a constant rate of extension (CRE) tester. Figure 3 shows a sample at different stages of tensile test.
49
Test results Figure 4 shows a typical load extension curve obtained during testing of bias samples. For all fabrics the curve corresponding to 45° shows the highest final extension, A. Unlike simple force-extension curves of textile fabrics, these curves show an initial negative curvature which changes sign upon further extensions. The curves become almost linear at the end of loading. Unloading
(a)
(b)
(c)
Figure 3. Bias sample on constant rate of extension (CRE) tester, (a) unloaded; (b) loaded prior to buckling; (c) after buckling
100
100
A
Force, gf
Force, gf
IJCST 7,1
50 50
C 20
x
B
E
D
y HI
Figure 4. Typical loadingunloading diagram and parameters measured, (a) forces and extenstions; (b) areas
z
G 0
0 0.4 1 F
Extension, mm (a)
Extension, mm (b)
curves show no marked differences from those of conventional tests. Since the samples undergo a complicated state of stressing, the unusual form of the loadextension curves is to be expected. Parameters measured from load-extension curves. For each curve, six values of extensions were measured at 20, 50 and 100g forces, at the intersection of the curves and a straight line joining the highest point and origin, the width of the curves at this point and the residual extensions after unloading. Three forces at 0.4mm and 1mm extensions and the heights of the intersection points were also measured. Areas between the loading and unloading curves below and above the intersections as well as the areas under unloading curves have been determined. Estimation of properties from measured qualities. Figures 5, 6 and 7 give the plots of actual tensile bending and shear properties, measured by KES system against the estimated values from the present set of tests. Correlation between the properties and the measured parameters, A, B… and so on are given in the Appendix. Charts belonging to all different fabric properties contain 24 points, which is double the actual number of fabrics tested. This is done by: ● considering each fabric in its conventional sense and relating its warp properties to the parameters along 22.5°, 45° and 67.5° from direction I (the actual warp); ● considering each fabric rotated through 90° and relating its weft properties to the parameters along 67.5°, 45° and 22.5° from direction I. As a result each property along a principal direction is related to the parameters 22.5° (1), 45° (2) and 67.5° (3) from its direction.
17
EMT%
●
WT gf/cm
●
22
●
15
A new approach to fabric assessment
●
●
●
●
13 17 Measured
Measured
●
11 ●
9 ● ●
7 ●
5
●
● ● ●
3
● ●
● ●
● ● ●
7
1
3
● ●
●
●
N = 24 R = 0.786
● ●
5
7
9
11
13
15
2
17
2
7
12 Estimated
(a)
(b)
● ●
Measured
●
60
● ●
55
●
● ●
● ● ● ●
● ●
●
●
65
●
● ● ●● ● ● ●●● ●
● ●
70
● ●
●
22
RT%
70 65
17
Estimated
LT%
75
Measured
● ●
● ● ●
●
1
●
●
N = 24 R = 0.782
●
51
●
12
●
● ●
●
●
●
●
60
●
● ● ●
● ●
● ●
●
55
● ●
●
50
●
N = 24 R = 0.662
50
N = 24 R = 0.698 ●
45
45 45
50
55
60
65
70
75
45
50
55
60
Estimated
Estimated
(c)
(d)
65
70
Discussions and conclusions All correlations between the estimated and measured values of the properties are significant at 0.1 per cent level. Comparison between three sets of results shows that estimation of shear properties are more accurate than the other two sets, tensile and bending. The shear properties also have their highest correlations with parameters along 45° bias. Since the shear modulus can be estimated from tensile properties along this direction as previously mentioned, the accuracy in estimation of 2HG and 2HG5 which have high correlations with G is not unexpected. Tensile properties, on the contrary, show the lowest correlations between estimated and measured values, although the nature of tests is tensile and one expects a better set of results. In retrospect, examinations showed that the maximum load of 100gf has not
Figure 5. Estimated and measured values of tensile properties, (a) extension; (b) work of tension; (c) linearity; (d) resilience
IJCST 7,1
B gf.cm
2HB gf.cm ●
●
0.2
0.15
● ● ●
●
●
●
0.1
●
● ●
● ●
●
0.1
● ●
●
● ●
●
0.05
● ●
● ●● ●
● ●●
●
●
● ●
●
0.05
●
●
Figure 6. Estimated and measured values of bending properties, (a) bending stiffness; (b) bending hysteresis
Measured
52
Measured
0.15
N = 24 R = 0.712
● ● ●
●
● ● ●
●
N = 24 R = 0.797
● ●
●
0
0 0
0.05
0.1
0.15
0.2
0
0.1
0.05
Estimated (a)
0.15
Estimated (b)
2.5 1.6
G gf/cm.deg
2HG gf/cm
● ●
● ●
2
1.4
● ●
● ●
●
0.8
0.4 0.4
●
● ● ● ●
0.6
●
● ●
●
●
1
● ●●
● ●
1.2
1.4
0
1.6
0
0.5
●
5
● ●
●
4 Measured
1
1.5
Estimated (b) 2HG5 gf/cm
● ●
3
● ● ●
● ●
●
● ● ● ●
2
●
●
N = 24 R = 0.941
● ● ●
1
N = 24 R = 0.807
● ●
● ●
1
●
●
Estimated (a)
Figure 7. Estimated and measured shear properties, (a) shear modulus; (b) shear hysteresis at 0.5º; (c) shear hysteresis at 5º
●
●
●
0.5
N = 24 R = 0.922
0.8
● ●●
●
● ● ● ● ● ●
0.6
●
1.5
●
Measured
Measured
1.2 1
●
● ●
1
2
3
4
Estimated
5
2
2.5
been high enough to stretch the samples (after the initial buckling) A new approach sufficiently. As a result, there is not a significant correlation between tensile to fabric properties and the corresponding parameters, e.g. EMT and A, WT and (x =+ assessment y + z) and so on. Correlations between the recovered energy, Z, and not the sum (x + y +z), with the first three tensile properties indicate the need for further studies under different maximum load and/or using samples of 53 different dimensions. The bending properties estimated are in reasonable agreement with the measured values. Comparing the present results with the results of tests on suiting and shirting fabrics previously obtained, Figure 8[11] shows that grouping fabrics and separate statistical analysis give far better estimations.
●
●
B gf.cm
3.5
●
(All fabrics)
B gf.cm ● (Suiting fabrics)
3.5
●
3
3
●
● ● ●
●
2
2.5
Measured
Measured
2.5 ● ●
● ●
●
1.5 ● ●
● ●
1
● ●
●
●
●
0.5
● ● ● ●●
● ●
●
N = 36 R = 0.685
●
●
●
●
1
1.5
2
2.5
3
0.5 0.5
3.5
●
1
1.5
2
2.5
3
3.5
Estimated (b)
Estimated (a) ●
B gf.cm (Shirting fabrics) 1.5 ●
Measured
1
N = 20 R = 0.896
●
0 0.5
●
● ●
●
●
0
●
●
1.5
●● ●
● ● ● ●●
●
●
2
●
1 ●
●
0.5 ●
●
● ● ● ● ●
●
●
●
●
N = 16 R = 0.840
0 0
0.5
1 Estimated (c)
1.5
Figure 8. Estimated and measured bending stiffnesses (a) suiting and shirting fabrics; (b) suiting fabrics only; (c) shirting fabrics only
IJCST 7,1
54
References 1. Stylios, G., Textile Objective Measurements and Automation, Ellis Horwood, Chichester, 1991. 2. Hearmon, R.F.S., An Introduction to Applied Anisotropic Elasticity, Oxford University Press, Oxford, 1961. 3. Lloyd, D.W., “Analysis of complex fabric deformations”, in Hearle, J.W.S., Thwaites, J.J. and Amirbayat, J. (Eds), Mechanics of Flexible Fibre Assemblies, Sijthoff and Noordhoff, 1980, pp. 311-42. 4. Alsawaf, F., “Areas change as a measure of fabric performance”, Doctorate Thesis, University of Manchester, 1985. 5. Amirbayat, J., “Seams of different ply properties”, Journal Textile Institute, Vol. 83 No. 2, 1992, pp. 209-17. 6. Timoshenko, S. and Goodier, J.N., Theory of Elasticity, 2nd ed., McGraw-Hill, New York, NY, 1951. 7. Goodier, J.N., “Compression of rectangular blocks”, Trans. ASME, No. 54, 1932, pp. 173-83. 8. Segedin, R.H., Collins, I.F. and Segedin, C.M., “The elastic wrinkling of rectangular sheets”, International Journal of Mechanical Sciences, Vol. 30 No. 1, 1988, pp. 719-32. 9. Amirbayat, J., “The buckling of flexible sheets under tension, Part I”, Journal Textile Institute, Vol. 82 No. 1, 1990, pp. 61-70. 10. Amirbayat, J. “The buckling of flexible sheets under tension, Part II”, J. Text. Inst. Vol. 82 No. 1, 1990, pp. 71-7. 11. Anderson, V.J., “An alternative method of evaluation of fabric properties”, Department of Textiles, University of Manchester Institute of Science and Technology. Appendix
EMT WT LT RT B 2HB G 2HG 2HG5
Correlation coefficients of fabric properties Z1 G1 H1 0.648 0.454 –0.411 Z1 G1 H2 0.613 0.481 –0.457 Z1 A1 Z2 0.427 0.417 0.348 D1 Z3 Z2 0.570 0.457 0.442 1/C3 1/I2 1/Z2 0.437 0.424 0.364 1/X1 1/Z1 (X+Y+Z)1 0.750 0.610 –0.500 1/E2 1/A2 1/D2 0.905 0.878 0.822 G2 I2 D2 0.708 0.691 –0.665 D2 G2 A2 0.900 0.825 –0.823
B3 0.405 G2 –0.441 C1 –0.310 A1 –0.440 1/(X+Y )1 0.363 1/Z3 0.467 1/(X+Y )2 0.822 B2 –0.662 B2 –0.796
Note: subscripts 1, 2, 3 refer to 22.5º, 45º and 67.5º from directions along which the properties were measured
Towards automated testing of fabrics
Towards automated testing of fabrics
Prasad Potluri, Isaac Porat Department of Textiles, and
11
John Atkinson Department of Mechanical Engineering, University of Manchester Institute of Science and Technology, Manchester, UK Introduction Globalization of the clothing industry and an increased competition in the world market means that the consumer expects high-quality garments at affordable prices. The quality of a garment, as normally perceived by a customer, depends on its aesthetic appeal, its ability to drape gracefully, its “handle” and durability. Of course, the quality of a garment depends on the quality of the fabrics used and the making-up process. A more expensive fabric does not necessarily result in a better-quality garment. Colour and design of a fabric along with drape, contribute to the aesthetic appeal of a garment. “Handle” or “hand” is a more complex phenomenon related to a fabric as part of a garment, contacting human skin and subjected to small stresses due to body movement. Traditionally, fabric quality is expressed in terms of subjective “hand”, evaluated by individual experts in the clothing industry. It is important to establish what constitutes the quality of a fabric or a garment and how can this be assessed objectively. Objective assessment of fabric hand Fabric hand is traditionally evaluated by experts in the clothing industry. They examine the fabric by performing certain physical movements, such as stretching, bending, shearing and rubbing, and express their feelings in terms of subjective sensations, such as stiffness, limpness, hardness, softness, fullness, smoothness and roughness. These expressions form the basis for fabric selection, although this type of subjective evaluation is not very accurate. As early as 1930, Peirce[1] recognized the need for quantitative assessment of hand. In his classic paper, “The handle of cloth as a measurable quantity”, Peirce translated subjective physiological sensations into mechanical properties such as bending length, flexural rigidity and hardness, etc. Following the pioneering work of Peirce, several other researchers, notably, Howarth[2], and Dawes and Owen[3], pursued research into quantitative assessment of hand. Their common goal was to eliminate the human element in hand assessment and develop quantitative factors that could be measured in a laboratory. Based on a multiple factor analysis, Howarth[2] identified certain physical factors such as fabric weight, thickness, bending stiffness, hardness and insulation
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 11-23. © MCB University Press, 0955-6222
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property. Dawes and Owen[3,4] analysed subjective feelings such as stiffness, liveliness and smoothness, expressed by a group of judges, by correlating with fabric mechanical properties. However, the most significant contribution came from Kawabata[5] and his colleagues in Japan. In the early 1970s, the Hand Evaluation and Standardisation committee was formed, with the involvement of the Japanese clothing industry. The main objectives of the committee were to: ● standardize the terminology and procedures used in subjective hand evaluation; ● relate subjective hand expressions to fabric mechanical properties (see Table I). They developed empirical relations for primary hand expressions, such as koshi, shari, numeri and furukami, and developed the concept of total hand values based on these primary hand values. Controls of garment making-up process The aesthetic value of garments is affected by the making-up process. Production of high-value garments in particular, requires a relatively puckerfree seam. Lindberg et al.[6] were the first to consider the relationship between fabric properties and the problems encountered in garment construction and manufacture. This aspect has received considerable attention in recent years[710]. Kawabata et al.[11] presented the possibility of automatic control of overfeed in sewing machines based on fabric mechanical properties. To improve the competitiveness of the clothing industry, several researchers have been making attempts to automate the making-up process. Handling of fabric panels by robots requires prior knowledge of mechanical and frictional properties to predict their behaviour[12]. Fabric test
Low stress properties
Notation
Tensile test
Linearity Tensile energy Tensile resilience Extensibility Shear stiffness Hysteresis at 0.5° shear angle Hysteresis at 5° shear angle Bending rigidity Hysteresis Linearity Compression energy Compressional resilience Coefficient of friction Mean deviation of MIU Geometrical roughness Thickness Fabric weight
LT WT RT EM G 2HG 2HG5 B 2HB LC WC RC MIU MMD SMD T W
Shear test
Bending test Compression test
Surface test Table I. Relationship of subjective hand expressions to fabric mechanical properties
Thickness Weight
Units
gf cm/cm2 per cent gf/cm deg gf/cm gf/cm gf cm2/cm gf cm/cm gf cm/cm2 per cent
µm mm mg/cm2
Now that the concept of objective assessment is well established, finding new Towards application areas and a widespread acceptance in industry depends on the automated availability of reliable test equipment. Some current developments in this area testing of fabrics are reported in this article. Test equipment for objective measurement of fabrics Fabric tests may be broadly classified as follows: ● High-stress mechanical tests are used to measure properties such as tensile strength, tear strength or abrasion resistance. Fabric samples are subjected to relatively high stresses and the tests are usually conducted until failure occurs. ● Low-stress mechanical tests reflect the range of stresses that a fabric undergoes during normal use, which are important for handle evaluation. It is interesting to observe that the pioneers who proposed the concepts of objective measurement also had to develop certain test instruments to prove their concepts. Peirce[1] developed the “flexometer” to measure bending length and flexural rigidity of fabrics. This instrument was subsequently modified to measure the cantilever length of a fabric at a specific angle of 41.5°[13]. This instrument found widespread application, although it is based on a simplified assumption of linear elastic behaviour of fabrics. Grosberg and Swani[14] developed an improved version to measure both bending stiffness and frictional couple. Livesey and Owen[15] developed a pure bending tester for measuring actual non-linear behaviour of fabrics. Although this instrument is a significant improvement over a simple cantilever tester, its application was limited to research laboratories because of the tedious measurement procedures. Test instruments were also developed to measure other mechanical properties: for example, the Fryma fabric extensiometer[13] for measuring tensile elongation, the Vinto fabric shear angle tester[16] for measuring shear stiffness, the Shirley thickness gauge[13] and the Shirley fabric friction tester[13] for measuring the coefficient of friction. Common features of these instruments are: ● During mechanical tests, loads are applied using standard weights so the tests can be conducted only at discrete loads. ● Fabric deformations are measured using a ruler or a micrometer. ● Fabric displacements are applied manually, with no control over the test speeds. ● The test results are recorded manually and the mechanical properties are calculated using simple equations. These instruments may be classified as first-generation test systems, where all aspects of testing are performed by human operators.
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Real progress in objective measurement came with the development of the Kawabata Evaluation System (KES), manufactured by Kato Tekko[17]. The test system consists of four instruments: (1) Tensile and shear tester. A tensile test is conducted by clamping the sample between chucks. A shear test is conducted under a constant tension, provided by a dead weight attached to the fabric sample. (2) Bending tester. A fabric sample is mounted in a vertical plane and a pure curvature is applied to record moment-curvature relationships. (3) Compression tester. A fabric sample is compressed in the thickness direction, using a compression head, and the load-deformation curve is recorded. (4) Surface tester. Surface roughness and the coefficient of friction are measured using two contact sensors, one for measuring thickness variation and the other for measuring frictional force. The fabric sample is moved, relative to the sensors, under a constant tension. These instruments can test fabrics automatically and provide continuous stress-strain curves. Loads and deformations are measured using sensors and recorded using an x-y plotter. Fabric handling is completely manual and therefore this equipment needs a skilled operator to obtain repeatable results. Fabric Assurance by Simple Testing (FAST) was developed by CSIRO Division of Wool Technology[18] as a simpler alternative to a more sophisticated KES system. This system consists of the following individual instruments: ● compression meter; ● bending meter; ● extension meter. These instruments are similar in operation to conventional measuring instruments except that measurement is carried out using sensors, and the test results are displayed digitally. The need to improve the reliability of fabric testing by automation has been recognized. An automatic version of the Vinto shear angle tester was developed by TNO[19]. Automatic fabric clamping and pre-tensioning were introduced in a new version of the KES tensile and shear tester[20] which claimed to have improved accuracy of measurement and productivity. Possible errors due to manual positioning of samples were reported. This instrument has been developed based on principles of hard automation. Any desired changes in the test parameters or in the test sequence need physical adjustments on a test machine. Activity analysis of fabric testing The principal aims of the earlier researchers was to eliminate the human element in hand assessment. Although this has been achieved by transferring
the skills of an expert judge to test instruments, human involvement is not Towards eliminated in operating the test instruments. A systematic activity analysis has automated been conducted with a view to automating the testing process further. The testing of fabrics following nine activities are involved in testing fabrics: (1) preparation of fabric samples: fabric samples are usually cut from a large piece of fabric along the principal or bias directions; (2) handling of fabric samples to position into a test equipment; (3) alignment of a fabric sample: low stress properties are sensitive to fabric orientations; (4) clamping a fabric sample; (5) setting of initial loads: standard weights are either attached to the fabric sample or placed so that a small tension is applied to the fabric; (6) subjecting the fabric to a desired test cycle: the speed is set before commencing the test cycle; (7) measurement of test parameters such as forces or strains; (8) plotting the measured data and subsequent analysis of the desired mechanical properties; (9) data management. Operators have to perform all the above activities while conducting tests on the first-generation equipment. In the FAST system, measurement and display of test parameters (7) are achieved by using sensors. These instruments may be classified as second-generation test equipment. In some later versions of FAST, data acquisition facilities are available (8) and (9). The KES system may be classified as the third-generation test equipment. Fabrics are tested automatically by applying continuous stress recovery cycles (6) rather than testing at some discrete points. Data measurement, plotting, analysis and management (7), (8) and (9) are performed automatically, although some involvement of an operator is necessary for setting an x-y plotter. A skilled operator is necessary to minimize positioning and clamping errors. Automated testing of fabrics Existing test equipment consists of either simple manually operated instruments or sophisticated electro-mechanical systems, with a certain degree of automation. The latter instruments were developed based on ideas of hard automation, prevailing in the 1970s. In 1990, a research project was initiated by the present authors to develop a totally automated fabric test facility by exploiting the recent developments in computer control of mechanical systems. A robotic test system has been successfully developed by realizing the following objectives:
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Low-stress mechanical properties are sensitive to alignment and clamping errors, which result from the manual operation of the existing test instruments. In the robotic system, automation has been achieved in all aspects of fabric testing, except sample preparation. ● At present, fabric tests are conducted on several discrete instruments. In the robotic system, all the tests are combined to create a unified test cell. ● Develop a flexible test system, with an emphasis on software control rather than physical adjustments to change the test parameters such as strain rate, sample size, etc. The operator needs to place a fabric sample on the test surface and press a button. A complete sequence of fabric positioning, alignment, clamping and testing is performed automatically. Test results are stored directly on a computer, analysed for the fabric properties and presented graphically. The ability of fabrics to bend simultaneously in two planes (complex buckling) is one of the main problems associated with automated handling. The robotic test system is designed so that a fabric sample needs to be manipulated in a horizontal plane. Uniform pressure is applied on the fabric sample by a manipulating device (see Figure 1), attached magnetically to the robot arm, to avoid possible shear distortion or shear buckling. With the aid of two infrared sensors embedded in the test surface, the fabric sample is aligned parallel to an edge. A lateral compression test is conducted by compressing the fabric at a predetermined rate, using a circular head attached to the robot arm (Figure 2). The test may be repeated at several places. Fabric thickness versus compression force data are stored on computer. The data are smoothed and a complete stress recovery curve is plotted (Figure 3). Fabric properties such as linearity, resilience and compression energy are computed directly from the data. In a KES bending tester, the fabric sample is mounted in a vertical plane. This aspect demands great skill and dexterity from an operator. It is very difficult to ●
Connected to robot arm Manipulating device Electromagnets Cantilever length Alignment sensors Bending angle
Figure 1. Non-linear cantilever bending test
Retroreflective sensor for detecting fabric edge
Load cell connected to the robot arm (compression force)
Towards automated testing of fabrics
Compression strain
17 Compression head
Figure 2. Compression test
Fabric surface
60
Pressure (cN/cm. cm)
50
40
30
20
10
0 0.1
0.2 0.3 0.4 0.5 0.6 Thickness compression (mm)
0.7
achieve this using a robot, which has a limited dexterity. A cantilever bending test is relatively easier to adopt to a robotic system but this type of test gives only limited information. A new bending test has been developed in which the fabric cantilever is tracked continuously, using an optical sensor (Figure 1). This gives a continuous measure of cantilever length versus bending angle. A non-linear moment-curvature relationship can be computed, using an analysis technique developed in the present work (Figure 4). The bending test can be repeated on all four edges of a sample, thus obtaining bending properties along warp and weft directions. The test system is equipped with two electro-pneumatic clamps, one fixed on the test bed and the other mounted at the end of the robot arm. A fabric sample
Figure 3. Compression recovery curve
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0.1
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0.08
0.06
0.04
0.02
Figure 4. Moment-curvature relationship
0 0.2
0.4 0.6 0.8 Curvature (1/cm)
1
1.2
is clamped between the clamp fixed and the clamp attached to the robot arm, for conducting shear and tensile tests. A shear test is conducted by deforming the fabric sample like a trellis while maintaining a constant tension (Figure 5). Resulting shear force and the shear strain values are stored on computer. The data are smoothed and displayed graphically (Figure 6). Fabric shear properties such as shear rigidity and shear hysteresis values are computed from the measured data. A fabric tensile test is conducted by clamping the fabric sample between the clamps and applying a tensile strain at Fabric tension
Robot trajectory Shear strain Clamp attached to robot
Fabric sample
Figure 5. Shear test
Fixed clamp
20
Shear force (cN/cm)
Towards automated testing of fabrics
10
19
0
–10
–20 –8
–6
–4
–2 0 2 Shear (deg)
4
6
8
Figure 6. Shear strain curve
a predetermined rate (Figure 7). Tensile force and strain data are plotted graphically (Figure 8) and analysed for fabric properties such as linearity, extensibility, tensile energy and tensile resilience. Surface roughness is measured using a technique based on a laser triangulation sensor[21]. The laser sensor consists of a laser diode, a photosensitive detector and focusing lenses mounted in a fixed casing. The laser sends a fine beam of light to the fabric surface. Based on the position of the reflected beam on the photosensitive device, the distance between the sensor and the fabric surface can be computed with a resolution of 10µm (Figure 9). By moving the laser sensor relative to the fabric surface, a series of data points, representing the variations in the fabric surface, can be recorded. These data are utilized in computing the geometric roughness value. A similar technique has also been utilized in the objective measurement of pills[22]. A Fabric tension
Tensile strain
Clamp attached to robot
Fabric sample
Fixed clamp
Figure 7. Tensile test
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Tensile force (cN/cm)
500
400
20
300
200
100
Figure 8. Tensile strain curve
0 0
2
4 Strain (percentage)
6
8
Laser diode
Detector
Figure 9. Laser sensor for roughness
Fabric surface
technique for measuring the coefficient of friction is under development. This technique would be incorporated into the robotic test system. A preliminary investigation was carried out by testing a few fabric samples using the robotic test system. The measured fabric properties have been compared with the data obtained using the KES system. Typical linear regression charts are presented in Figure 10.
Robotic system
Towards automated testing of fabrics
Robotic system
0.5
0.14 0.12
Correlation coefficient = 0.978
0.4
Correlation coefficient = 0.96
0.1 0.3
0.08
21
0.06
0.2
0.04 Compression energy: WC (cN. cm/cm. cm)
0.1
0 0
0.1
0.2
0.3
0.4
Bending stiffness: (B) (cN. cm. cm/cm)
0.02
0.5
0 0
Kawabata Evaluation System
0.02 0.04 0.06 0.08
0.1
0.12 0.14
Kawabata Evaluation System
Robotic system
Robotic system
2
25
20
Correlation coefficient = 0.986
Correlation coefficient = 0.99
1.5 15 1 10 0.5
0
Shear stiffness: G (cN/cm. degree)
0
0.5
1
1.5
Kawabata Evaluation System
Tensile energy: WT (cN. cm/cm. cm)
5
2
0 0
5
10
15
20
25
Kawabata Evaluation System
Future possibilities in fabric testing The future certainly lies with the ability to test fabrics reliably and cost effectively. This is only possible with the availability of totally automated test systems, to enable their use in an industrial environment. The tedious process of sample preparation should be eliminated in future developments. A test system capable of measuring fabric properties on a large fabric sample should be developed, to eliminate the need for sample preparation and the difficulties associated with free curling edges. With the development of new sensors and faster computers at cheaper prices, online measurement and quality control is becoming a reality in the textile industry. Online measurement of length, speed and shrinkage of a fabric, using “Correvit” which is widely used in automotive testing, has been reported[23]. Online fabric fault detection using digital image processing[24], using opto-
Figure 10. Bending test results
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electronic processing[25] and online monitoring for uniformity of non-woven fabrics[26] have been reported. Fabric mechanical properties vary along the length of a fabric roll. The properties measured on a few fabric samples do not adequately represent the entire length of the fabric roll. Therefore, it is desirable to measure properties continuously along the length of a fabric. Very little progress has been reported in this area. Panarusky et al.[27] presented the possibility of online measurement of bending stiffness. They utilized a vision system to capture the image of a fabric loop and computed the bending stiffness by processing this image. The ultimate objective of automation should be to achieve online continuous measurement of all the fabric properties, during finishing or the final inspection process. The fabric property data can be supplied with each roll of a fabric, which may be utilized during the making-up process. Conclusion Based on the extensive research, it has been well established that the garment quality and its making-up process can be controlled based on fabric mechanical and surface properties. Widespread use of this technology in industry would depend on the availability of totally automated test equipment, to eliminate human errors and improve productivity, in terms of the number of samples tested in a given duration. The development of a robotic system, capable of conducting all the fabric tests on a single sample without operator intervention, has been presented. It is envisaged that online measurement of fabric mechanical, surface and dimensional properties would become a reality in the near future. References 1. Peirce, F.T., “The handle of cloth as a measurable quantity”, Journal of the Textile Institute, Vol. 21, 1930. 2. Howarth, W.S., “The handle of suiting, lingerie and dress fabrics”, Journal of the Textile Institute, Vol. 55, 1964, p. 3. 3. Dawes, V.H. and Owen, J.D., “The assessment of fabric handle. Part 1: stiffness and liveliness”, Journal of the Textile Institute, Vol. 62, 1971, p. 5. 4. Dawes, V.H. and Owen, J.D., “The assessment of fabric handle. Part 2: smoothness”, Journal of the Textile Institute, Vol. 62, 1971, p. 5. 5. Kawabata, S., The Standardization and Analysis of Hand Evaluation, Hand Evaluation and Standardisation Committee, Textile Machine Society of Japan, Osaka, 1980. 6. Lindberg, J., Waestraberg, L. and Svenson, R., “Wool fabrics as garment construction materials”, Journal of the Textile Institute, Vol. 51, 1960. 7. Mahar, T.J., Dhingra, R.C. and Postle, R., “Fabric mechanical and physical properties relevant to clothing manufacture”, International Journal of Clothing Science and Technology, Vol. 1, 1989, p. 1. 8. Brendt, H., Fortess, F., Wiener, M. and Furniss, J.C., “The use of KES and FAST instruments in predicting processability of fabrics in sewing”, 1st International Clothing Conference, Bradford, 1991. 9. Shishoo, R.L., “Relation between fabric mechanical properties and garment design and tailorability”, 1st International Clothing Conference, Bradford, 1991.
10. Stylios, G. and Lloyd, D.W., “The mechanism of seam pucker in structurally jammed woven fabrics”, International Journal of Clothing Science and Technology, Vol. 1, 1989, p. 1. 11. Kawabata, S., Niwa, M., Ito, K. and Nitta, M., in Stylios, G. (Ed.), Textile Objective Measurement and Automation in Garment Manufacture, Ellis Harwood, New York, NY, 1991. 12. Gunner, M.B. and Taylor, P.M., in Stylios, G. (Ed.), Textile Objective Measurement and Automation in Garment Manufacture, Ellis Harwood, New York, NY, 1991. 13. Shirley Developments Catalogue No. 6, Shirley Developments Ltd, Manchester, 1991. 14. Grosberg, P. and Swani, N.M., “The mechanical properties of woven fabrics”, Textile Res. Journal, Vol. 36, 1966. 15. Livesey, R.G. and Owen, J.D., “Cloth stiffness and hysteresis in bending”, Journal of the Textile Institute, Vol. 55, 1964, p. 10. 16. Vinto Shear Angle Tester (SDL/93), Shirley Developments Ltd, Manchester, 1993. 17. Manuals for Kawabata Evaluation System, Kato Tekko, Kyoto, Japan, 1986. 18. Fabric Assurance by Simple Testing, CSIRO Division of Wool Technology, Australia, 1988. 19. “Automated TNO shear tester”, International Journal of Clothing Science and Technology, Vol. 4, 1992. 20. Kawabata, S., “Automatic model of KESFB-1”, International Journal of Clothing Science and Technology, Vol. 3, 1991. 21. Ramgulam, R.B., Amirbayat, J. and Porat, I., “Measurement of fabric roughness by a noncontact method”, Journal of the Textile Institute, Vol. 84, 1993, p. 1. 22. Ramgulam, R.B., Amirbayat, J. and Porat, I., “The objective assessment of fabric pilling”, Journal of the Textile Institute, Vol. 84, 1993, p. 2. 23. Longerich, B., Arzit, R. and Kohler, J., “Non-contact length, speed and shrinkage measurement of fabrics during processing”, Melliand Textilberichte, Vol. 72, 1991. 24. Hinze, D., Mehlhorn, H. and Burkhardt, S., “Digital image processing by means of a parallel computer system for the testing of fabrics, Melliand Textilberichte, Vol. 72, 1991. 25. Ribolzi, S., Merckle, J., Gresses, J. and Exbrayat, P.E., “Real time fault detection on textiles using opto-electronic processing”, Textile Res. Journal, Vol. 63, 1993, p. 2. 26. Aggarwal, R.K., Kennon, R.W. and Porat, I., “Scanned-laser technique for monitoring fibrous webs and non-woven fabrics”, Journal of the Textile Institute, Vol. 83, 1992, p. 3. 27. Panarusky, M., Clapp, T.G. and Buchanan, D.R., “Automation and the control of product performance”, Performance and Failure Annual Symposium, Manchester, 1992.
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A study of factors affecting fabric cover-shelter properties Peter Totterdill The Work and Technology Programme, Nottingham Trent University, Nottingham, UK
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 24-34. © MCB University Press, 0955-6222
Introduction During the 1980s it was common currency in some circles to dismiss textiles and clothing production as a “sunset industry”, whose migration to low-cost countries was seen as a virtual certainty. This top-down dismissal of the industry is, however, belied by the experiences of several European textile and clothing regions during the 1980s. If many companies, particularly in more developed countries, found that they could no longer compete in price-sensitive markets against the import of mass-produced garments, then other opportunities were beginning to emerge. Markets for higher-value fashion grew rapidly during the decade, in part as a result of changing patterns of consumer taste and in part owing to changing patterns of competition between retailers. Price factors were no longer the principal determinant of competitiveness either for manufacturers or retailers: style, quality and the ability to respond rapidly to changes in fashion were increasingly seen as central to success. Even in countries such as the UK, where retailing is dominated by large multiples, the growth in the significance of higher-value outlets was significant. Mid-market retailers also started to demand more from their suppliers in terms of design, reliability and quick response – often leading to an increase in European sourcing at the expense of cheaper but more distant manufacturers. Electronic point of sale (EPoS) systems, for example, allow consumer sales to be analysed and stock to be reordered on a continuing basis. This enables retail buyers to order a much greater percentage of stock during the course of a season rather than months in advance: cautious preorders before the season are followed by a series of repeat orders in small batches. One retail buyer commented in a recent survey that: “two seasons of 26 weeks have been replaced by 26 seasons of two weeks”. For manufacturers the consequence is that batch sizes are falling dramatically, placing considerable strain on the ability of traditional production lines to cope. While the current European recession has undoubtedly reversed these trends to some degree, the 1980s, nonetheless heralded a long-term shift in the pattern of competitive advantage for the apparel industry in developed countries. Domestic manufacturers can find themselves at an advantage in comparison
with their geographically distant competitors – providing that they can meet the demand for quick response and continuous design innovation. Successful adaptation to meet the new market conditions clearly requires changes throughout the company. In particular it requires: ●
intensive market intelligence linked to continuous design innovation;
●
effective problem-solving and innovation skills throughout the enterprise;
●
a highly versatile system of production;
●
substantial investment in vocational education and training at all levels.
These requirements effectively reverse those traditionally associated with mass production, because they are oriented towards an environment characterized by uncertainty. In such circumstances, it is human resources, not technology and control, which provide the competitive edge. In this sense, Tayloristic production methods, grounded in the predictability of the mass market, can be seen as a major obstacle to successful restructuring. The limitations of the traditional production line As in other industrial sectors, the efficient production of apparel has been seen as synonymous with Taylorism by managers for several decades. Frederick W. Taylor’s work as an industrial engineer at the beginning of the century in the USA led, among other outcomes, to the adoption of assembly-line working in automobile production by Henry Ford. Taylorism has since been constantly modified and developed, and remains a major influence both on work organization and machine design in the apparel industry. Indeed the threat from low-cost imports in recent years has led some industry experts to reassert Taylorist principles as a means of re-establishing competitiveness through cost reduction and greater efficiency. Its influence on the sector can be characterized under the following four headings. Deskilling and specialization of labour Taylorism claims to achieve its highest outputs when tasks are split into the shortest practical cycles, resulting in the extreme routinization of work. As Ford’s experience with the inauguration of production lines before the First World War readily demonstrated, effective output could be established in a short space of time using workers who lacked any industrial experience or even the same language. In much of the contemporary apparel industry it is rare for operators to possess more than one or two skills. However, the capacity of the firm to resolve day-to-day production problems is considerably weakened by the deskilling of operators in this way. Bottlenecks can only be addressed if there is sufficient machinist versatility to permit movement from one operation to another, and lack of room for manoeuvre in the
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flexible deployment of skills frequently disrupts the smooth running of the line. The prevalence of piecerate systems of payment in some countries actively discourages multiskilling. Evidence from interviews with machinists in the UK suggests that it is common to conceal competence in all but the one operation at which an individual can earn most money. Some companies attempt to enhance flexibility through the deployment of “floaters” – multiskilled operators who can be moved from one operation to another to help rebalance the line. But such options can be costly and are not always effective; moreover suitably qualified personnel are not always easy to recruit in Taylorized labour markets. Industrial relations tension is often significant during changeovers between styles on traditional production lines, especially where piecerate systems of payment exist. Usually supervisors have a nominal responsibility for advising machinists on the optimum methods for assembly of a new style, but a frequent complaint is that the supervisors are unwilling or unable to do so. Supervisors themselves may not have been multiskilled machinists, and may lack an adequate knowledge of garment construction to play this role effectively. In practice, less experienced machinists may learn their method of working on a new style by looking over the shoulder of their more experienced neighbours – thus losing piecerate earnings and thereby subsidizing their own instruction. Learning curves on new styles are typically very slow, and high fault rates persist for some time. One machinist commented that “supervisors are only concerned with completing orders, not with helping individuals to improve their performance”. Style changes involve quite complex problem-solving skills, and Taylorism denies the space and resources needed to support this activity on the shopfloor. This means that machinists on piecerate can find that their earnings drop dramatically during changeover periods, and management is usually forced to compensate by means of wage supplements. A study of a UK factory found that typically during the first week of a new style, a machinist’s earnings would be enhanced by 60 per cent, falling to 20 per cent by week three. Even though this rarely compensates the machinist in full by bringing his/her wages back to their previous level, the costs to management of a combination of prolonged learning curves and frequent style changes can be disastrous[1]. Taylorism necessarily leads to the segmentation of local labour markets as companies gear recruitment to immediate needs – filling specific gaps on the line. Segmentation can extend not just to the specialized operation, but to the garment, the fabric and even to the brand of sewing machine. Clearly this can only have an adverse effect on workers’ employment opportunities in their local labour market, as well as on the quality of the work experience itself. Taylorism in machine design Taylorist logic is firmly embedded in the design characteristics of much of the sewing machinery currently in use in the apparel industry[2]. Work organization
based on the repetition of short cycles allows for increased sewing speed (the number of stitches per minute possible on the average sewing machine has doubled in the last 50 years) and the specialization of machinery. Singlepurpose machines dedicated to the performance of small and very specific tasks can be highly efficient, especially on complex seams, and clearly reflect the Taylorist division of labour. But such machines also tend to be very inflexible, and can be susceptible to redundancy as a result of even quite minor style changes. Computer-controlled conveyor systems represent a further manifestation of Taylorist principles, reducing the considerable handling times associated with the transfer of garments between the many workstations. Such systems often provide a very visible reminder of the division of labour by surrounding the individual operative with walls of part-completed products, minimizing opportunities for communication. Separation of production from control Functions such as planning and line balancing become the preserve of a technical élite (industrial engineers, production managers), and will exclude operatives from active participation. These functions are crucial for the profitable operation of the line, and seek to ensure that the machinist works at his/her highest level of output throughout the day, with a minimum of “waiting time”. Increasingly the separation of planning and control is reinforced by computerized systems for production planning and control. By placing terminals at each workstation, managers can monitor output at each stage of production, thereby enabling bottlenecks to be avoided through the rebalancing of lines. This technology also enables managers to police the performance of individual operatives, including the monitoring of lateness and the duration of rest breaks. However, Taylorism cannot replace the need for management to draw on the co-operation of human labour in a variety of unanticipated circumstances. Indeed there is evidence that the designing out of operative discretion often leads to greater reliance on informal strategies of serendipitous problem solving by operatives[1,3]. Taylorism and working life While Taylorism may claim some success in boosting the performance of the apparel industry during much of the twentieth century it is, crucially, dependant on the willing consent of the workforce, and of society as a whole, to accept the conditions of employment implicit in Taylorized production. Drawing on the available literature as well as on group discussions held with machinists in a number of factories, these conditions can be summarized in the following terms. Conventional production lines alienate machinists from the manufacturing process as a whole. The fragmentation of the labour process means that individual machinists gain little knowledge about the broader process of
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garment construction, and may never see the finished products on which they are working. Separation between planning and production means that there is little sense of control over the working environment. For example, in one factory, machinists do not learn about planned changes of style until the new cut parts start to arrive at their workstations on the overhead conveyor. Production flows also assume considerable importance, in part because of their impact on piecerate earnings and in part because interruptions in the flow underline a machinist’s sense of powerlessness and inability to control the immediate circumstances which govern his/her work. Some interruptions, such as a supervisor’s failure to ensure an adequate supply of buttons or thread to the machinist, may be merely frustrating. More serious disruption is caused by a failure to ensure an adequate balance of work between each station on the line – a problem greatly exacerbated by falling batch sizes since the work content (i.e. the configuration of different types of sewing machine and the amount of work required from each) may vary considerably from style to style. Machinists are also distanced from real involvement with the quality of the finished product – especially when working on piecerate. Short, pressurized and repetitive cycles give machinists little opportunity or incentive to correct flaws: …Everybody (is) having to rush and work to make money and you (don’t) have time to rectify one particular garment at that time… (Operator A). And I don’t think you check other people’s quality when you are on piecework, you just do your own, you do not check whether the buttonholers are doing their job or the button sewer or whether their tacking’s right or anything, you don’t bother with anybody else’s job[3] (Operator B).
Alienation from the overall production process is exemplified by the way in which quality issues tend to be defined in terms of culpability rather than responsibility or achievement: who is to blame, and who will be caught out? Operators are isolated from one another. Sometimes they are physically isolated by plant such as overhead conveyor systems, and typically by the fragmentation of the labour process, which inhibits communication and denies opportunities for collective learning and the sharing of experience through dayto-day problem-solving activity. Conflict is deeply entrenched in the work experience. In the words of one production director, companies wage “a war of attrition” against operators in order to achieve targets and to pin down earnings. Piecerate creates a high degree of suspicion on both sides – management suspects workers of “abusing” the system, while workers fear that they will be cheated of fair earnings by excessively high targets. The increasing frequency of style changes means that such conflicts are rehearsed with even greater regularity. Managers become preoccupied with policing the system at the expense of strategic goals.
Production lines are stressful and fatiguing. Occupational stress is widely reported by machinists on production lines. In part this can be a product of the piecerate system: while the opportunity to earn “good money” on long production runs is certainly appreciated by machinists, wage levels are also unstable. “You’ve got to earn your money by lunchtime” because in the afternoons you are too tired to work at speed. Certainly the theme of mental and physical exhaustion is a recurrent one in discussions with machinists on production lines. According to the literature, stress is closely linked to an individual’s lack of control over the working environment: this can be related to the sense of powerlessness over such factors as work flow and the level of earnings, and the frequent disputes with managers over target setting. Production lines can be bad for your health. Highly routinized work based on short cycles is related to a range of repetitive strain injuries, and machinists report frequent instances of wrist and hand problems (such as Carpal Tunnel Syndrome), back and neck pains, headaches and eyestrain. Production lines mean dead-end jobs. The separation of planning and production strengthens barriers to career development for workers in the industry. Because discretion is minimized in day-to-day work, machinists have very little opportunity to acquire the problem-solving and self-efficacy skills that might facilitate progress into technical or managerial jobs. Even the role of the traditional supervisor offers little potential for the exercise of managerial discretion, and cannot be said to offer an effective stepping stone in the conventional workplace from the shopfloor into higher grades. This is embodied in the structure of vocational education and training in many countries. It is not uncommon for an individual’s experience of training to be confined to his/her initial entry into the labour market. Even machinists appointed to supervisory grades may receive no further training to fulfil this role, and in many regions training providers offer no courses to bridge the gap between supervision and management. Even where exemplary courses are available, these may only be delivered on a full-time basis, making them inaccessible to many. Open and distance learning is still relatively uncommon throughout the industry. Taylorism and the labour market Given the characteristics of working life on production lines, it is barely surprising that workers in developed countries are becoming increasingly reluctant to enter, or to remain in the industry even when high levels of unemployment are prevalent. It is not uncommon to hear managers claim that “girls (sic) like boring jobs”, especially when good piecerate earnings can be made on long, uninterrupted runs. For such managers it is axiomatic that machinists are interested only in their level of earnings, and are largely unconcerned with the quality of job content. This is reflected in a number of ways by the labour market. One somewhat puzzled manager told the author:
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“I’ve simplified and deskilled the job so much that any moron could do it. But still I can’t get anyone to work for me”. Labour recruitment difficulties are becoming increasingly severe, especially in urban areas where the industry does not have a monopoly of employment opportunities. Retention of machinists is also a problem for many firms – some UK factories experience an annual turnover in excess of 100 per cent. This is both costly (taking into account the time and resources required for advertising, selection and induction) and clearly disruptive in terms of production. Absenteeism can also be high on many production lines, exacerbating the problems of line balancing to a considerable degree. It is clear that Taylorism has always represented an assault both on the quality of work in the apparel industry and on the scope of employment opportunities. Economic considerations continue to force many workers to accept these conditions, but it is also increasingly clear that the industry can no longer continue to rely on the consent of labour to such methods of work organization. Nor should the industry believe that the traditional neglect of the potential skills of its workforce represents an effective means of achieving competitive advantage in current market conditions. The scope of teamworking The nature of teamworking There are as many different approaches to teamworking as there are companies which operate it. How, therefore, can the core principles of teamworking be defined? Whatever the precise form of a teamwork system, it should embody the following principles: ●
Operatives should be multiskilled, both in terms of the technical skills required to enable them to make a considerable part of the whole garment, and in terms of the communication, assertiveness and problemsolving skills required to enable them to work effectively with other team members. Adequate training and support should be provided by the company to ensure the effective acquisition of these skills throughout the workforce.
●
Team members, both individually and collectively, must exercise considerable discretion in the planning and implementation of tasks. Teams also play a key role in, and share responsibility for, decisions about the quality and quantity of output. Supervisors and managers fulfil a planning and co-ordinating function, not one of direct control. Successful teamworking is also dependant on size: teams typically have between three and ten members to ensure their effective operation. Individual piecerate systems of payment are inappropriate because the
contribution of each operator to the team cannot be measured; rather, flat-rate or group bonus systems are devised, reflecting the non-manual roles of teams in, for example, self-organization and the maintenance of quality standards. Each company thus faces a number of strategic choices in the design and implementation of teamwork systems[4], for example: ●
Operators walking/standing or sitting?
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A high or low ratio of machines to operators?
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Single garments or bundles?
●
Group payment system or flat rate?
A search for “correct” solutions to these questions would be misguided. First, there is insufficient comparative data to derive firm guidelines about which type of system is the most efficient in different types of company. Second, many practitioners would argue that the process of implementation can be more important than technical differences in the design of systems. Companies must develop systems through careful negotiation and bargaining with all employees likely to be affected by the change, allowing the optimum solution to emerge by the creation of consent rather then by the imposition of a predetermined blueprint. This process prefigures the type of cultural change that a company must undergo. Teamworking requires the real empowerment of operators, who must participate fully and possess a sense of ownership in relation to the organization of production. There are inevitably implications for other roles within the company, and recognition of this need for wider change is central to the successful implementation of teamworking: ●
Supervisors must change from a policing role to that of facilitating and advising team decisions.
●
Industrial engineers must change from their traditional preoccupation with cutting wage rates for particular jobs to providing an accessible source of expertise on workstation layout and the improvement of methods.
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Trainers must help to turn the workplace into an environment for continuous, self-guided learning (in relation to both production and nontechnical skills) rather than simply provide instruction in machining methods.
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Factory managers must become less remote and less a symbol of authority; rather, they must play a more effective role in co-ordination and development.
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Teamworking can never be conceived as a purely technical change: it is one which requires a complex combination of human and non-human aspects to achieve effective operation[3,4]. Unwillingness to recognize the need for such comprehensive change is, perhaps, the most frequent cause of failure in teamworking. A number of examples of total or partial failure have been found in the UK clothing industry, often where teamworking has been introduced in parts of large companies as a result of corporate decisions imposed from the top. One subsidiary of a large UK manufacturer achieved notable results with teamworking, leading the parent company to encourage other subsidiaries to follow. But managers in these other plants frequently failed to grasp the extent of the changes normally associated with teamworking. Operators were offered neither multiskill training or opportunities for teambuilding, while supervisors and managers often felt threatened by the removal of their policing functions. In such cases, the enlargement of operators’ jobs without an increase in the level of collective autonomy has almost always led to failure. The avoidance of failure is thus clearly contingent on the willingness of companies to invest rather heavily in appropriate training at all levels, and even to reorganize the structure of management itself. How companies gain from teamworking As the previous section has argued, not all companies benefit equally from the introduction of teamworking: some fail to maximize potential gains because implementation is incomplete and not linked to a sufficiently clear model of cultural change. It should also be made clear that, for many companies, notably those with long experience in higher value markets and in which Taylorism never really took root, teamworking merely consolidates existing patterns of versatility and polyvalency. Nonetheless, to generalize from the experience of a number of companies in Europe and the USA, manufacturers can typically expect improvements in the following areas[4]: ●
enhanced ability to respond to market demand;
●
improved quality;
●
reduced work-in-progress;
●
fewer recruitment, retention and absenteeism problems;
●
simplification of planning;
●
teamworking as the motor for change and improvement throughout the company.
Disseminating teamworking Despite these increasingly well-documented advantages, however, the spread of teamworking in the apparel industry remains surprisingly limited. In the UK, it
is estimated that up to 70 apparel companies were involved in teamworking during the summer of 1993. Examples are fairly common in Finland, Norway, Sweden, the southern states of the USA and, to a lesser extent, in Denmark. Elsewhere, instances of teamworking remain either sporadic or non-existent. Several reasons can be cited for this, many of them related to the opposition between teamworking and the deeply ingrained culture and principles of Taylorism: ●
Lack of awareness of innovation in general is endemic among apparel industry managers in many areas, and the diffusion of new technologies is notoriously slow. Wide variations in the use of the term “teamworking” also leads to misconceptions and misunderstandings about its potential relevance.
●
Access to relevant sources of expertise relating to teamworking remains a serious problem for companies, especially SMEs. As this article demonstrates, teamworking cannot be implemented “off the shelf”, but requires a careful process of learning and negotiation. Experts with understanding and experience of these issues are rare, and companies may be reluctant to embark on a process of major change without appropriate guidance.
●
The past deskilling of workforces presents a significant obstacle to change. Operators, supervisors and managers require a much broader range of skills – both technical and social. Considerable innovation is needed in the design of training curricula and in the delivery of vocational courses, requiring new forms of partnership between employers and providers.
It is the contention of this article that targeted intervention by public bodies and trade unions is required to overcome these obstacles[3]. Public policy has a legitimate interest both in the competitiveness of traditional industries such as apparel manufacture and in the enhancement of womens’ and other workers’ employment. In seeking a convergence between these objectives through the dissemination of new, human-centred forms of work organization, public policy has three critical roles to play: (1) To demonstrate alternatives to Taylorism through programmes of research and development, both in order to create awareness that different and perhaps more effective solutions exist to current problems, and as a means of building a core of expertise for future dissemination. (2) To disseminate alternative technologies, patterns of work organization and training modules to firms. Dissemination strategies do not invariably grow from the types of research and development projects described above; in some instances over-optimistic assumptions are
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made about the ability of pilot initiatives alone to promote the diffusion of new practices to industry. The Nottinghamshire Work and Technology Programme is an example of a systematic approach to dissemination. (3) To influence the wider infrastructure on which companies depend. Companies operating team-based systems will have quite different requirements in relation to, for example, the structure of vocational education and training. Traditional curricula will need to be rewritten to meet new skills needs, and new forms of delivery will need to be devised to ensure the widest possible access to training on a continuous basis. References 1. Farrands, C. and Totterdill, P., Markets, Production and Machinists in Nottinghamshire’s Clothing and Knitwear Industries, Nottingham Trent University, Nottingham, 1990. 2. Banke, P. and Binder, T., “Design of human centred technology in the clothing industry: TA-approach to sewing machine technology”, paper presented at ECTA III Conference, Technology and Democracy: The Use and Impact of Technology Assessment in Future Europe, Danish Technological Institute, 1992. 3. Middleton, D. and Totterdill, P., “Competitiveness, working life and public intervention”, in Kasvio, A. (Ed.), Industry without Blue-collar Workers: Perspectives of the European Clothing Industry in the 1990s, Work Research Centre Paper 36/92, University of Tampere, Finland, 1992. 4. Tyler, D. (Ed.), “The introduction and support of teamworking in garment companies – a manual for managers”, Nottinghamshire Work and Technology Programme, Nottingham Trent University, Nottingham, 1994.
Importance of mechanical and physical properties of fabrics in the clothing manufacturing process
Importance of fabric properties in manufacturing 35
Roshan L. Shishoo Swedish Institute for Fibre and Polymer Research (TEFO/IFP), Mölndal, Sweden Introduction The main task of the clothing manufacturer is to produce shell structures out of flat fabrics to match the shape of the human body. In all shape-producing methods, there will be an interaction between particular methods used and various mechanical and physical properties of the fabric. The objective measurements of mechanical properties of fabrics can be traced back to the work by Peirce[1] on fabric bending and compression. The pioneering research work was, however, carried out at TEFO in the late 1950s and 1960s involving evaluation of low-stress mechanical properties of apparel fabrics[2-4]. This research related the basic fabric mechanical properties such as bending, buckling, tensile, shear and compression, to the tailorability and formability of fabrics into garments. Lindberg et al.[4] were the first to apply the theory of buckling to textile fabrics in garment technology. Longitudinal fabric compression is a fabric mechanical property that is particularly important in tailoring, i.e. the forming and sewing of flat pieces of fabrics into three-dimensional shaped garments. This property was related to fabric formability by Lindberg et al. Fabric formability is a measure of the degree of longitudinal compression sustainable by a fabric in a certain direction before the fabric buckles. For the case of plate buckling this compression limit is dependent on the product of fabric compressibility and fabric bending rigidity. The mechanical properties of apparel fabrics are important from the point of view of stresses applied to fabrics in the making up, as well as physical changes in the fabrics as a result of application of forces in a garment during its use. Kawabata Evaluation System (KES) In 1972, the Hand Evaluation and Standardisation Committee (HESC) was, through the efforts of Professor Kawabata and his associates, and the support of the Textile Machinery Society of Japan, created in Japan for the purpose of The author would like to sincerely thank M. Choroszy, A. Tesfay and E. Tobisson for their important experimental contribution.
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standardization of the hand evaluation. Based on the standardization of the primary hand values and the development of the numerical expression of item, the research of the interaction between these hand values and the mechanical properties of fabrics was started by Kawabata and Niwa in mid-1970s. The following two research and development areas were covered by Kawabata and his co-workers: (1) determination of the hand values which characterize the linear and non-linear mechanical properties of fabrics; (2) development of a quick and accurate measuring system for the fabric mechanical properties. This work led to the development of Kawabata Evaluation System for Fabrics (KES-F) instruments for the objective measurement of fabric mechanical and surface properties[5]. Applications of fabric mechanical property measurements have extensively been reported in the proceedings of Australia-Japan symposiums and a symposium held at Bradford University[6-9]. These applications include the general headings of: ● engineering fabrics using objective mechanical property data; ● objective assessment of fabric handle; ● objective evaluation of fabric tailorability; ● performance characteristics of fabrics and garments. KES-F instruments have been sold in very large numbers in Japan. In Europe and the USA these instruments are being used mainly at research institutes and R&D laboratories of major fibre producers. TEFO’s contribution in this field TEFO’s recent work covering the use of the KES-F systems has been on the interface between material characteristics and garment making-up processes[10,11]. Investigations have been made on the following two projects: (1) Material characterization in relation to garment making-up. The objective is to characterize those material properties which are of importance as regards formability, tailorability, quality and process control. This work includes analysis of physical and mechanical properties of fabrics in relation to automation in apparel production processes. (2) Critical body measurements and computer-aided two- and threedimensional pattern design (CAD). The objective is to use critical body measurements data for two- and three-dimensional garment design and pattern construction. The role of fabric properties such as extendibility, stiffness and draping in CAD-simulation of garment designs and shapes is being investigated.
Fabric mechanical properties and tailorability Importance of A test-production line comprising machinery and process control system for the fabric properties evaluation of garment production system was installed at TEFO some years in manufacturing ago. The installation contains among other things computerized systems for production data collection and standard time determination (computerized 37 methods analysis – CMA). This production line allows one to carry out fullscale evaluation not only of different production and processing units, but also of material-related processing efficiency, now used for quantifying tailorability. The time taken for combined sewing and handling operations in the production of test garments is registered based on MTM time and motion studies. For a given garment type and a given set of basic patterns, we believe that the tailorability of processed fabric can be quantified by standard time determinations using CMA. According to our hypothesis, CMA-time obtained could be a good and practical measure of the ease of forming and tailoring flat fabrics into three-dimensional garments. Results of some of our investigations on tailorability of apparel fabrics using TEFO CMA are described below. The test garment type selected was a jacket containing pockets and flaps. A large number of commercial test fabrics made of cotton, wool, polyester and blend fibres were obtained from the production line of some garment manufacturers. The basic mechanical properties used in this study are the following KES-F parameters: ●
tensile properties: EMT1, EMT2;
●
bending properties: B, 2HB;
●
shearing properties: G, 2HG, 2HG5;
●
thickness.
The measurements were carried out on fabrics conditioned in a standard atmosphere of 65 per cent rh and 21˚C. Figure 1 shows CMA-time distribution for 24 different test fabrics. In our investigations the CMA-times obtained were in the range of 283Cmin to 312Cmin. This difference can be explained in terms of the difference in basic mechanical and physical properties of these test materials. Extensive empirical analysis has been made at TEFO of the correlation between the CMA-times obtained for various test fabrics and the calculated tailorability functions based on various mechanical properties of these fabrics. Very satisfactory correlation was found between CMA-time and TEFO’s new tailorability function log(B × EMT1/2HG5), where EMT is the level of extension at 50gf derived from the standard load-extension curve. The correlation coefficient obtained was 0.89 (Figure 2).
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305 300 295 290 285 280
Figure 1. CMA-time of tailorability of 24 different fabrics
A
C B
F E
3 8
1 6
H 1 9 2 M N P G 7 4 J K 5 O R Fabric quality
Note: CMA = computerized methods analysis
CMA time Cmin 315 310 305 300 295 290 285 280 –2
–1.5
–1
–0.5
LT x log (B x EMT/2HG5)
Figure 2. Correlation between CMA-time and tailorability function
n = 24 r = 0.89
Source: [10] Note: CMA = computerized methods analysis
0
0.5
Fabric mechanical properties and garment design Importance of During the last decade, CAD/CAM systems have been used at an ever fabric properties increasing rate in the garment manufacturing industry. Computerized in manufacturing sketching and fashion illustrations have been available as a tool during the last three to four years but this technique is limited mainly because of the noncompatibility with follow-up processes such as pattern design and pattern 39 engineering. The existing CAD systems are still very operator-controlled, and characteristics underlying pattern engineering processes are based on operator skill and experience. In future three-dimensional pattern design systems, the possibility should exist for CAD simulation of a finished garment, thereby eliminating the long process of making a large number of garment samples and evaluating them on mannequins before the start of the production process. A designer should not only be able to evaluate how garment style is influence by fabric characteristics, but also be able to manipulate basic patterns for fabrics with different mechanical properties in order to obtain a particular garment style and shape. Work has been started at TEFO with the aim of establishing quantitative relationships between fabric mechanical properties and garment design/shape. The experimental garment is a skirt based on a semi-circular pattern. Different fabric qualities have been selected and the test garments are produced in two sizes. A test garment is then put on a mannequin of corresponding size, and the resulting shape and draping behaviour is quantified. Studies are being made on the interaction between fabric mechanical properties and the garment shape with and without pattern manipulations. Figure 3 shows the difference in node formation obtained in two skirts made from two different fabrics, cotton and polyester, but of the same basic semicircular pattern. This aspect has to be taken in consideration when designing garments to be made from different fabrics. Figure 4 shows the relationship between drape coefficients of 20 fabric qualities and one of Kawabata and Niwa’s mechanical parameters for total appearance value prediction[12]. The correlation coefficient obtained is 0.92. Correlation coefficient 0.88 was also obtained between the drape coefficient and
180° skirt Front Polyester
Front
Cotton
Left
Right
0°
Right
L Left
R
0°
I 45°
r
45°
45° 45° 90°
A = 10.6dm2 Back
Back
Warp
A = 11.0dm2
90°
Weft
R r l L
= 70.00cm = 22.30cm = 70.05cm = 289.96cm
Figure 3. Node formation in two skirts made from two different fabrics but of the same basic pattern
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40
Draping coefficient
50 45 40 35 30 25 20 15 0.06
Figure 4. Relationship between draping coefficient and Kawabata and Niwa’s mechanical parameter
0.07
0.08
0.09
0.1
0.11
0.12
([M (K = 1) + 2HB/2]/w)1/3 Mechanical parameter x 31 in Kawabata and Niwa's total appearance value prediction[12]
Note: N = 23 r = 0.91
the function B.G/W; where G = shear stiffness, B = bending stiffness and W is the weight per square metre (Figure 5). Concluding remarks Two major developments in fabric objective measurement have taken place in the last decade. First, there now exists a general consensus of opinion that the objective measurement of fabric properties is necessary for quantifying changes in fabric quality, tailorability and garment performance. Second, coherent systems of highly sensitive instruments have been developed which measure the important mechanical and physical properties of apparel fabrics. Fabric objective measurements are now used for a variety of purposes, including buying control, product development and process optimization. Although instruments and theories for measuring important physical, mechanical and surface properties have been available for many years, the development of the KES-F system has led to considerable increases in the use of fabric objective measurement to evaluate fabric quality. Professor Kawabata has been very successful in renewing scientific interest in this field. More recently a new set of instruments Fabric Assurance by Simple Testing (FAST) has been developed by CSIRO in Australia which is designed to predict the tailoring performance of fabrics and the appearance of garments in wear by
Importance of fabric properties in manufacturing
60 55
Draping coefficient
50 45
41
40 35 30 25 20 15 1.00E-05
1.00E-04
1.00E-03
1.00E-02
B *G/W Note: N = 23 r = 0.88
measuring the tensile, shear, bending and compression properties as well as the dimensional stability[13]. These instruments also give information which can be related to fabric handle. Unlike the KES-F system, FAST only measures the resistance of fabric to deformation and not the recovery of fabric from deformation. However, the FAST system is much cheaper, simpler and more robust than the KES-F system, and, as such, perhaps more suited to an industrial environment. A number of other simple techniques for measurement of bending and shear stiffness and extendibility also exists at many industrial laboratories. These are regularly used for in-house control of quality and tailoring performance of fabrics. References 1. Peirce, F.T., “The handle of cloth as a measurable quantity”, Journal of the Textile Institute, Vol. 21, 1930, pp. 377-416. 2. Eeg-Olofsson, T., “Some mechanical properties of viscose rayon fabrics”, Journal of the Textile Institute, Vol. 50, 1959, p. T 112. 3. Lindberg, J., Behre, B. and Dahlberg, R., “Mechanical properties of textile fibers”, TRJ, Vol. 33, 1961, p. 99. 4. Lindberg, J., Westerberg, L. and Svensson, R., “Wool fabrics as garment construction materials”, Journal of the Textile Institute, Vol. 51, 1960, p. T 1475. 5. Kawabata, S., The Standardisation and Analysis of Hand Evaluation, 2nd ed., HESC, Textile Machinery Society of Japan, Osaka, 1980.
Figure 5. Relationship between draping coefficient and fabric property function: B.G/W (bending stiffness × shear stiffness/weight per unit area)
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6. Kawabata, S., Postle, R. and Niwa, M. (Eds), Objective Specification of Fabric Quality, Mechanical Properties and Performance, Textile Machinery Society of Japan, Osaka, 1982. 7. Postle, R., Kawabata, S. and Niwa, M. (Eds), Objective Evaluation of Apparel Fabrics, Textile Machinery Society of Japan, Osaka, 1983. 8. Kawabata, S., Postle, R. and Niwa, M. (Eds), Objective Measurement, Application to Product Design and Process Control, Textile Machinery Society of Japan, Osaka, 1985. 9. Stylios, G. (Ed.), “Textile objective measurement and automation in garment manufacture”, Special Issue, International Journal of Clothing Science and Technology, Vol. 2 No. 3/4, 1990. 10. Shishoo, R.L. and Choroszy, M., “Analyses of mechanical and dimensional properties of wool fabrics relevant to garment making”, 8th International Wool Textile Research Conference, Christchurch, New Zealand, February 1990. 11. Shishoo, R.L., “Relation between fabric mechanical properties and garment design and tailorability”, International Journal of Clothing Science and Technology, Vol. 2 No. 3/4, 1990, pp. 40-7. 12. Kawabata, S. and Niwa, M., “Fabric performance in clothing and clothing manufacture”, Journal of the Textile Institute, Vol. 80, 1989, p. 19. 13. Ly, N.G., Tester, D.H., Buckenham, P., Roczniok, A.F., Brothers, M., Scaysbrook, F. and de Jong, S., Simple Instruments for Quality Control in a Tailoring Company, Report No. 11, International Wool Textile Organization, Technical Committee Meeting, Paris, December 1988.
FAST – Fabric Assurance by Simple Testing
FAST – Fabric Assurance by Simple Testing
Pier Giorgio Minazio International Wool Society (Secretariato Internazionale della Lana), Biella, Italy
43
Introduction Objective measurement has played an important role in the wool-textile industry. Its uses are many and varied and occur all along the wool-textile pipeline, from the marketing of raw wool to various processing stages and, finally, retailers of wool products. While the objective measurement of raw wool has been widely accepted for specification and marketing of raw wool, it is only in recent years that the implementation of fabric objective measurement technology has drawn much international attention. Credit for this must be given to Professor Kawabata, who developed the Kawabata Evaluation System for Fabrics (commonly known as the Kawabata system or the KES-F system). Fabric objective measurement provides a scientific means to quantify the quality and performance characteristics of fabrics. It can be used as a basis for fabric specification, product and process development, process control, quality assurance and communication between various industry sectors. FAST-1 compression meter The processes of worsted finishing, in particular dry finishing, modify the surface of a fabric. These modifications are primarily fabric compression, which occurs during the pressing operations, and the stabilization of the compressed form (setting), which occurs during decatizing. The degree of fabric compression and amount of fabric stabilization affect the thickness of the fabric surface layer and consequently the appearance and handle of a fabric. The FAST-1 compression meter (Figure 1) has been developed to measure fabric thickness and, in addition, the variability and the durability of the thickness of the fabric surface layer. The FAST-1 compression meter is able to measure fabric thickness to micrometer resolution at two predetermined loads, and thereby measure the thickness layer. The surface layer is defined as the difference in fabric thickness at the two predetermined loads. For determining the durability of the surface layer, the measurements of fabric thickness are repeated after the fabric has been released. The fabric is released by steaming on an open Hoffman press for 30 seconds. The increase in fabric surface thickness obtained by this process is similar to the increase that
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 43-48. © MCB University Press, 0955-6222
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occurs during the rigours of garment manufacture. The following parameters are determined by the FAST-1 compression meter: ● fabric thickness at various loads; ● surface layer thickness; ● released surface layer thickness. FAST-2 bending meter The bending meter, shown in Figure 2, measures two bending properties of a fabric, namely the fabric bending length which is related to the ability of a material to drape, and the fabric bending rigidity which is related to the quality of stiffness when a fabric is handled. The bending rigidity is particularly crucial in the tailoring of lightweight fabrics as a very flexible fabric (low bending rigidity) may cause seam puckering while a high bending rigidity fabric can be more manageable in sewing and so produce a flat seam. The bending length (BL) is displayed automatically, thus the error due to the operator’s judgement is eliminated. FAST-3 extension meter During garment making-up, in particular fabric shaping and sewing, the fabric needs to be stretched to a certain degree to conform to the intended shape. This ability of a fabric to stretch at low load, or fabric extensibility, is of major concern to tailors. The FAST-3 extension meter (Figure 3) is capable of measuring the fabric extensibility in warp, weft and bias directions over a range of loads, with direct reading of extension as a percentage of the initial gauge length.
Surface thickness
Figure 1. Measuring principle of the FAST-1 compression meter
Fabric thickness
Bending length
Figure 2. Measuring principle of the FAST-2 bending meter
41.5°
FAST – Fabric Assurance by Simple Testing 45
Extension
Figure 3. Measuring principle of the FAST-3 extension meter
The ability of a two-dimensional piece of fabric to form a three-dimensional garment also depends on how easily the fabric can be sheared in its plane. This mode of deformation is characterized by the fabric shear rigidity, which can be estimated from the bias extensibility. The fabric formability can also be calculated by multiplying the fabric extensibility with the fabric bending rigidity. This parameter was first introduced by Lindberg et al. as a measure of the degree of compression sustainable by a fabric in a certain direction before the fabric buckles. It is related to the limit of overfeed possible in a seam before seam puckering occurs. Low levels of formability can lead to difficulties in forming such seams as the sleeve cap, or any other eased seam. The following parameters are determined by the FAST-3 extension meter: ● extensibility at various loads; ● bias extensibility; ● shear rigidity; ● formability. FAST-4 dimensional stability test Low dimensional stability of fabrics is one of the main causes of poor appearance in garments. The change in fabric dimensions can occur during the garment making or later during wear as the fabric is subjected to changing humidity conditions including steaming. This change is controlled by the amount of relaxation shrinkage and hygral expansion in a fabric. Relaxation shrinkage is caused by the recovery of fibres strained during manufacturing,
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while hygral expansion or contraction is caused by the swelling or deswelling of hygroscopic fibres. High levels of relaxation shrinkage can cause puckering of fused panels and discrepancies from intended garment size. On the other hand, insufficient relaxation shrinkage results in an unsatisfactory joint at the junction of the sleeve and shoulder region of the garment because the cloth at the head of the sleeve must shrink during steaming. Large hygral expansion can cause seam puckering, poor matching of patterns at seams, waviness and buckling during pleating and an overall lack of “balance” in the garment. In the FAST-4 dimensional stability test (Figure 4), the fabric specimen is dried to zero regain to measure its dry dimensions (L1), then soaked in water to measure its wet relaxed dimensions (L2). The specimen is then redried to measure its final dry dimensions (L3). The relaxation shrinkage and hygral expansion are then calculated. The FAST-4 dimensional stability test has the advantage of not requiring a conditioned laboratory. The testing time is reduced to less than one hour in contrast to the conventional one-day test. Its simplicity and precision are attractive for in-house product development and quality control in the textile trade. Summary of the properties A summary of the five properties measured by FAST is as follows: (1) Compression: ● fabric thickness; ● fabric surface thickness; ● released surface thickness; (2) Bending: ● bending length; ● bending rigidity;
Fabric dimension
RS = 100 L1 - L3 L1
Figure 4. Schematic diagram of the FAST-4 dimensional stability test procedure
HE = 100 L2 - L3 L3
Drying Soaking Drying
L1
L2
L3 Time
FAST – Fabric Assurance by Simple Testing
(3) Tensile: ● extensibility; ● formability; (4) Shear: shear rigidity; (5) Dimensional stability: ● relaxation shrinkage; ● hygral expansion. The FAST control chart is shown in Figure 5.
47
Units Fusing Relaxation shrinkage
RS–1
–1
Pleating
Sizing
0
1
2
3
4
Wool/polyester Hygral expansion
5
%
10
%
RS–2
HE–1
–1
–2
Pleating-puckering
0
1
2
3
4
0.4
0.5
5
6
7
8
9
HE–2
Pucker Formability
F–1
0.0
0.1
0.2
0.3
0.6
0.7
0.8
0.9
1.0
1.1
mm2
F–2 Overfeed moulding
Extensibility
E100–1 E100–2
1
Check matching laying-up 2
0
1
3
2
3
4
4
5
5 6
7
Cutting Bending rigidity
B–1
1
3
% 8
9 %
Stiff 5
7
9
11
13
15
40
50
60
70
80
17
19
21
µNm
B–2 Laying-up
Shear rigidity
G
Thickness
T
10
20
Moulding sleeve insertion 30
Lean 0.2
90
100
110
N/m
Full 0.4
0.6
0.8
1.0
1.2
1.4
mm
0.2
0.4
0.6
0.8
1.0
1.2
mm
0.2
0.4
0.6
0.8
1.0
Smooth Surface thickness
ST
Released surface thickness
STR
Weight
W
0.0
mm 0.0 Light 150
1.2 Heavy
200
250
300
350
g/m2
Figure 5. FAST control chart
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Pressing performance In addition to the aforementioned properties measured by FAST, a new property was discovered a few years ago and now represents an upgrading of FAST technology. This property has been named pressing performance (PP). The garment appearance is largely imparted by the final pressing operation and it is therefore important to be able to predict the seam pressing performance of a fabric prior to cutting, so that remedial measures can be taken when required. It has been confirmed that the ability of a seam to be pressed flat is not directly related to other fabric properties, but can be modified during finishing. Fabric PP can be predicted by forming a crease in a sample of fabric and measuring the recovery rate of the crease angle under a standard atmosphere. Because a conventional garment press is not suitable for use in a standard test method owing to a large amount of variability in steam conditions between presses, an alternative method of setting the crease has been developed. The principle of this test has already been approved by the International Wool Textile Organisation (IWTO) as the IWTO Draft TM A2 “Crease pressing performance test”. Work carried out with CSIRO, a Biella weaver and extensive trials done with a maker-up in Italy has confirmed the results of these studies and has also shown that a low crease angle is a necessary condition for a pressed seam with a good appearance. Prediction of final appearance Pressing performance crease angle as measured by the test method, and formability appear to be the two most important factors which influence the final appearance of the garment. The new pressing performance test was found to be able to predict the propensity of a fabric to produce blown seams after pressing. Seam blowing is when the pressed seam in a garment does not remain flat but has a rounded or “blown” appearance.
A neuro-fuzzy control system for intelligent overlock sewing machines George Stylios and J.O. Sotomi
Neuro-fuzzy control for sewing machines 49
Department of Industrial Technology, University of Bradford, Bradford, UK Introduction Limp materials such as fabrics have to be stitched in order to form the shape of a three-dimensional body, as in a garment. Sewing by needle and thread has survived for centuries and this is the only means of joining acceptable to consumers. Fabrics are very complex materials for defining, they have different properties that interact with one another and change with processing. Sewing machines have inherent problems in the engineering sense also, through their complex mechanisms necessary for the many different stitch types. Optimization of the conditions of sewing machines has been and still is one of the most important requirements for the textile, garment and retailing industries, and for the sewing machinery manufacturing industry. The complex interactions at the fabric machine interface have been the theme of study for some years[1,2], the scientific deliverables of which have formed the foundations for research into the next generation of the so-called “intelligent sewing machines” with which this article is concerned. Problems associated with “limp materials” Although there is enough progress in relating fabric properties with sewing machine settings and stitching quality, there are still areas that cannot be numerically defined because they have not just one value of acceptability, but many values which can change all the time. In some circumstances, properties interact with one another to produce a phenomenon – like a fault; in other instances they interact totally differently, producing a different fault or no fault at all. The behaviour is sometimes linear but, especially for a limited sample of fabrics, the majority of times it is vastly non-linear. These circumstances cannot all be effectively modelled. Seam quality itself can also have many definitions which are still subjective or even if measured have to retain the analogous subjective understanding: unbalanced stitch, without seam pucker, with no seam slippage, no holes, etc. Furthermore, during the sewing machine operation a discrete problem may occur which cannot be modelled, such as pulling the fabric – inducing undue thread tension for instance. Solutions to all these problems have to be effectively provided for in the design of a new control model.
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 49-55. © MCB University Press, 0955-6222
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The fabric/machine interface From the results of an earlier study of the effect of fabric properties on the sewing process[3-5], the following fabric properties have been found to interact with the sewing process: ● thickness; ● compression; ● bending; ● tensile; ● friction. These properties can automatically be measured using specially developed measurement systems. An industrial sewing machine has been instrumented with sensors so that the following data can be captured at different sewing speeds: ● sewing machine speed (measured by shaft encoder); ● thread tension (diaphragm-type strain gauge); ● tension disk pressure (strain gauge); ● presser foot pressure (strain gauge); ● feed dog pressure (strain gauge); ● feed dog differential (linear variable differential transformer). A representative sample of commercial fabrics was selected, and 69 experiments were conducted on each fabric at different sewing speeds. To establish the effect of thread tension and foot pressure on seam quality, more than 1,000 designed experiments in total took place. Analysis of these results established the theoretical sewing model[6] and the rules to the sewing machine control model. It has been revealed that sewing machine speed affects the static settings of the mechanisms which have an optimum of a particular fabric for an acceptable and consistent seam quality: ● Seam quality is reduced with increase of sewing thread tension. ● There is a relationship between presser foot pressure and sewing thread tension, seam quality is reduced with high foot pressure and high thread tension, while seam quality improves with increasing foot pressure and low thread tension. ● Generally speaking, seam quality is reduced with increasing sewing machine speed and is further reduced with increasing thread tension. ● There is an interesting trend for some fabrics to reach poor seam quality at speeds of around 3,500rpm, and others to reach high seam quality at presser foot pressure of around 28N.
Neuro-fuzzy control model Neuro-fuzzy The building blocks of fuzzy rules consist of linguistic expressions of the form control for ~ if x is Ã, then y is B, else y is C where C is the universe of discourse. sewing machines Relational rules of the form: ~
∼
∼
R = (Ã × B) ∪ (Ã × C ) are then composed from the specified matrix operations. Fuzzy logic linguistic control rules of the form:
(1)
51
if fabric is poor and speed is high then tension is low can be used effectively when human operators can express the control knowledge that they use in controlling a process in terms of rules of the above form. The linguistic rules determined from experimental data for the optimal control of the Rimoldi 194-G single needle industrial overlock sewing machine are tabulated below. The fuzzy system control algorithm takes two inputs, namely, sewing machine speed and fabric sewability, and maps them on to the optimum values of disk and foot pressures. While neural networks can be extremely well trained on numerical data once the output is known, nonnumerical knowledge and understanding of experts about the general trend and rate of change of variables’ values cannot be easily incorporated (as they can with fuzzy logic). Therefore, it appeared that the synergy between neural networks and fuzzy logic was needed to enable automatic learning by a fuzzy system. The sewability of any given fabric was predetermined prior to input into the fuzzy system. This preprocessing was performed by a neuron and a weight on each link between the neuron and a fabric property, trained using error back propagation, to predict accurately fabric sewability from its relevant physicomechanical properties. Furthermore, the learning capabilities of a neural network were used to optimize the input and output membership functions (Figures 1-4) of the fuzzy system by implementing the system in a neural network: ● Antecedents. The antecedents layer of the neuro-fuzzy network consist of neurons which compute fuzzy membership functions (the following) for their outputs according to the following expression: Low
1,500
Medium
3,000
High
4,500
Figure 1. Membership functions of the first input – speed of sewing machine
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µR (x, y) = max µA– (x) ^ µB– (y), 1 – µA– (x) ^ µC– (y) . (2) ● Rules (aggregation of relational rules). The rules layer consists of neurons which perform the composition and aggregation of fuzzy relational rules according to the following equations: (3) R = R1 ∩ R2 ∩ … Rn for conjunctive set of rules (and) (4) my (y) = Min [my1 (y), my2 (y), … , myn (y)] y ∈ Y
[(
(
))]
R = R1 ∪ R2 ∪ … Rn for disjunctive set of rules (or) my (y) = Max [my1 (y), my2 (y), … , myn (y)] y ∈ Y. Poor
Figure 2. Membership functions of the second input – fabric sewability
0
Low
Figure 3. Membership functions of the first output – foot pressure
Low/ medium
0
1.0
Medium
2,100
Medium/ high
3,000
Medium
240
(6)
Good
0.5
1,200
Low
Figure 4. Membership functions of the second output – disk pressure
Average
(5)
High
3,900
High
480
Implementation and validation Neuro-fuzzy Figure 5 shows the general concept of the system. It consists of a neural control for network which is the engine for the prediction of fabric sewability, the output of sewing machines which becomes the input of the neural fuzzy system. This incorporates fuzzy logic rules (Figure 6) in a neural network architecture where the tuning of membership functions have become automatic, making the system effective and 53 robust. Using neural fuzzy, the developed fuzzy system was implemented in a neural network with each layer of the network representing a step in the fuzzy logic procedure. The input layer consists of automatic determination of machine speed and fabric quality for a good seam. Fabric quality is determined by preprocessing fabric properties using neuron weights on the links between the properties and the neuron. The output layer performs the defuzzification of the fuzzy outputs. Fuzzy logic Fabric properties (input) Machine speed WD1
Optimum foot pressure
Double jersey WD2
WD3
Optimum thread tension
WS1 Fabric sewability Single jersey
WS2 WS3
Input layer
Output layer
WS4
Figure 5. Neural-fuzzy network
Sewability prediction
Low
Low/ medium
1,200
Medium
2,100
Medium/ high
3,000
3,900
High
Figure 6. Membership functions of the optimum disk pressure after automatic tuning
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The model has been implemented on an industrial sewing machine which has shown considerable improvements in stitching quality, especially when difficult-to-sew materials were used. In this investigation, all the input membership functions were left unchanged and automatic tuning was performed on output membership functions only (see Figures 7 and 8). The membership functions representing low optimum outputs were the most severely affected by the tuning, in terms of magnitude of change, and the direction of tuning, i.e. they were reduced. The other membership functions were, however, marginally increased in size. Low
Figure 7. Membership functions of the optimum foot pressure after automatic tuning
Medium
0
High
240
1
480
C SL SR Low
Low
2 Machine speed
C SL SR Low/ med
Med
3 High
C SL SR Med
4
Foot pressure
C SL SR Med/ high
5
C SL SR High
6 Poor
7 Fabric sewability
C SL SR Low
C SL SR Average
8
Med
Thread tension
C SL SR Good
Figure 8. Model of fuzzy logic rules (overlock)
Inputs
Antecedents
9
High
Rules
Consequents
Output
Discussion and conclusion Neuro-fuzzy The synergism of a neural, neuro-fuzzy approach has been found most control for successful for modelling the control of sewing machinery for complex sewing machines interactions with limp materials. It is now possible to optimize sewing machinery settings automatically, statically and dynamically, under any material to be stitched. This research establishes the next generation of 55 intelligent sewing machines in the newly developed area of intelligent textile and garment manufacturing systems. References 1. Stylios, G., “Prognosis of sewability problems in garment manufacture using computer based techniques”, Proceedings of the IEEE International Conference on Systems Engineering, Pittsburgh, PA, 9-11 August 1990. 2. Stylios, G. and Fan, J., “An expert system for fabric sewability and optimisation of sewing and fabric conditions in garment manufacture”, Proceedings of the 1st International Clothing Conference – Textile Objective Measurement and Automation in Garment Manufacture, 9-11 July 1990, Ellis Horwood, New York, NY, 1991. 3. Stylios, G., Fan, J., Sotomi, O.J. and Deacon, R., “A new concept in garment manufacture; the sewability integrated environment incorporating automatic objective measurement systems”, 2nd International Clothing Conference, July 1992 and in International Journal of Clothing Science and Technology, Vol. 4 No. 5, 1992, pp. 45-8. 4. Stylios, G. and Sotomi, O.J., “Automatic settings of optimum sewing machine conditions for the manufacture of high quality garments made of lightweight synthetic fibre fabric”, Proceedings of the 21st Textile Research Symposium on Basic Properties of Fibres and Fibre Assemblies, Performance and Design of New Fibrous Materials, Japan, 7-9 August 1992. 5. Jamshidi, M., Vadiee, N. and Ross, T.J. (Eds), Fuzzy Logic and Control: Software and Hardware Applications, Prentice-Hall, Englewood Cliffs, NJ, 1993. 6. Berenij, H.R. and Khedlar, P., “Learning and tuning fuzzy logic controllers through reinforcements”, IEEE Transactions on Neural Networks, Vol. 3 No. 5, 1992, pp. 724-40. 7. “A neuro-fuzzy control system for intelligent sewing machines”, accepted for the IEEE International Conference on Intelligent Systems Engineering, Germany, 1994.
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Garment sewing processing parameters Determination using numerical methods and computers Dubravko Rogale Department of Clothing Technology, University of Zagreb, Zagreb, Croatia Introduction Conventional measurings with the aim of investigating processing parameters of garment sewing were carried out using stop watches or electromechanical monitoring devices. The results obtained were, in most cases, the time periods needed to perform certain technological procedures or operations. On the basis of the data collected, time norms, as well as the elements and structure of a particular technological operation, could be defined. The accuracy of these movements is uncertain, however, owing to the possible error resulting from delayed reaction of the person performing the measurement.This error can be significant when measuring short intervals. Electromechanical monitoring devices contribute to measuring accuracy, but reading off of the results is not easy and takes quite a lot of time[1]. Computers have made quick and accurate measuring possible, as well as storing quite a number of measured data into the computer memory banks, together with the subsequent mathematical processing and analysis of the data obtained[2].
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 56-60 © MCB University Press, 0955-6222
Characteristics of measuring systems Measuring systems for performing the measurements of processing parameters are, in most cases, concerned with the measurements of rotation speed of the sewing machine main shaft. Alternate and direct tachogenerators are used for this purpose[2], as well as incremental transformers of turn angle[3] or contactless measuring methods[4]. Mounting a tachogenerator or an increment tranformer on the machine has a number of disadvantages: the mounting must be done by an expert; problems with rotation axis centring arise; the elements mounted change the mechanical properties of the system (for example, coefficient of inertia is changed); the sewing machine headstock geometry is changed through the addition of the measuring elements, which can lead to certain changes in methodology of the work done; sometimes it is simply impossible to mount the necessary elements as a synchronizer is already fixed on the shaft.
Owing to the above disadvantages, the best results have been obtained Garment sewing through the implementation of contactless methods of measuring the rotation processing speed of the sewing machine main shaft, with infrared reflexive diodes used as parameters a measuring converter. Measuring principles are rather similar for all the methods mentioned: the measuring signals are guided from the sewing machine main shaft, through the 57 stitching speed measuring converter, to the measuring device, and then are introduced into the computer with the help of an analog/digital converter (A/D converter), where measuring data pairs are stored – e.g. stitching speed and time period concerned (Figure 1). On the basis of data pairs stored, and using the computer diagrams of the functional dependence of stitching speed change according to the time period, or the diagram of the function vs = vs(t), Figure 2 can be constructed during measuring or in the subsequent analysis. When the data pairs stored in the computer memory banks are arranged in the form of a field or a matrix with two columns, they can be subjected to the operations of searching and calculating, or numerical methods of differentiation/integration can be applied[5,6]. Through the application of a rather simple computer operation of searching and looking for the data with the top value, the parameter of maximum stitching speed (stitches/minute) can be obtained. If n measurements have been performed, and n pairs of stitching speeds recorded, the searching can offer a maximum value of stitching speed, vsmax: vsmax = max {vs1, vs2, … , vsn}.
(1)
Through the application of a rather simple computer operation of searching and looking for the data with the top value, the parameter of maximum stitching speed (stitches/minute) can be obtained. If the maximum value of the stitching speed is known, it also means that the corresponding part of the couple, indicating the time period in which the top speed has been attained, has also been found. This searching can be done during the analysis of the sewing of one segment of the seam, or for the seam as a whole. The parameter of the average stitching speed vsred can be calculated using the following expression:
Applied numerical methods
Vs Sewing machine
Workplace
Measuring transducer
A/D converter
Computer New process parameters
Figure 1. Principles of measuring stitching speeds
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File Program Edit Fonts Colours Patterns Lines Windows H 6,000
Figure 2. Diagram depicting the dependence of stitching speed changes on the time period, obtained through the use of a computer, on the basis of the data pairs stored
Sewing speed (st/min)
Number of stiches per segment: 476 Proportion of maximum speed work: 53.4% Proportion of time in operation: 24.0%
58
BASIC2
Results-1
Segment 1 t = 12.6 s
4,000 lpp = 56 tpi = 3.5 s dpp = 201 tzi = 12.6 s
2,000
0 0
10
20
30
dt = 9.1 s vm = 4544 vp = 3125 ub/min 40Time (s)
n
∑ vst
vsred =
i =1
. (2 ) n Similarly, employing searching and simple calculations, the parameter of the part the machine operates at maximum rotation speed, umax, can be determined. The number of measurements at which maximum sewing speed has been recorded is determined through the operation of searching. Supposing n measuring results have been recorded, and among them m with the maximum speed values, then: umax = 100m/n. If the time period necessary to measure a single datum is known (e.g. acquisition time, t ak) for the measuring system, the parameters of sewing machine operating time at maximum speed (tmax = mtak ), or total time (t1 = tak ) can be determined. Similarly, some other parameters can be easily determined, e.g. the parameter of the machine operating time in a technological operation, the parameter of the machine main shaft idling, degree of sewing machine utilization, and a number of other practical parameters. As the measurements are ascertained using a computer, the values measured are much more accurate than those measured by conventional methods. Application of numerical methods Data pairs stored can be used as a basis for application of numerical methods of differentiating and integrating. Differentiating can be used to determine the derivation of the functional dependence of stitching speed:
a s = a s (t ) =
dvs (t )
.
(3 )
dt Deriving the functional dependence of stitching speed according to the time period vs(t ), we get the new parameter describing the functional dependence of sewing acceleration on the time period. This new functional dependence, which could not be determined by employing conventional measuring methods up to now, can significantly influence future investigations of the process of sewing. A new field with data couples indicating main shaft rotation speed changes (acceleration and deceleration) is another product of derived numerical differentiation, and these speed changes can be extremely important when investigating sewing machine operating dynamics, energy consumption, etc. Multiplication of data fields with stitch length p, a new parameter denoting the functional dependence relating to workpiece ai acceleration, can be obtained: dv (t ) a s = p ⋅ a s (t ) = p ⋅ s . (4 ) dt By employing numerical methods of integration the parameter of the number of machine stitches performed, ns, can be determined: ns = ∫ t 1 vs (t ) dt . t0
(5 )
In this way, using computer and numerical methods of integration, the number of stitches in a segment of the seam can be determined, for the seam as a whole or for an interval from t0 to t1. By employing simultaneous integration and multiplication by sewing machine stitch length, the parameter of functional dependence of seam parts connected according to time period (ss) can also be determined: ss = s0 + p ⋅ ∫ t 1 vs (t ) dt . t0
(6 )
Figure 3 gives a symbolic presentation of deriving other functional dependences from the initial data concerned with the functional dependence of sewing speed changes. Broadening the range of usable processing parameters can significantly influence research in the field of clothing technologies. Conclusion The procedures of measuring and storing data on stitching speeds can be used to determine a number of new processing parameters for garment sewing, employing the methods of data searching, calculating and numerical methods. The possibility of using numerical methods opens the way to a broad range of
Garment sewing processing parameters 59
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– ai
ai = ai (t ) t
60
– ai + as
as = as(t )
t
– as
Vs
Vs = Vs(t )
t
ns
Figure 3. Symbolic presentation of deriving other functional dependences from the initial data concerned with the functional dependence of sewing speed changes
ns = ns(t )
t
ss
ss = ss(t ) t
new and useful processing parameters which can enhance research in the field of clothing technologies to a substantial scientific level. References 1. Rogale, D., “Measuring methods and equipment for determination of process parameters of technological operations of clothing production”, Tekstil, Vol. 40, 1991, p. 12. 2. Knez, B., Rogale, D. and Dragc˘ evic´, Z., “Influence exerted by the structure of machine time on the degree of exploitation of sewing machine”, Tekstil, Vol. 34, 1985, p. 8 3. Z˘unic˘ Lojen, D. and Knez, B., “Vpliv metode s˘ivanja na strukturo tehnolos˘kih operacij s˘ivanja, 1. simpozij ‘Oblac˘ilno inz˘enirstvo ’92’ ”, Ljubljana, 1992, University of Maribor, Maribor, Slovenia, 1992. 4. Rogale, D. and Knez, B., “Determination of yield factor of nominal speed of sewing machines and of average sewing speed in apparel manufacture”, Tekstil, Vol. 39, 1990, p. 11. 5. Shoup, T.E., Applied Numerical Methods for the Microcomputer, Prentice-Hall, Englewood Cliffs, NJ, 1984. 6. Miller, A.R., Basic Programs for Scientists and Engineers, Sybex, Berkeley, CA, 1981.
Use of mathematical programming for workplace design in sewing rooms Karl Gotlih
Mathematical programming for sewing rooms 61
Maribor Institute for Textile and Garment Manufacture Processes, University of Maribor, Maribor, Slovenia Introduction In the clothing industry a sewing machine operator must sew a cloth part for all of his/her working day. The job is not very difficult, but it is a repetitive job with a very high output. Such jobs are very tiresome. The aim of this article is to show an approach to workplace design. The designing process is the optimal location “x g, y g and zg” search in a non-linear optimization procedure. The values xg, yg and zg are the co-ordinates of the operator’s seat with respect to the sewing machine needle, as shown in Figure 1. The whole optimization process is simulated on a computer, so the operator must also be simulated as a kinematic chain. The trouble is that human beings have rotational joints in their arms with three degrees of freedom. This will result in a human arm (left or right) with nine degrees of freedom each (three in the shoulder, three in the elbow and three in the wrist). These kinds of mechanism are highly redundant and there is no simple algorithm to control such an arm in a human manner. To overcome this problem, the arm was modelled as a redundant four degrees of freedom mechanism, with just the dominant degrees of freedom of a real human arm. There are three degrees of freedom in the shoulder and one in the elbow. The clothing parts are positioned on the table of the sewing machine, so the orientation’s part of the human arm can be omitted. This means that the wrist is not included in the arm model. This arm is shown modelled in Figure 2. The well-known prescribed task for which the workplace is designed is measured in real production, is given in the reference co-ordinate system of the sewing machine needle and gives a description of the movement for the left and right arm of the operator. The task is time dependent and prescribed was precisely as it was measured. Problem definition The optimal search of the operator’s location with respect to the prescribed task is closely connected to the inverse problem in the kinematics because, in the inverse kinematics, the response of the operator’s model is analysed with respect to this task.
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 61-70. © MCB University Press, 0955-6222
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z0
z0
Sewing machine
x0
62
yg
y 0 zg Operator's seat
y0 xg
x0
Figure 1. Technological workplace with a basic sewing machine
Z0
Ish
11
12
ϕ3
ϕ1
Is x0
Y0
Sewing machine
Operator
x0
Figure 2. Kinematic model of an operator behind a sewing machine
ϕ2
ϕ4
ϕ3
Operator's seat
The inverse problem in the kinematics was solved for the simplified redundant kinematic model of the operator’s arms with the use of a global optimal control algorithm introduced by Kazerounian and Zhaoyn[1]. For each point in the task space:
u(t ) = {x (t ), y(t ), z(t )}T
(1)
we get a point in the configuration space:
ϕ (t ) = {ϕ 1 (t ), ϕ 2 (t ), ϕ 3 (t ), ϕ 4 (t )}T
(2 )
63
where t ∈ [0, τ].
τ is the end time of one cycle of the technological process. The global optimal control algorithm was developed with the use of the calculus of variations. The aim of this control algorithm is to find a path from the prescribed starting point of the task to the end point of the task in such a manner that a chosen functional will be the minimum of all possible paths. The functional in this algorithm is written in the form: I =∫
tf t0
(ϕ˙ Τ ϕ˙ ) dt
(3 )
and the constraints are the governing kinematic constraints of the open kinematic chain: G k (ϕ , t ) = u − f (ϕ ) = 0.0.
(4 )
The solution of such an optimization problem gives a boundary value problem written in the form: +
ϕ˙˙ = J (ü − ((ϕ˙ T ⊗ I m ).
∂J ∂ϕ
).ϕ˙ )
(5 )
where ⊗ denotes the Kronecker product and Im the m × m identity matrix. The matrix J + is the n × m generalized or Moor-Penrose pseudoinverse of the Jacobian matrix of the redundant open kinematic chain with the form: +
Mathematical programming for sewing rooms
T
T −1
J = J .( J . J ) .
(6 )
The start and end values of the variables for the boundary value problem are given with the boundary conditions: + J . u˙ (tb ) = ϕ˙ (tb )
where tb = t0 or tb = t f .
(7 )
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The boundary value problem (5) with boundary conditions (7) was solved numerically with a routine with the code name D02HAF from the Numerical Algorithms Group (NAG) library[2]. This routine solves the two-point boundary-value problem for a system of ordinary differential equations, using a Runge-Kutta-Merson method and a Newton iteration in a shooting and matching technique.
Optimization of the operator’s location A mathematical model for the operator’s location optimization was developed in the form: min (max F (x, ϕ, t))
(8)
x ∈ ℜn t ∈ [0,τ]
subject to gi (x, ϕ, t ) ≤ 0.0 where t ∈ [0, τ] and i = 1, … , m
(9)
and the state equation: h(x, ϕ, t) = 0.0 and t ∈ [0, τ].
(10)
In equations (8), (9) and (10), x is the vector of the design parameters, ϕ the vector of state variables (2), and u the vector of the task in task space (1). This mathematical model must be transformed before a non-linear programming algorithm is used. To do so a method contributed by Haug[3] was used. The method of non-linear programming is not directly useful for this kind of problem, unless we remove the max-value function from (8) and reformulate the time dependency of the problem. To do so we add an auxiliary design parameter xm + 1, and a constraint: F(x, ϕ, t) – xm + 1 ≤ 0.0 and t = [0, τ]
(11)
and replace the objective function (8) by: F0 = xm + 1.
(12)
We substitute the constraints (9), (10) and (11) by equivalent integral constraints. For a continuous function a(t) we may replace the inequality: a(t) ≤ 0.0 t = [0, τ]
(13)
by the equivalent integral constraint: τ ∫ 0 a (t ) dt = 0.0
where <•> is defined by:
(14 )
a , a ≥ 0.0 a = 0 , a < 0.0.
Mathematical (15 ) programming for
sewing rooms
The problem (8)-(10) yields the form:
65 min ( xm + 1 ) x ∈ℜm + 1
(16 )
subject to Fi = ∫ τ0 g i ( x , ϕ , t ) dt = 0.0 i = 1, 2 , 3 , … , n
(17 )
and Fn + 1 = ∫ τ0 ( F ( x , ϕ , t ) − Xm + 1 )
(18 )
The design parameters and the state variables are connected by the equation (5) with conditions (7) and (10). The transformed optimization problem can be solved by the sequential quadratic programming (SQP) method. We used a routine named E04UCF from the NAG library[2]. This routine is designed to minimize an arbitrary smooth function subject to constraints, which may include simple bounds on the variables, linear constraints and smooth non-linear constraints. E04UCF uses a sequential quadratic programming algorithm in which the search direction is the solution of a quadratic programming problem.
Example of workplace design As an example, a sewing operation on an upper part of a sleeve on a basic sewing machine is shown. The movements of the operator’s left and right arms are shown in Figure 3. The objective functional from (8) is the quadrate length of the vector of angular accelerations of the joints for the left and right arms for the operator’s kinematic model from Figure 2: n
F ( x , ϕ , t ) = ∑ ϕ i2 .
(19 )
i =1
The time intervals for the five parts of the whole cycle are t1 = 3.00 s, t2 = 3.10 s, t3 = 3.80 s, t4 = 4.00 s and t5 = 4.00 s[4]. The total time is ttot = 17.90 s. The
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t
x
y
z
t
1
0. –0.1
0.
2
0. –0.1
0.
3
0. –0.33
0.
4
0. –0.33
0.
4 –0.03 –0.13 0.
5
0. –0.1
0.
5 –0.03 –0.20 0.
6
0. –0.1
0.
6 –0.03 0.00 0.
y0
x
y
z
y0
1 –0.3 –0.07 0. 2 –0.03 –0.07 0. 3 –0.03 –0.20 0.
[m]
[m]
[m]
x0 6
1
5
2
x0
6 2 1
Right arm move
4
Left arm move 5 4
Figure 3. The movements of the left and right arms of the operator
3
3
velocity profiles for each part of the cycle are chosen as a function of time, and are given in the task space: ü (t ) =
C 2πT
.cos(2π
t T
)−
C 2πT
t ∈ [0, T ].
(20 )
In the equation (20), C is an additional constant, depending on the length of path i and T the time for passing the section i of the technological process. The procedure for the transformation of the optimization problem into the form for numerical calculation is shown in the article[5]. The dimensions and the kinematic constraints of the rotational joints for the right and left arms for the simplified redundant kinematic model of the operator are given in Verhovnik and Polajnar[6] and DIN 33402[7]. In the boundary value problem (5) with the boundary constraints (7) we use the Jacobian matrix of the open kinematic chain (left or right arm model). This matrix is in this case for the redundant mechanism with four degrees of freedom given:
Mathematical programming for sewing rooms
− l s c − l s c c + l s s c s + l c s s , − l c s − l c s c − l c c c s , 1 1 2 2 1 2 4 2 1 2 3 4 112 2 12 4 2 123 4 213 4 J = 0.0 , l1c2 + l2c2c4 − l2 s2c3 s4 , − l1c1c2 − l2c1c2c4 + l2c1s2c3 s4 − l2 s1s3 s4 , l1s1s2 + l2 s1s2c4 + l2 s1c2c3 s4 , + l2 s1s3c4 − l2c2 s3 s4 , − l2 s2 s4 + l2c2c3c4 − l2 s1s2 s3 s4 + l2c1c3 s4 , l2 s1c2 s4 + l2 s1s2c3c4 − l2c1s3c4
67
l2c1s2c3 s4 + l2 s1c3 s4 , − l2c1c2 s4 − l2c1s2c3c4
where si = sin(ϕ i ) and ci = cos(ϕ i ).
(21)
In the Jacobian matrix (21), l1 is the length of the upper arm and l2 the length of the forearm (Figure 2). The optimization of the problem was performed with an SPQ algorithm. The SQP method is a gradient method and we need either sensitivity analysis to obtain all components of a gradient or we use a standard subroutine[2] where all components of the gradient are calculated numerically. Our choice was numerical differentiation. Simulation results The results of the simulation for the given task for the left and right arm movement (Figure 3), are shown in Figures 4-7. Figure 4 shows the values of the angular joint velocities for the left arm for the operator’s initial position, Figure 5 shows the joint angular velocities for the left arm for the operator’s optimal position, and Figure 6 the joint angular velocities for the right arm for the operator’s initial position. Figure 7 illustrates the joint angular velocities for the right arm for the operator’s optimal position. The initial operator’s position is, as shown in Figure 1, defined with the coordinates xg = 0.0 m, yg = –0.15 m and zg and is defined with the human body dimensions (zg = 0.25 m). The optimal position obtained with the developed procedure is defined with the co-ordinates xg = 0.0 m and yg = –0.2 m. From Figures 4 and 5 it can be seen that the velocities in the optimal operator’s posture are smaller for the second degree of freedom. This fact means that the left arm in the first section will move relatively slower, but it completes its task according to sense. Figures 6 and 7 show the opposite. The velocities are greater for the optimal operator’s posture, but not so much that the operator would be much more loaded. This happens because the left and right arms are connected with the body and so the change of the posture of the body results in change of the kinematic parameters of the arms. The optimization procedure tries to minimize the biggest values and so it happens that the smaller values are slightly increased.
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Angular velocities (rd/s), LI
0.75
68
0.5 0.25 0 –0.25 –0.5 –0.75 –1 0
Figure 4. Joint angular velocities for initial operator’s location, left arm
50
100
150
200
250
t/100 (s) Key: 1
2
3
4
1
Angular velocities (rd/s), LO
0.75 0.5 0.25 0 –0.25 –0.5 –0.75 –1 0
Figure 5. Joint angular velocities for optimal operator’s location, left arm
50
100
150
t/100 (s) Key: 1
2
3
4
200
250
Mathematical programming for sewing rooms
0.5
Angular velocities (rd/s), RI
0.4 0.3 0.2
69
0.1 0 –0.1 –0.2 –0.3 –0.4 –0.5 0
50
100
150
200
250
Figure 6. Joint angular velocities for initial operator’s location, right arm
t/100 (s)
Key: 1
2
3
4
0.5
Angular velocities (rd/s), RO
0.4 0.3 0.2 0.1 0 –0.1 –0.2 –0.3 –0.4 –0.5 0
50
100
150
t/100 (s)
Key: 1
2
3
4
200
250
Figure 7. Joint angular velocities for optimal operator’s location, right arm
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Conclusions In this work an approach to workplace design was shown. The aim was to find such a position of the sewing machine operator behind the sewing machine that the angular velocities, which are produced by the arms (shoulder and elbow) for the realization of the prescribed job, are small. The cost function is for that reason chosen in such a way in the optimization process that it guarantees smaller relative angular accelerations of the arms for the fixed cycle time and, through this, smaller velocities. This results in a smaller energy consumption for the chosen task. References 1. Kazerounian, K. and Zhaoyu, W., “Global versus local optimization in redundancy resolution of robotic manipulators”, The International Journal of Robotics Research, Vol. 7 No. 5, October 1988, pp. 3-12. 2. Numerical Algorithms Group, The NAG Fortran Library Introductory Guide – Mark 13, The Numerical Algorithms Group, 1988. 3. Haug, E.J. and Arora J.S., Applied Optimal Design, John Wiley & Sons, New York, NY, 1979, Ch. 5, pp. 329-86. 4. Z˘unic˘ Lojen, D., “Structure of a technological operation in the sewing process” (in Slovene), Msc thesis, University of Maribor, Slovenia, 1993. 5. Gotlih, K., “Optimal location of the robot base in a flexible manufacturing cell with respect to a prescribed task”, ICARCV ’92, Singapore, pp. IA 6.6.1-IA 6.6.5. 6. Verhovnik, V. and Polajnar, A., “Work design and the work place design”, University of Maribor, Slovenia, 1994. 7. DIN 33402, Körpermasse des Menschen, Beuth Verlag GmbH, Berlin, 1978.
Rheological properties of threads Their influence on dynamic loads in the sewing process
Rheological properties of threads 71
Jelka Gers˘ak University of Maribor, Slovenia Introduction Definition and cognition about the influence of rheological properties of a sewing thread on its dynamic loads in a stitch formation process are important for optimization of the sewing process. They are also important for the seam strength planning. The strength of a seam must meet the conditions of garment use and therefore form loading. In spite of the fact that there are numerous analyses of the seamability of a thread, defined as a number of sewing thread breaks in the sewing process, as well as scientific research works on the dynamic load of a thread in a stitch formation process and its influence on change in sewing thread strength[1-5], the influence of rheological properties of a thread on its dynamic loads has not been thoroughly investigated. The complexity of a sewing process and general requirements of technological dependent forces in a stitch formation process; the rheological properties of sewing threads and their influence on dynamic loads, and thus arising change in a thread strength will be presented in the frame of this contribution. Sewing thread rheological properties On the basis of knowledge of mechanical properties of fibres and threads, it can be assumed that these structures have visco-elastic properties[2,5,6]. They have the properties of elastic hard substances and viscosity of fluids. Visco-elastic behaviour of threads is not coincidental. It is conditioned by a complex anisotropy arrangement of molecules, on which acts the macroscopic mechanical deformation. Starting out from differential network model, Figure 1, which is formed of macro-molecules with crystalline and amorphous regions, the mechanical properties of textile fibres can be explained. The molecules are oriented along the longitudinal fibre axis and joined together by inter-molecular joints. Moving of macro-molecules and thus arising deformation depends on intensity of acting force and are changing with time and temperature. The deformation, which is a thermodynamical change of condition, occurs when a
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 71-80. © MCB University Press, 0955-6222
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72
Crystal region
Figure 1. Network model with amorphous and crystalline regions
Source: [6]
fibre is influenced by a force. This deformation, is shown in a way of extension. The resulting deformation could be elastic or plastic. It depends on intensity and duration of loading, as well as on duration of relaxation, and is more or less reversible. A part of used energy is hereby stored as potential energy. The other part will be changed by heat which causes defined structural changes. It increases the moveability of macro-molecular segments, which causes greater or minor deformations. The deformation energy is reversible to the extension work. This is valid only for slow deformations, since during fast deformations, the molecules produce greater resistance. The additional deformational resistance is bigger if deformation is faster in relation to normal speed of conformational change in a fibre. The resulting deformation could be elastic or plastic. The elastic deformation is given by Hook’s law: Ex = (dσx /dt)
(1)
while the plastic deformation is given by Newton’s law of viscosity:
σx = ηx(dx/dt) where
σ η Ε x
= tension (tractive or pressure) = viscosity = module of elasticity = deformation.
(2)
The combined effect of the interaction of plastic and elastic behaviour of a thread in a produced seam can be described by using simple models. We can take a spring as a model for pure plastic body. As a model of pure elastic body, a piston, which moves in a viscid medium, can be taken (Figure 2). On the basis of given models, the characteristic parameters, such as: module of elasticity, viscosity index, etc. could be defined. The behaviour of deformation as a function of tension, temperature and pressure can be described with the mathematical expression on the basis of rheological law, which enables the calculation of the material behaviour for every kind of loading. The time-dependent behaviour of characteristic parameters depends on relaxation. The fibre does not follow the directly acting force because of its visco-elastic behaviour. The final condition is approached a bit slower. That condition corresponds to the acting force and to state of balance. Such process can be described on the basis of exponential law[7]:
Rheological properties of threads 73
Load
Time t Deformation D Pure elastic
D Pure plastic
D Vogt-Kelvin
D Maxwell
D General
Figure 2. Reaction of different models of bodies on a course of mechanical loading
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x = x0e–λt
(3)
where x = state in the time t x0 = state in the time t = 0 λ = relaxation or coefficient of declination. The formed deformation is not a linear function of acting force. However, it depends on speed and duration, as well as on intensity of a load. The same processes occur at mechanical deformations of fibres and threads. If a fibre or a thread is being exposed to the constant straining load, the result will be defined extension which will increase in time. The delay between a source and effect depends on visco-elastic behaviour of a fibre or thread respectively. This leads to: ● relaxation of the extension, which characteristic is delay of extension by fast stretching loads; this is a reason for the following time-dependent deformation; ● relaxation of a tension, which reflects as a time-dependent change of tension at constant deformation and temperature; fast extension is followed by delayed declination of tension; ● appearance of hysteresis that refers to cyclic repeating – sinus acting tension, which causes a phase delay of extension of the same frequency at dynamic loads. The given relaxation processes are of essential importance for further processing properties of fibres and threads. Investigation of rheological properties of threads Visco-elastic behaviour of threads could be followed by statistical and dynamomechanical tests. During statistical tests, influence of acting force and deformations is observed in a longer period of time. The dynamic tests provide information on thread behaviour in very short time intervals. A study of visco-elastic properties of the tension σ – deformation ε curve is important in the frame of statistical tests[7]. Thereby the following parameters, gained on the basis of the tension σ – deformation ε curve[7,8], are significant: ● module of elasticity E; ● extreme value of module of elasticity change; ● loading σy and extension εy in the flow point; ● work until the break Ap, a part of elastic work Ael and work till the flow point Ay. The module of elasticity, which is gained from the first derivative of the curve σ(ε), represents the resistance against further deformation which appears in a
thread. The modules of elasticity E0 , E1 and E2 can be defined from the course of the elasticity curve E. Starting module of elasticity E 0 and adequate extension ε0 are defined in the first gradation point of the curve E. Modules E1 and E2 as well as adequate extensions ε1 and ε2 are defined in the second resp. third gradation point, where the second derivative σ" = 0, which means at minimum resp. maximum. The starting module of elasticity E0 appears at the start of loading. The deformation is at this point in proportion to loading since it is in the area of elastic deformation, where the Hook’s law is valid. The elasticity threshold is followed by the plastic threshold, resp. the threshold of flowing σy, which gives the force that is responsible for the first irreversible change or deformation. The flowing threshold is of practical importance. The definition of the force which causes the first irreversible deformation is namely a basis for the definition of the allowed loading of a thread in the sewing process. Furthermore, it presents also a basis for determination of maximal dynamic loading during use of a garment. The evaluation of the tension σ – deformation ε curve could be explained in this way: some “loose” fibres are being stretched in a longitudinal direction when a thread is exposed to stretching load. Hereby a movement of the fibres, spun into thread, does not appear. After that stage, greater force is needed for stretching of a thread. This reflects in maximum of the module of elasticity E (value E0). This value of module of elasticity is rapidly reduced for acting stretching load and causes first slips of fibres. The stretch loading remains unchanged from that moment, which reflects in reduction of the module of elasticity. It reduces until the value E1. In this point, the slipping and deformation of fibres, spun into thread, are the largest. From that moment, the stretch loading causes bigger and bigger strains of fibres. Since the fibres are very stretched, they cause a pressure in direction, transversal to the acting load. This leads to increase of a tension. At the same time, the module of elasticity is also increasing. Break of a thread will occur when fibres, spun into thread, do not hold further loading (value E2 ). Tests of flowing and relaxation present a transition from static to dynamic measurements. Flowing gives a relative deformation under the constant deformation and temperature. Flowing increases with the duration of loading and directly reflects visco-elastic properties of threads. In contrast to static tests, during the dynamic tests, the tension and deformation are observed in very short time periods by sinusoidal oscillating tension which causes sinusoidal oscillating deformation. From the rheological point of view, determination of dynamic loads of threads are important, these are: ●
rheological elasticity;
●
plasticity;
Rheological properties of threads 75
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●
flowing;
●
relaxation of extension and tension;
●
hysteresis at cyclic loading.
Methodology The influence of rheological properties of threads on their dynamic loads in the sewing process can be shown on the basis of study of visco-elastic properties, i.e. tension σ – deformation ε curve. For that purpose, the research on viscoelastic properties of a sewing thread before and after loading in the sewing process has been carried out. The material used was a thread spun out of a 100 per cent PES fibres – core with PES coat. Two threads from two different producers were used (Table I). Research has been carried out on a fabric from 100 per cent combed cotton in twill weave with the surface mass 399gm –2. The analysis of visco-elastic properties of applied threads after loading in sewing process has been carried out for two threads, unsewn from the samples with dimensions 2,000mm × 150mm. Samples were sewn with the seam type 1.01.01/301[9], 10mm from the edge. The technical and technological sewing parameters listed in Table II, were applied. Results Achieved results of research on influence of rheological properties of threads on their dynamic loads in sewing process are shown on the basis of measurements of breaking tension σ, breaking elongation ε, work A and evaluated viscoelastic parameters of threads before and after loading in the sewing process. These results are shown in Table III. The course of module of elasticity E,
Thread type Table I. Properties of applied threads
PES/PES PES/PES
Thread code Table II. Technical and technological parameters of sewing
Sa Po
Code
Fineness T1/tex
Breaking F/cN
Breaking elongation ε/per cent
Twist Tm/m–1
Sa Po
21.76 × 2 21.41 × 2
2,136.39 2,188.10
20.80 19.92
777 669
Static tension of Thread stretch Thread dynamic needle thread force strain σs/cNtex–1 F/cN σD/cNtex–1 6.47 6.54
536.30 429.48
12.40 10.03
Stitching velocity Average Maximal np/min–1 nmax/min–1 4,038.23 4,045.32
4,234.38 4,238.41
Properties of a thread Breaking tension σ/cNtex–1 Breaking elongation ε/per cent Work until break Ap/MJ Work until flowing Ay/MJ Tension in flow point σy/cNtex–1 Extension in flow point εy /per cent Module of elasticity E0/cNtex–1 Extension ε0/per cent Module of elasticity E1/cNtex–1 Extension ε1/per cent Module of elasticity E2/cNtex–1 Extension ε2/per cent
Thread with the code Sa Before After loading loading 49.09 20.80 97,130.00 965.20 5.38 1.68 351.11 1.28 97.78 4.43 406.67 14.69
37.99 16.21 57,380.00 84.05 1.13 0.70 165.00 0.28 130.00 0.98 353.33 14.00
Thread with the code Po Before After loading loading 51.10 19.91 99,140.00 987.60 5.59 1.70 355.56 1.25 91.11 5.13 401.00 16.31
39.93 16.20 57,390.00 82.42 1.14 0.71 163.89 0.38 148.61 0.93 383.33 12.50
change of velocity of module of elasticity and its extreme values in relation to deformation are shown in Figures 3 and 4. Discussion From results of research work on visco-elastic properties of sewing threads before and after loading in the sewing process, it can be seen that after loading both analysed threads show a substantial reduction of module of elasticity E0. By the thread with the code Sa, the module E 0 is reduced from 351.11165.00cNtex–1 and by the thread with the code Po from 355.56-163.89cNtex–1. Analyses of gained results show also a decrease of breaking strength for 22.62 per cent by the thread with the code Sa and 21.86 per cent by the thread with the code Po. The reduction in thread strength is a consequence of dynamic loads in the sewing process. It causes decreased resistance against further loading, which reflects in reduction of the module of elasticity E0 and growth of the module of elasticity E1 regarding the values before loading in sewing process. In this way, the module of elasticity E1 which presents the state of maximal slipping and deformation of fibres, spun into thread, shows an increase of 32.22cNtex–1 by the thread with the code Sa and 57.50cNtex–1 by the thread with the code Po regarding the initial values of E1. In comparison with the module of elasticity E1 after loading in the sewing process, the module E1 is not significantly reduced – only for 35.00cNtex–1 by the thread with the code Sa and for 15.28cNtex–1 by the thread with the code Po. Reduced values of the module of elasticity E0 and higher values of the module of elasticity E1 considering the values before loading in the sewing process do not reflect only the reduced area of elastic deformations but also the increased area of plastic deformations. This statement confirms the achieved values of
Rheological properties of threads 77
Table III. Results of research of breaking tension σ, breaking elongation ε, work A and visco-elastic parameters of threads
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6
75
80 7
4
50
40 3.5
2
25
v
(1)
78 0 0
0
–40 –3.5
–2
0
(3) –60 –7
22.5
25.0
18
20
20.0
17.5
15.0
12.5
10.0
7.5
5.0
(1)
2.5
–4 (2)
0
(3)
Extension (a)
0 0
0
0
–150 –9.5
–2
–300 –19
–4
(3)
(2)
Key: v cN/tex
(1)
16
20
14
2
12
150 9.5
(2) (3) v
10
40
8
4
6
300 19
4
60
2
6
0
Figure 3. Visco-elastic properties of a thread with the code Sa (a) before loading in sewing process; (b) after loading in sewing process
450 28.5
Extension (b) (1)
(2) cN/tex%
cN/tex%2
(3)
cN/tex%3
loading in the flowing point. The flowing point is after loading in the sewing process reduced from 5.38cNtex–1 to 1.13cNtex–1 by the thread with the code Sa and from 5.59cNtex–1 to 1.14cNtex–1 by the thread with the code Po. Conclusions The study of the network model and rheological properties of fibres, which can be transmitted into thread, shows that rheological properties of sewing threads directly influence the dynamic loads of threads in a stitch formation process.
240 21
6
75
160 14
4
50
80 7
2
25
0 0
0
0
–80 –7
–2
Rheological properties of threads
v
79
(1)
(2) –160 –14
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
(1)
2.5
(2)
0
(3)
–4
Extension (a)
–150 –8
–2.5
–300 –16
–5
(3)
(2)
Key: v cN/tex
(1)
20
0
18
0
0 0
16
20
14
2.5
12
150 8
(2) (1) v (3)
10
40
8
5
6
300 16
4
60
2
7.5
0
450 24
Extension (b) (1)
(2) cN/tex%
cN/tex%2
(3)
cN/tex%3
The resulting deformation as a consequence of technological conditioned forces in a stitch formation process is not a linear function of the acting load. The deformation depends on the duration and intensity of loading. The results of research work on visco-elastic properties of threads show at dynamic loading very similar behaviour of two sewing threads having the similar rheological properties, such as module of elasticity E, initial module of elasticity E0, tension and extension in the flow point. The achieved cognition present a starting point for further research on the influence of rheological properties of threads on their dynamic loads during the use of garments, which
Figure 4. Visco-elastic properties of a thread with the code Po (a) before loading in sewing process; (b) after loading in sewing process
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will be investigated on the basis of known mechanical models and described with the help of rheological law. References 1. Gers˘ak, J., “Analiza obremenitve sukanca med procesom oblikovanja vboda”, Tekstil, Vol. 36 No. 9, 1987, pp. 481-9. 2. Gers˘ak, J. and Knez, B., “Untersuchungen über die Größe der Belastung des Nähfadens während des Stichbildungsprozeßes”, Bekleidung + Wäsche, Vol. 40 No. 16, 1988, pp. 37-41. 3. Gers˘ak, J. and Knez, B., “Reduction in thread strength as a cause of loading in sewing process”, International Journal of Clothing and Technology, Vol. 3 No. 4, 1991, pp. 6-12. 4. Gers˘ak, J., “Dinamic˘ko naprezanje konca kao posljedica technolos˘ki uvjetovanih sila u procesu oblikovanja uboda”, Tekstil, Vol. 40 No. 5, 1991, pp. 213-22. 5. Gers˘ak, J. and Knez, B., “Technic˘ko-technolos˘ki parametri konca i njihov utjecaj na c˘vrstoc´u odjevnih s˘avova”, Tekstil, Vol. 41 No. 5, 1992, pp. 211-8. 6. Gruber, E., Polymerchemie, Dr. Dietrich Steinkopff Verlag, Darmstadt, 1980. 7. Bobeth, W., Berger, W., Faulstich, H., Fischer, P., Heger, A., Jacobasch, H.-J., Mally, A. and Mikut, I., Textile Faserstoffe – Beschaffenheit und Eigenschaften, Springer-Verlag, Berlin Heidelberg, New York, NY, 1993. 8. Gers˘ak, J., “Verbesserung der Nahtfestigkeit durch Optimierung der Nähparameter”, Dresdner Textiltagung, Vol. 1, Dresden, 1992, pp. 783-94. 9. ISO 4916, “Textiles – seam type – classification and terminology”, 1982.
Computer-aided processes in garment production Features of CAD/CAM hardware Zoran Stjepanovic˘
Computer-aided processes
81
Faculty of Mechanical Engineering, Institute for Textile and Garment Manufacture Processes, University of Maribor, Maribor, Slovenia Clothing industry and new technologies Introduction of computer-aided processes and appropriate information systems to support the area of technological preparation of production, started in the clothing industry in the mid-1970s. This was a logical result of rapid development in computer technology and is becoming both a matter of urgency and a decisive factor in the clothing producer’s success. The use of modern and capable computer hardware and software can assure competitive advantages, such as high and constant quality of garments, productivity, flexibility and quick response to the requirements of the fashion market. Computer equipment is widely used for design and production of garments as well as for the assurance of effective information flows. The producers of such computer equipment, such as graphic workstations, have successfully adopted the characteristics of the engineering area of clothing technology. By introducing the new technologies into the process of garment production, we can achieve a substantial increase in productivity and quality of work. Consequently, the clothing industry is being transformed from a traditional, labour-intensive industry, into a highly automated and computer-aided industry. Garment-production processes require, above all, the development and application of the following computer-aided technologies: ●
CAD – computer-aided design;
●
CAM – computer-aided manufacturing;
●
CAPP – computer-aided process planning;
●
CAQC – computer-aided quality control;
●
CAT – computer-aided testing;
●
NC – numerical control;
●
MRP – manufacturing resources planning.
The above-mentioned computer technologies have been successfully supplemented by some methods of artificial intelligence (AI), mainly expert
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systems (ES), solid modelling and feature-based design (FBD)[1]. These new technologies are presently in intensive development and growth. They can be evaluated as very promising and are a potential enrichment of conventional CAD/CAM technologies that already support production of garments. The installed computer-aided modules and appropriate equipment are linked into the concept of computer integrated manufacturing (CIM). The basic principle of a CIM concept is the development of directions and techniques of integration of all processes and procedures in technical, marketing, and production areas[2]. This integration should be based on use of computer and communication equipment. The concept should not be understood as the automation and integration at any cost which is confirmed by the development of a new access to low-cost modernization and automation of production using the highest grade of own resources – the lean production[3]. New achievements in hardware development In approximately 40 years, the production of digital computers has grown from a few experimental machines to an international industry that consists of one of the strongest industry sectors. The computer and information systems industry is rapidly growing and gaining in importance[4]. Generally, the CAD/CAM systems supporting the garment manufacture processes, consist of the following elements: ● computer hardware; ● computer software; ● communication equipment. The configuration of a specific CAD/CAM system is being dictated by purpose and functions of a computer software which satisfies the user’s needs. The development of a new generation of complex and powerful software packages, places more complex requirements from computer hardware[5]. For some purposes, for example, computer-aided process planning, a microcomputer without special graphic devices would satisfy the needs of a small, or even medium-sized company. However, more powerful graphic workstations and special input/output devices are needed for computer-aided design in construction of garments. CAD/CAM systems for technical preparation of production and cutting In the clothing and textile industry, the majority of software has been written for marker making[6]. This task is well defined: the aim is to lay down pattern pieces on cloth, minimizing the amount of waste material. Here, the input and output methods are quickly identified: the user digitizes existing pattern shapes which are manipulated on the system. Then the marker is either plotted on a flat-bed or drum plotter, to produce a paper marker, or directly transferred to the NC cutting machine. Moreover, the other functions and processes in the
production of garments have been automated by computer systems using the specific and complex software packages. The computer hardware of a new generation of CAD/CAM systems for designing the models and collections, patterns, marking, laying out and cutting in production of garments, shown in Figure 1, basically consists of the following components: ●
CAD/CAM database server;
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powerful microcomputers for particular workstations;
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high resolution colour graphic displays;
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input devices;
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output devices;
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communication devices;
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special equipment for laying out and cutting.
Digitizer
Printer
Scanner
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Mainframe computer
Video camera Plotter
Grading, marking
Pattern construction
Design
Plotter
Optimization of spreading and cutting process
CAD/CAM data server
Common database
Computeraided process planning
Automatic laying-out machine
Automatic cutting machine
Cost planning and calculation
Figure 1. Components of CAD/CAM systems
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The most important part of such integrated CAD/CAM systems, is a so-called CAD/CAM database server. Generally, it is a powerful 32- or 64-bit super microcomputer with a processor performing up to 100 million instructions per second (MIPS). Graphic workstations for designing the models and collections, patterns and marking, are linked with the CAD/CAM data server through the local area network (LAN) or other types of computer networking systems. This enables the use of a common database of production data, stored in external data storage devices. Fast magnetic or optical disk units are commonly used for that purpose. Different interactive input and output devices enabling the implementation of software functions are connected to graphic workstations with the help of interfaces and specific graphic devices. The CAD/CAM data server is also linked with the mainframe computer at the managing level. Thus, an information exchange is enabled between the technical and management levels within the factory. Computer as the element of CAD/CAM systems The out-of-date 16-bit minicomputers and older generation of 16- and 32-bit microcomputers which present the main part of previous graphic workstations, have now been replaced by more powerful microcomputers. The modern microcomputers enable faster and more reliable operation of the entire graphic system. The architecture of hardware is fully subordinated to the efficient execution of a software function. The majority of producers of CAD/CAM systems for the clothing industry offer several levels of computer hardware. Smaller and cheaper systems usually use a 32-bit IBM-compatible microcomputer. Such systems are more suitable for smaller companies which would like to modernize their production and are not able to invest into more efficient equipment which is more expensive. The central parts of new generation powerful CAD/CAM systems are 64-bit super microcomputers, such as the ALPHA AXP family of Digital Equipment Corporation, and IBM 9404. The basic characteristics of both are: ● 64-bit architecture with the system clock speed of up to 150MHz; ● working (main memory; RAM) up to 80MB; ● effective data bus design; ● magnetic or optical disk units up to 16GB. Open and compatible hardware system architecture is becoming a more important factor in the efficiency of CAD/CAM systems. Thus, the majority of computer and information systems producers for automation of the clothing processes, use standard and acknowledged computer networks and data protocols. However, some of them have designed their own concepts, such as the network application support (NAS)[7], programmed by the German software producer, Eurolog. This network support software is offered as a standard part of CAD/CAM workstations and graphic systems. The open system architecture enables the networking of different levels of industrial standardized computer
hardware starting from personal computers and workstations up to terminal servers and mainframes. During the practical realization of local or widened area computer networks, difficulties, caused by incompatibility of computer equipment, can occur. This was the reason that the development of a special network software which supports all the varieties of CAD/CAM systems for automated clothing processing has become one of the main objectives and a factor of success of such systems. The network software has to ensure the communication between different computer systems which use various operating systems such as MS-DOS, OS-2, Windows NT and Unix[7]. Interactive input devices Besides the keyboard and mouse, the most common input devices of CAD systems for the technical preparation area of production, would be: ● digitizer; ● graphic tablet; ● video camera; ● digital scanner; ● spectrophotometer (for some special design functions). The graphic applications of CAD workstations for textile and garment design, require the reliable presentation of a product. Frequently, the task of a designer is to use a specific fabric sample for a garment. The fabric pattern can be input into the computer and presented on the screen using a video camera or digital scanner through appropriate graphic software. The pattern can be changed or modified accordingly. The typical resolution of a scanner is 300 dots per inch (DPI). The resolution of a video camera could be even higher and depends on its quality and optical system. The high resolution colour graphics display, combined with the keyboard, mouse and/or graphic tablet, presents the basic input/output device of a CAD system to be used in the textile and garment industry. The basic features of new generation graphic displays are: ● display size: 19 or 20 inches; ● graphic resolution: 1,280 × 1,024 dots; ● colour palette: up to 16,777,216 colours; ● low level of high-frequency radiation. Computer-controlled output devices Graphics output devices, most frequently used in referred CAD/CAM systems are[7]: ● colour hardcopy devices; ● colour video and photographic cameras; ● different types of printers;
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different types of plotters; ● automated spreading and labelling machines; ● automated cutting machines. These systems most frequently use pen or ink-jet flat-bed plotters up to 240cm wide. The drawing resolution of such plotters is up to 0.05mm[7]. The characteristics of NC cutting machines are also of great importance. The cutting width is up to 235cm and the height of the material layers is up to 7.5cm. The length of a cutting table depends on a number of table modules. The modern cutting machines use built-in intelligent systems for pattern matching and optimization of the movement of a cutting head. ●
Criteria for selection of suitable computer hardware and software Extensive analyses, performed by producers of CAD/CAM systems for automation of clothing manufacture processes, as well as by some independent institutions, have shown the justification of the introduction of CAD/CAM systems into the production process. Unfortunately, it is still connected with rather high costs of purchase and maintenance of computer hardware and software and other related equipment. However, the investment will be justified, if installed CAD/CAM systems perform their functions effectively. Some producers offer integrated CAD/CAM systems for use in garment production. The investment into complete automation from design up to automated cutting would be very high, therefore, only the largest and most successful companies could afford such expenses at once. Smaller and middlesized companies should carefully analyse the potential cost savings the computer support would bring. For such companies interested in grading and marking, only the choice of an appropriate system could be heavily influenced by price, as all grading and marking systems are fairly comparable. Therefore, concrete and objective evaluation of each system offered will be needed to choose the right one. The priority areas of modernization and automation of production with CAD/CAM systems should be defined within the framework of such analyses. The CAD/CAM program packages are, in most cases, of modular structure, therefore the progressive integration of computer-aided processes is possible. When deciding about the selection of supplier of CAD/CAM systems, it is very important to define the priorities and contents of selection criteria[1,6]. If one of the criteria, for example the price, is overestimated; and another criterion, for example, compatibility with other computer systems underestimated, unexpected difficulties could occur during the installation and integration of the system in the production environment. Basically, the following criteria should be considered: ● adaptation to the area of application; ● newest technology and quality of hardware; ● quality of software;
updating of software; ● stability and robustness; ● compatibility with other computer systems and CAD/CAM modules; ● references of computer hardware and software producers; ● price. The systems should be evaluated by considering the criteria and two or three systems should be selected for more detailed investigation. Prior to the final decision, the answers to the following questions should be considered: ● In what degree could the system be adopted to the actual production conditions and requirements? ● How reliable is the computer hardware and software? ● Are spare parts and a good service guaranteed? ● Who is performing the service? ● Which responding times are to be expected? ● How many steps are needed for execution of a certain program function? ● How fast is the response of the system? ● What are the integration possibilities? ● Is the system compatible with the existing computer equipment? ● Does the supplier’s price include installation, initial training and network support? ● Is the computer hardware up to date? ● How long will the planned configuration satisfy the requirements without the additional investments? ● How could the system be expanded and what are its limitations? The best way of finding answers to these questions is to test the selected systems with certain tasks and actual examples from production. During testing, the behaviour of system’s components and responding times of program functions have to be observed and documented carefully. ●
Conclusions The necessity of reacting quickly to changes in fashion trends and meeting ever-growing competition made garment producers apply CAD/CAM systems in their production processes. Therefore, the modern garment industry uses different computer and information systems to support the production processes and to make them more efficient. In integrated CAD/CAM systems, the CAD functions are supplemented by some CAM functions, for example automated laying out of material and automated cutting. The decision to introduce CAD/CAM systems into production of garments is based on the understanding that computers and information systems could be used both to
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speed up and improve the quality of information flowing through an organization. Furthermore, it has been recognized that if the total compatibility between the CAD/CAM systems is to be achieved, it would be necessary to install compatible hardware and software components. Specific information systems for automated clothing processing are arrangements of independent human and machine components and procedures which interact to support the production of garments with a higher grade of efficiency and quality. Usually, such systems do not come pre-packaged like some software application programs, often they have to be customized. The CAD/CAM data servers and graphics workstations use powerful 32- or 64-bit super microcomputers with clock speeds up to 150MHz and speed of processing of software instructions up to 80MIPS. The working memory (RAM) has reached 80MB and the capacity of magnetic or optical storage devices amounting to several GB. The capacity of back-up copy storage devices – for example magnetic tape units – has also been increased up to several hundred MBs. The speed, accuracy and reliability of interactive input and output devices are being improved too, although not so revolutionary as by microcomputers. The definition of priorities and contents of selection criteria is very important when choosing the suitable CAD/CAM system’s computer hardware and software for supporting the technical preparation of production and cutting in garment production. The lowest price should not be the most decisive criterion. More important is the reliability and stability of a CAD/CAM system and its components, the possibility of adapting to the actual production conditions and requirements, as well as compatibility with other computer equipment. Note and references 1. Jezernik, A., C˘ep, J., Dols˘ak, B., Golob, B., Hren, G., Stjepanovic˘, Z., Ulaga, S. and Ulbin, M., Computers in Construction and Production Processes (in Slovene), Faculty of Technical Sciences, Maribor, 1992. 2. Stjepanovic˘ , Z., “IMB ’91 – trends in development of computer integrated garment manufacture factories of the future” (in Slovene), Tekstilec, Ljubljana, Vol. 34 Nos 7-8, 1991, pp. 271-81. 3. Gers˘ ak, J., “World fair IMB ’93 – an impulse for the branch” (in Slovene), Tekstilec, Ljubljana, Vol. 36 Nos 11-12, 1993, pp. 397-404. 4. Jezernik, A., Golob, B., C˘ep, J. and Dols˘ak, B., Computer Science (in Slovene), Faculty of Technical Sciences, Maribor, 1991. 5. Hutchinson, S.E. and Sawyer, S.C., Computer and Information Systems, Irwin Advantage Series for Computer Education, 1994-1995 ed., Irwin, Boston, MA, 1994-1995. 6. Aldrich, W., CAD in Clothing and Textiles, BSP Professional Books, Oxford, 1992. 7. Documentation of textile and garment manufacture CAD/CAM system’s hardware and software and documentation on computers and common computer equipment.
Workplace design and loadings in the process of sewing garments Zdenka Bezjak
Workplace design and loadings 89
LABOD – Clothing Industry, Novo mesto, DELTA Ptuj, Slovenia, and
Blaz˘ Knez Textile-Technological Faculty, University of Zagreb, Zagreb, Croatia Introduction The analysis of working processes began with a technological analysis of the clothing production. This analysis revealed that a greater specialization of workers for each technological operation was needed. Technical progress enables the use of special sewing machines, sewing automates and aggregates. With growing technical progress, the experts established that more effective working is not possible with greater worker’s loading. For this reason, workplace design was concentrated on, including improvements in working methods, technological procedures, working articles, working conditions, workplaces, machines, tools and other equipment necessary in production[1]. Because workplace design must correspond with working method design in order to enable work with decreased worker’s loading, it is necessary to ascertain the worker’s loading. Workplace design in the sewing processes The system man-machine-environment, which includes just one workplace is understood to be a designed workplace[1]. The workplace is designed in the sense of economy and the ability of the worker in order to enable great quantity effect or shorter production time and additionally assures quality as well as safety. Knez[2] and Rogale[3] researched the design of the workplace in the clothing production process. They classified the influence factors in four groups: (1) technological factors; (2) technical factors; (3) ergonomic factors; (4) economic factors. The designing of the workplace can be accomplished within each of these groups, but the results are better if the workplace is designed with respect to all factors from all four groups. The factors of workplace design in the clothing production process are shown in Table I.
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 89-101. © MCB University Press, 0955-6222
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Technological factors of workplace design are dependent on the clothing part, which affects the work technological operation, the number and the sequence of the technological operations, number of workers, rhythm of the working group and the distribution of technological operations on workplaces[2,4]. The technological operations differ according to the type and design of the clothing part, the position of the seam in the clothing part, function of the seam in the sewing material, the sort and type of the sewing stitches, position of the sewing material in the sewing region, shape and length of the seam and mode and the execution of the sewing technological operation. In the technological process of cloth production, mostly technological operations are represented which combine machine, auxiliary machine, machine-manual, auxiliary machine-manual, manual, auxiliary manual and manual-covered technological procedures. The working method represents an important technological factor of workplace design with respect to the type and the structure of technological operation, which is the prescribed mode of the work-flow execution, with which the worker completes a decided sewing operation. With the appropriate working method, it is possible to obtain good results with relative small costs (in comparison with other factors). With respect to the ergonomic and technical factors of workplace design it is possible to gain faster production times and less worker fatigue[4], with a designed working method. With this aim, working methods with different kinds of sewing[5] were developed in the Fachhochschule Sigmaringen in Germany. The basic characteristics of the developed working methods are those material feedings which enable the sewing of a definite seam with no interruption or with minimal interruption of the sewing machine. Technical factors cover workplace design from the viewpoint of machine equipment. The ergonomic factors of workplace design are related to the requirements of the anthropometric measurements of workers’ bodies and their physiological and psychological loadings, which proceed from the work in the workplace.
Technological factors
Technical factors
Ergonomic factors
Economic factors
Type of clothing part
Types of sewing machines
Anthropometric body measurements
Workplace costs
Type of technological operation
Built-in system of the workplaces
Physiological loadings Efficiency of on workers the workplace organization
Work safety
Psychological loadings Efficiency of the on workers information flow on the workplace
Table I. The factors of Working methods workplace design in the clothing production process
Type of technological operation and workplace restrict workers, which result in non-symmetric work loadings. Working in the same workplace in the same position, for many years can result in damage to various parts of the body. Medical analysis shows that sedentary work, at the sewing machine, can damage shoulder joints and leg muscles. Damage caused by overloading the spine in the neck region is also quite common[6]. During the determination of workplace dimensions, it is necessary to consider average values of anthropometric measurements such as 5 per cent and 95 per cent with the standard deviation data[3]. Visible angles, which are dependent on eye capability, determine the maximal sight field, optimal sight fields and sight circles, which arise with the head turning and within which the workplace must be contained. Human work, in principle, consists of repetitive arm and leg movements, therefore, with respect to anthropometric requirements, normal and maximal arm reaches were designed (Figure 1). In the normal reach area, all cyclic technological procedures can be accomplished. Machines and auxiliary resources should also be in this area. Points from the area of maximal reach, which can just be reached with a stretched arm, are reasonable for periodic technological procedures. Profit and authorization are evaluated with economic factors of workspace design.
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Loading on the worker in the sewing process At the execution of the technological operation of clothing-part sewing, the worker is exposed to physical loads. These loads are the result of the different 15°
15° 30°
30°
55°
55°
400
600
100 15°
400 1,200 1,600
Figure 1. Ground view of the normal and maximal reach area with the horizontal optimal sight field
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body positions during work of the worker, for example workers are frequently exposed to loads because of the spine bending. The bending of the spine to the left, right and forward in the neck and kidney region and the rotation of the back of workers in the sewing process were researched by Mijovi´c et al.[7]. They studied the position of the spine of three workers during three different technological operations of the sewing on three differently designed workplaces. Recordings were made using photographs from the right side and from the back and top at the distance of 2.5m (Figure 2). Using the measurements, and with the use of mathematical formulae[7], the angles of the spine and the angles of the rotation of the back of the body are calculated (see Figure 3). Graphic representation of the results are shown in Figure 4. The angles ϕ1 show the bending of the spine in the neck region to the left and right. ϕ2 is the angle in the kidney region (Figure 4(a)). Angle ϕ3 shows the bending of the spine forward in the neck region, the angle ϕ4 is in the kidney region (Figure 4(b)). The angle γ shows the rotation of the back of the body (Figure 4(c)). The curves in Figure 4, marked “I”, represent the values at the beginning of the workday, the curves, marked “II”, show the values at the end of the work day. With the analysis and the comparison of the values of the angles, the loading of the workers in the sewing process is estimated. Loading on workers, as a result of different positions and attitudes, can be determined on the base of the modified OVACO Working Postures Analysing System (OWAS) method, developed for the analysis of body positions in steel production in Finland by OVACO (the association of Finland steel industry)[8]. The method was successful and therefore further developed and modified. Body positions at work are divided into four samples of thoracalumbal spine position, four samples of upper extremities position, three samples of arm position, seven lower extremities positions, two movement samples, five samples of head position and three samples of resistants (from 10N up). There are static and static-observed characteristics of body positions, which are marked throughout the workday. With respect to the marked data, the percentile part of the representation for each body position and attitude and also the time of each body position in the workday is calculated. These calculated data were then compared with represented body positions in the OWAS method (Figure 2), where the limited areas of allowed loading were assigned or the requirements for arrangements for loading minimization for the worker were realized. Experimental work Research was carried out on the technical operation of runstitching of collars in the production process of men’s shirts using different working methods. The times of execution of the technological operation of sewing were measured and calculated for workplace design and compared with the use of suitable working methods. The workers were monitored according to the OWAS method and representation of body positions was calculated for analysis of loading on the
Key: Arrangements are not required Arrangements must be done in feasible time Arrangements must be done now For the clearing up, detailed investigation is required
10 20 30 40 50 60 70 80 90 100
%
OWAS
Seg- Thoracalumbal spine Upper extremities Arms Lower extremities ment 1.1. 1.2. 1.3. 1.4. 2.1. 2.2. 2.3. 2.4. 3.1. 3.2. 3.3. 4.1. 4.2. 4.3. 4.4. 4.5. 4.6. 4.7. 4.8. 5.0.
Head 5.1. 30o
5.2. 30o
5.3. 30o
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45o
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5.7. 100 do 199
10 do 99
Forces (n) 5.6.
199
5.8.
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Figure 2. Positions of a worker at a sewing machine
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z
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Figure 3. Spine bending (a) to the left and to the right, (b) forward and (c) the rotation of the back of the body
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Figure 4. Representation of body position using OWAS method with need for arrangements
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workers. The technological operation of collar runstitching with two working methods (the existing and suggested) was studied. Used materials, sewing machine and the measurement system For sewing, a one-coloured textile in plain weave was used. The raw composition was 70 per cent cotton and 30 per cent polyester, 0.16mm thick and the weight per sq. metre was 80gm2. The density of threads was 35 threads in 10mm in the warp thread and 30 threads in the weft thread. The thread used for sewing was 100 per cent polyester thread, assigned 7.2tex × 2. For collar runstitching, a basic one-needle sewing machine – Pfaff 487-706/81 – with combined feeder was used. The sewing machine was equipped with a mechanical device for tightening at the beginning and at the end of the seam, and with the device for the thread cutting on the seam end. The nominal stitching velocity of the sewing machine was 4,440 stitches per minute. The sewing machine was equipped with a universal table, the altitude of which can be adjusted from 750mm to 890mm. The treadle can be adjusted 115mm forwards and backwards. To measure time used in the carrying out of collar runstitching, a measurement system developed in the Faculty of Mechanical Engineering in Maribor[9], was used. Workspace design for the execution of collar runstitching In the experimental part of the work, design of the workplace was accomplished. The workplace was designed for the implementation of collar runstitching. The workplace for the existent working method is shown in Figure 5. The sewing machine (1) is placed on the table (2). On this table, there is a pole for thread reels (3). Near the sewing machine there is a chair (4) on which the worker sits. The worker has lower parts (5), upper parts (6) and the runstitched collars (7) on the knees. The sign L on the collar marks the outer side of the textile, the sign H the inner side of the textile. The cross (x) marks the point where the workers grip that part of the collar. There are lower and upper parts of the collar and runstitched collars on the shelf for depositing (8). When the worker finishes runstitching the prepared collars, another worker takes them away to the next workplace and brings new lower and upper parts of collars. The greater part of the working area is in the normal reach of the arms (9). A smaller part is in the area of maximum reach (10). In the existent working method, the worker takes the lower part of the collar from the material with both hands and puts it on the table of the sewing machine. The worker then takes the upper part of the collar and puts it onto the lower part. With both hands, the worker positions the two parts under the pressure foot and switches on the sewing machine. With the pressure of the thumb of the right hand on the button, the worker switches on the tightener. The collar is fed through using the left hand. When the shorter side is finished, the collar is rotated with the left hand for 130°. The worker grips the collar and
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1,055
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Figure 5. Ground view of the workplace for the execution of collar runstitching using the existent method
5 8
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then sews the longer side for the first third of the length. Using both hands, the worker adjusts the hem. With the right hand the worker holds the collar tip and begins to sew. During sewing, the worker holds, with the left hand, about 10cm before the tip. The right hand is then withdrawn to sew the longer side of the collar. The worker then rotates the collar with the left hand for 130° and adjusts the hem with both hands the worker then sews the shorter side of the collar and feeds the collar with the left hand. With the right hand, the worker pushes the button to tighten and, using both hands, puts the collar on their knees. The base of the analysis of the existent accomplishment of collar runstitching the workplace was designed (Figure 6), with the aim to get the appropriate working method. The sewing table was increased (4) in width to 120mm and in length to 200mm. At a height of 100mm, an additional shelf for depositing the sewing material (5) was placed. Near the sewing machine, there is a chair (6) on which worker sits. On the sewing machine table, the lower (7) and upper (8) parts of the collar are arranged, as are runstitched collars (9). When the worker finishes runstitching the prepared collars, the finished parts are deposited on a transporter (10) and pushed to the next workplace. Most of the work is within the normal reach of the arms (10), a lesser part is in the area of maximum reach of the arms (11). Collar runstitching using the suggested working method, was done with the basic control mode[5]. In the same time, the worker grasps the lower and upper parts of the collar on the left side of the table of the sewing machine. When they are moved to the sewing machine, the worker adjusts the two parts on the hem.
Workplace design and loadings
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The adjustment under the pressure foot of the sewing machine is done with both hands, so the starting point for sewing from the hem of the collar is 5mm in the transverse direction and 10mm in the direction of the seam. The worker sets free both of the parts and, using both hands, adjusts the hem of the shorter side of the collar. The worker then takes the lower and upper part of the collar with the left hand and sews the shorter side. With the left hand the worker rotates the sewing material for 130°. With both hands the parts at the end of the longer side are adjusted. With the left hand the collar is stretched and with the right hand the collar is held in the middle and pulled towards the body. With the left hand, the worker pulls the collar forward and begins to sew using the basic feeding mode with two control points, and no interruption before the end of the longer side. After that the collar is turned through 130° using the left hand and the shorter side is sewn, tightening the seam. The left hand, with the help of the right hand, deposits the collar on the shelf (5). The efficiency of the designed workplace The determination of the efficiency of workplace design with the use of existent and suggested working methods is done with respect to the determination of basic technological time and auxiliary technological time for sewing. Thirty measurements of the time of the technological operation were done for existent and for suggested working method. The time measurements were done in a normal production line for men’s shirts in normal climate conditions. For the results the following were calculated:
Figure 6. Ground-view of the designed workplace of collar runstitching for the suggested working method
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technological time tt ; auxiliary machine-manual time tpar ; auxiliary manual time tpr ; time for execution of the technological operation top ; – average of measured times for execution the technological operation t .
Determination of loading on workers for executing sewing The loading on workers for executing sewing for existent and suggested working methods are determined with the modified OWAS method. To do this,
Technological time Number tt
Table II. Results of the existent working method for runstitching collars
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 – t
10.84 10.35 9.91 9.93 9.73 10.56 9.15 9.28 10.41 10.56 9.50 9.50 9.33 10.34 9.67 9.72 11.06 10.40 9.98 10.04 9.75 9.79 9.95 10.28 10.31 10.15 9.31 9.81 9.86 9.96 9.98
Type of time for execution of sewing/s Auxiliary machineAuxiliary manual manual time time tpar tpr 5.41 4.38 5.45 4.62 6.25 5.34 5.02 4.82 5.07 5.16 5.32 5.08 5.12 4.68 5.15 5.52 4.75 4.33 4.69 5.67 5.16 4.71 5.34 5.45 4.57 4.48 3.75 5.19 4.58 4.42 4.98
4.90 4.50 4.86 5.57 4.62 4.65 4.62 6.39 4.36 4.91 4.70 4.77 4.75 4.60 5.06 5.51 4.66 4.95 4.75 4.67 5.06 4.44 4.46 5.43 4.83 5.05 5.78 5.10 4.68 6.06 4.96
Execution time top 21.15 19.23 20.22 20.12 20.60 20.55 18.79 20.49 19.84 20.63 19.52 19.35 19.20 19.62 19.88 20.75 20.47 19.68 19.42 20.38 19.97 18.94 19.75 21.16 19.71 19.68 18.84 20.10 19.12 20.44 19.92
records of the positions and the attitudes of the worker for each minute in the workday were used. A total of 440 notes were made for each working method. On the base of the records, the percentile representation of single positions and attitudes of bodies in the work day was calculated. The calculated values are compared with the estimated picture with the OWAS method, from which the degree of loading and the need for minimization of them is seen.
Workplace design and loadings 99
Results The results of the measured and calculated times for the designed workplace for collar runstitching, with existent and suggested working methods are shown in Tables II and III.
Technological time Number tt 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 – t
9.31 8.69 9.11 7.68 8.55 8.16 7.93 8.06 8.40 8.82 8.69 8.80 7.99 8.74 8.38 8.66 8.17 8.80 8.02 8.95 8.08 9.08 8.50 8.71 8.43 8.77 8.82 8.56 8.55 8.77 8.54
Type of time for execution of sewing/s Auxiliary machineAuxiliary manual manual time time tpar tpr 3.84 2.93 3.88 2.83 2.91 3.08 2.97 2.81 2.91 2.40 2.85 2.67 3.61 3.16 2.38 2.97 2.79 3.17 3.74 2.58 2.71 3.04 3.83 3.09 2.64 3.15 3.52 3.11 2.70 2.87 3.04
3.87 4.54 4.60 5.27 5.59 4.61 5.19 4.64 4.35 4.49 4.25 5.57 4.36 4.35 4.88 4.56 4.84 4.24 4.33 4.24 5.54 5.52 5.03 4.33 4.56 5.50 5.16 4.89 4.89 5.24 4.78
Execution time top 17.02 16.16 17.57 15.78 17.05 15.85 16.09 15.51 15.66 15.71 15.79 17.04 15.96 16.25 15.64 16.19 15.80 16.21 16.09 15.77 16.33 17.64 17.36 16.13 15.63 17.42 17.50 16.56 16.14 16.88 16.36
Table III. Results for the suggested working method of collar runstitching
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Results of the positions and attitudes of workers using the modified OWAS method for the analysis for each method are shown in Table IV and the results of representation of single body positions and attitudes are shown in Table V. Discussion and conclusions With respect to the analysis of the existent method (Figure 5), and experimental designed workplace for collar runstitching with the corresponding method (Figure 6), it is seen that the changes in workplace design with the suggested method are due to the extension of the sewing machine table and by adding a shelf for depositing. Using the suggested method, better manipulation with the parts is possible. By using the data from Table II and Table III, it can be concluded that the suggested methods minimize work time for collar runstitching by 21.77 per cent. With respect to the results, which are shown in Table III and Table IV, the loading on the worker can be analysed for the execution of the operation on the designed workplace for existent and suggested working methods. The use of the suggested method enables minimized loading on workers during sewing. For the new method, the representation of the bending attitude of the back (1.2) is shown to be smaller, from 92.7 per cent to 0.5 per cent. The upright attitude of the back (1.1) has increased from 7 per cent to 99.3 per cent. Workplace design and the determination of loading during the process of sewing has a significant influence on productivity and workers’ state of health. Number of records of body position ThoraNumber columbal Upper Lower Working of spine extremities Hands extremities Head method records 1.1 1.2 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 4.1 4.2 5.1 5.2 5.3
Table IV. Results of worker with the modified OWAS method
Table V. Results for single worker position
Existing
440 31 408
Suggested 440 437
2
1
85
352
2
1
283
–
157 436
4
22
417
1
1
21
416
1
2
353
–
87
3
32
407
1
Working method
Work day body positions (per cent) Thoracolumbal Upper spine extremities Arms
Existing
1.1 1.2 1.4
Suggested
2.1
2.2 2.3
2.4
3.1
3.3
437
Lower extremities
Head
4.1 4.2 5.1
5.2 5.3
7.0 92.7 0.2
19.3 80.0 0.5
0.2
64.3 35.7
99.1 0.9 5.0
94.8 0.2
99.3 0.5 0.2
4.8 94.5 0.2
0.5
80.2 19.8
99.3 0.7 7.3
92.5 0.2
References 1. Verhovnik, V. and Polajnar, A., Designing of the Work and the Workplaces (in Slovene), University of Maribor, Faculty of Mechanical Engineering, Maribor, 1994. 2. Knez, B. and Rogale, D., “Designing of the workplaces in the clothing industry” (in Croatian), Proceedings of the Conference ITO I SITTH, Zagreb, 1995, p. 63. 3. Knez, B. and Rogale, D., “Designing of the workplace in the clothing industry with the use of anthropometric measurements, visual areas and reaching areas” (in Croatian), Proceedings of the Symposium ITO I SITTH, Zagreb, 1989, pp. 35-48. 4. Z˘ unic˘ Lojen, D. and Knez, B., “Influence of the sewing method on the structure of the technological operation of sewing” (in Slovene), Clothing Engineering ’92, Proceedings, Technical Faculty, Department of Mechanical Engineering, Institute for Textile and Garment Manufacturing Processes, Ljubljana, 1992, pp. 64-78. 5. Liekweg, D., Rademacher, K., Deseyve, A. and Hopf, H., “Optimale Nähmetoden für die Konfektion von Maschenwaren” (in German), Ausbildungsprogram, Technischer Beratungsdienst an der Fachhochschule Sigmaringen, Sigmaringen, 1983. 6. Blickle, M. and Holdenried, U., “Kriterien zur maßlichen Gestaltung von Näharbeitsplätzen” (in German), Bekleidung und Wäsche, Vol. 2, 1983, pp. 56-66. 7. Mijovi´c, B., Knez, B. and Ujevi´c, D., “Use of ergonomic principles for loading of workers in the process of sewings cloths” (in Croatian), Tekstil, Vol. 42 No. 8, 1993, pp. 439-45. 8. Sus˘nik, J., Position and Movements of the Human Body at Work, Analysis of the Effector System (in Slovene), No. 1, The University Institution for Health and Social Protection (UZZSV), Ljubljana, 1987, pp. 147-54. 9. Z˘ unic˘ , D., Gers˘ak, J. and Gotlih, K., “Model for determination of the structure of the technological operation and the preparations of the sewing material and the sewing” (in Slovene), Clothing engineering ’92, Proceedings, Technical faculty of Maribor, Department for Mechanical Engineering, Institute for Textile and Garment Manufacturing Processes, Ljubljana, pp. 22-9.
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Objective evaluation of a stabilized garment parts handle Jelka Gers˘ak and Andreja S˘aric˘ Faculty of Mechanical Engineering, Institute for Textile and Garment Manufacture Processes, University of Maribor, Maribor, Slovenia Introduction The products of the clothing industry are generally based on flat textiles that should have the same properties along the whole surface, although the required properties show different values at different locations on a garment. The required properties of textile surfaces on certain locations of a garment could be achieved on the basis of stabilization. For the purpose of garment parts’ stabilization, the fusible and non-fusible interlinings or other substances that can be fused on the surface of a cloth part can be used. The purpose of stabilization is the achievement of formability and appropriate flexibility, as well as the improvement of the look, fall and applicable properties of a produced garment. The right selection of interlinings considering the type of a flat textile surface, i.e. its mechanical and physical properties, fashion requirements and fusing technology, is very important from the point of view of the quality of a produced garment and a garment’s applicable properties.
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 102-110. © MCB University Press, 0955-6222
Importance of mechanical and physical properties of textile surfaces for the process of garment stabilization Stabilization of garment parts can be achieved by mechanical joining of nonfusible interlinings with the shell fabric or on the basis of front fusing. The technological operation of fusing is a heat and chemical process. During the front fusing, the shell fabric is being joined with the fusible interlining by heat and pressure. Considering the fact that this method presents the mainly used process of garment stabilization, the influence of mechanical and physical properties of fabrics on the quality of the fused panel, i.e. garment part, will be presented. Regarding the purpose of garment stabilization, the mechanical and physical properties of shell fabrics and interlining have a double function in a finished garment: ● they have to ensure the formability and appropriate elasticity of a produced garment as well as improve the appearance, “fall” and applicable properties of a garment;
●
they have to enable the compatibility between the dimensional properties of shell fabric and interlining during the fusing process as well as during the cleaning and maintenance of a garment.
To ensure the above-mentioned properties of stabilized garment parts, the following properties are important from the point of view of mechanical and physical properties of shell fabrics and interlining: tensile and elastic properties, bending, shearing and surface properties. Tensile and elastic properties of shell fabric, on the one hand, influences the properties of stabilized garment parts and, on the other hand, influences the compatibility with the interlining. The bending properties of shell fabric and interlining ensure the suitable appearance and the “fall” of stabilized garment parts. Besides the above-mentioned properties, also the shear stiffness is very important for the fusing process. Namely, it is not easy to keep the appropriate form of a cloth part during fusing if the shear stiffness is too low. Taking into account the stated facts, it is necessary to treat with special respect the required formability and properties of a produced garment, with regard to the mechanical and physical properties of a shell fabric and construction of the garment as well as the purpose and process of fusing. On the basis of knowledge of mechanical and physical properties of stabilized garment parts and their influence on required properties of stabilized garment parts, the suitable interlining can be chosen and the fusing conditions can be selected. The degree of compatibility between the dimensional properties of shell fabric and interlining in the fusing process, as well as during the maintenance of a garment, refers first of all to relaxation shrinkage and hygral extension of shell fabric and interlining in the area of fused garment parts. This means that the surface folding of stabilized garment parts can occur if the difference between the dimensional stability of shell fabric and interlining is too big. The folding could be minimized if the shell fabric has a possibility to adapt to shrinkage during fusing. The unsuitable selection of fusible interlining and fusing parameters will result in unsatisfactory quality of stabilized garment parts which will significantly influence the quality of a finished garment. Thus, directly after the fusing process, as well as later in process of dry cleaning and washing, the damage of garment parts can occur which reflects in[1-3]: ●
flattening of a shell fabric in the area of fused garment parts;
●
hardening of a shell fabric in the area of fused garment parts;
●
folding of fused garment parts or bubbling between the shell fabric and interlining or even separating of shell fabric and interlining;
●
appearance of streaks caused by irregular shrinkage of shell fabric and interlining during washing and dry cleaning;
●
dimensional changes of the fused panel.
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To avoid the above-stated difficulties, it is necessary to know the actual, for fabric handle, important mechanical and physical properties of shell fabrics, interlinings and fused panels. Only then are we in position to plan the required properties of fused garment parts and to ensure the suitable appearance and the “fall” of a finished garment.
104
Objective evaluation of stabilized cloth parts’ handle The fabric handle, which represents the psychological perception of fabric’s character, such as soft, plain, rigid, etc. is measurable and could be defined on the basis of fabric’s mechanical and physical properties. The characteristics of textile surface, which are determined visually or by subjective estimation of a handle, are important for evaluation of fused garment parts’ quality. However, the objective estimation of fused garment parts’ quality is not an easy task to do by hand evaluation. The estimation should answer the following question: is the flat textile surface good for wearing and what will be the processing properties? From the point of view of a consumer, the flat textile surface quality factor on the one hand, depends on fashion, aesthetic view and mechanical durability of a garment, and at the other hand on human’s sensibility, i.e. consumer’s comfort during the wearing of a garment. Two measuring systems for objective evaluation of fabric handle have been development in the past. The objective of these two systems was the avoidance of a subjective measurement of fabric handle. These two systems are Kawabata Evaluation System for Fabrics (KES-FB)[4] and Fabric Assurance by Simple Testing (FAST)[5]. The KES-FB measuring system is designed for objective evaluation of fabric handle[4], which is based on characterization of mechanical and physical properties of textile surfaces. These values are used to give the fabric’s total hand value on the basis of transforming equations, developed by Kawabata[4,6]. The KES-FB measuring system is composed of four components. Using these components, 15 physical properties of textile surfaces, shown in Table I, important for fabric handle, can be determined. The fabric weight is here presented as a sixteenth parameter. The results of the measurements of the fabric’s mechanical and physical properties are drawn as curves in the system of co-ordinates. The fabric’s characteristic parameters can be achieved on the basis of evaluation of gained curves[7]. Thus, during the tensile testing, where the resistance of the sample against the moving of the clamps is measured, the curve load F versus extension ε is evaluated. During the testing of shear, where the two-dimensional loading, resp. the shear deformation appears at +8° and –8°, the level of power Ks, needed to deform the sample, is measured. The result is shown in the form of hysteresis of the shear force Fs versus shear angle Φ. The bending rigidity is determined on the basis of evaluation of hysteresis of bending moment M in dependence to the bending movement K. During testing of the coefficient of friction, the measuring unit measures the change of electrical signal that appears because of
Measuring system
Parameter
Low stress properties
Notation
KES-FB1
Tensile
P1 – tensile energy P2 – tensile resilience P3 – linearity of load/extension curve P4 – shear rigidity P5 – hysteresis at 0.5° shear angle P6 – hysteresis at 5.0° shear angle
WT RT LT G 2HG 2HG5
Shear
KES-FB2
Bending
P7 – bending rigidity P8 – hysteresis of bending moment
B 2HB
KES-FB3
Compression
P9 – compressional energy P10 – compressional resilience P11 – linearity of compression/thickness curve P15 – fabric thickness P16 – fabric weight
WC RC LC T W
KES-FB4
Surface and structure of textile surface
P12 – coefficient of friction P13 – mean deviation of MIU P14 – geometrical roughness
MIU MMD SMD
105
Table I. List of physical properties, important for the fabric handle
the friction. The friction force R at the sample length x is drawn into the system – of co-ordinates. The change of sample thickness |d(x)–d |versus the sample length x is measured as a geometrical roughness. The total hand value (THV) of the textile surface, which exactly characterizes its properties and predicts the properties of finished garment are: 5 – excellent; 4 – good; 3 – average; 2 – fair; 1 – poor; and could be calculated using the expression (1)[6,7]: k
THV = c0 + ∑ zi
(1)
zi = ci 1 ( yi − M i 1 ) / σ i 1 + ci 1 ( yi2 − M i 2 ) / σ i 2
(2 )
i=1
where: c0
– empirical value
k
– number of determined primary hands
yi
– HV of the i-th primary hand
Mi1 – mean value y Mi2 – mean value y2
σi
– standard deviation.
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Experimental work Considering the above mentioned theoretical principles, the influence of mechanical and physical properties of shell fabric and interlining on quality resp. the handle of fused panel was studied. The research on objective evaluation of base cloth and fusible interlining handle has been carried out for this purpose. The measurements have been performed on base cloth and interlining before technological operation of fusing as well as on garment part after fusing. The fabric in a 1⁄2 twill weave “Z ” direction, produced from 85 per cent PES 15 per cent PA blend was used as a shell fabric. The thickness of this fabric was 0.36mm and the surface fabric weight was 176.5g/m2 at warp count of 440 yarns/10cm and weft count of 240 yarns/10cm. The pure cotton fabric in a plain weave with 25 mesh (112dots/cm 2 ) was used as fusible interlining. The thickness of interlining was 0.35mm and the surface fabric weight was 90.4g/m2 at warp count of 240 yarns/10cm and weft count of 160 yarns/10cm. The front fusing was performed on a continuous press machine. The fusing parameters were: ●
Fusing temperature T = 150°C;
●
Fusing time t = 15s;
●
Fusing pressure = 3.5N/cm2.
The measurements of 16 mechanical and physical parameters of shell fabric, interlining and fused cloth have been performed on the KES-FB measuring system. Results The achieved results on research of mechanical and physical properties of shell fabric, interlining and fused panel are shown in Table II and in Figures 1 and 2. Table II shows the average values of mechanical and physical properties of shell fabric, interlining and fused panel. The value of EMT refers to the maximal elongation at the boundary tensile force while EMC refers to the thickness of a testing sample at maximal compressibility. TO presents the thickness of a sample at pressure of 0.5p/m2. Figure 1 presents the bending hysteresis for the interlining, shell fabric and fused panel. The bending rigidity B and the hysteresis of bending movement 2HB are evaluated from the course of bending hysteresis. The hysteresis of shear deformation for shell fabric, interlining and fused panel is shown in Figure 2. Discussion From the analyses of the achieved results of research on the mechanical and physical properties of the shell fabric, interlining and fused panel, it could be seen that the garment part adopts the specific properties considering the base
Testing direction
Interlining Warp Weft
Shell fabric Warp Weft
Fused panel Warp Weft
4.65 8.55 65.71 140.49 55.6 38.74 0.492 0.67
3.13 53.20 53.0 0.694
Notation
Unit
EMT WT RT LT
Per cent mNcm/cm2 Per cent –
– – – –
3.38 47.07 49.5 0.568
5.65 82.13 43.6 0.592
G 2HG 2HG5
nN/cm grd mN/cm mN/cm
– – –
4.05 5.39 7.85
4.54 6.37 9.32
B 2HB
mNcm2/cm mNcm/cm
– –
0.276 0.257
0.16 0.134
TO TM WC RC EMC LC
mm mm mNcm/cm2 Per cent Per cent –
– – – – – –
MIU
–
Right Left
0.194 0.191
0.187 0.188
0.333 0.227
0.406 0.278
0.342 0.195
0.375 0.186
MMD
–
Right Left
0.024 0.036
0.018 0.037
0.016 0.008
0.021 0.019
0.012 0.018
0.011 0.022
SMD
µm
Right Left
6.26 8.69
3.98 0.858
1.414 1.81
3.255 8.48
1.231 2.651
0.838 0.338 2.54 42.47 59.67 0.207
11.38 18.43
10.05 23.29 45.60
10.91 27.95 47.81
1.042 0.502
0.564 0.552
0.55 0.40 1.43 39.38 27.27 0.389
4.75 84.09 45.18 0.722
32.79 58.84 111.55
27.58 57.00 104.81
8.89 4.87
6.50 5.79
Stabilized garment parts handle 107
1.118 0.675 3.118 41.12 39.62 0.287
fabric and interlining. These properties are the consequence of the interaction of shell fabric and interlining, or their behaviour, respectively. The results show the change in each of 16 mechanical and physical properties of a fused panel. Hereby, the tensile energy WT of a fused panel is being reduced by 19.04 per cent in warp direction and by 40.15 per cent in weft direction with respect to the shell fabric. The tensile resilience RT was reduced by 4.68 per cent in warp direction, while in weft direction, the RT was increased by 16.62 per cent. The values of shear stiffness and bending rigidity, which present the measure for intensity of stabilization, have been also changed. Both values have been increased with regard to the shell fabric. This is also a precondition for the assurance of a good “fall”, stability of garment’s form and improved applicable properties. The shear stiffness, which is a measure for yarn’s movability in a fabric, has been increased from 10.5mN/cm grd to 32.79mN/cm grd in warp direction and from 10.91mN/cm grd to 27.58mN/cm grd in weft direction with respect to the shell fabric. The bending rigidity has even been increased 8.53 times in warp direction and 11.52 times in weft direction.
Table II. Average values of mechanical and physical properties of the shell fabric, interlining and fused panel
M, gf.cm/cm
IJCST 7,2/3
0.2
Warp
0.1
Weft –3
–2
0
–1
1
2
3
K, cm –1
108 –0.1
–0.2
M, gf.cm/cm
(a)
–3
–2
0.2
Warp
0.1
Weft
0
–1
1
2 K, cm –1
3
–0.1
–0.2 (b)
0.4 Warp 0.3 M, gf.cm/cm
Weft
–3
–2
–1
0.2
0.1
0
1
2 K, cm –1
–0.1
–0.2
Figure 1. Bending hysteresis curve (a) interlining, (b) shell fabric, (c) fused panel
–0.3
–0.4
(c)
3
Fs, gf/cm
10
5
Weft
Stabilized garment parts handle
Warp –8
–6
–4
–2
0
2
4
6
8
ϕ, degree
109
–5
–10 (a)
15 Weft
Fs, gf/cm
10
–8
–6
–4
Warp 5
–2
0
2
4 ϕ, degree
6
8
–5
–10
–15 (b)
15
Fs, gf/cm
Weft 10
Warp 5
–8
–6
–4
–2
0
2
4 6 ϕ , degree
8
–5
–10
–15 (c)
Figure 2. Hysteresis of shear deformation (a) interlining, (b) shell fabric, (c) fused panel
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Furthermore, the interaction between the interlining and shell fabric is very interesting. This interaction is presented on the basis of the mechanical and physical properties of a fused panel. The results of research work show that the tensile resilience of fused panel RT has been increased by 7.07 per cent in warp direction and by 3.62 per cent in weft direction with regard to the tensile resilience of interlining. Further comparison of tensile resilience as well as other mechanical and physical properties of shell fabric and interlining with properties of fused panel shows that these properties depend on interaction of shell fabric and interlining, or their mechanical and physical properties, respectively. Conclusions It is not an easy task to predict the behaviour of a textile surface during the production process. The same conclusion can also be made for its behaviour in wearing and maintenance of a garment. The answer to a question what processing properties, essential for the production process will a certain textile surface have, depends largely on its mechanical and physical properties. The achieved results of research on the influence of mechanical and physical properties of shell fabric and interlining on properties of a fused panel confirm the above-stated fact. It can be seen from results that the mechanical and physical properties of a fused panel reflect the interaction between the shell fabric and fusable interlining, or their mechanical and physical properties, respectively. At the same time, this means that on the basis of the exact definition of mechanical and physical properties of shell fabric and fusible interlining, the properties of fused panel can be predicted. Thus, considering the known mechanical and physical properties of shell fabric and the required properties of a garment part, we can choose the appropriate interlining. References 1. Kaiser, A., “Requirements imposed on front bonding of clothing of high value from modern light base fabrics” (in Croatian), Tekstil, Vol. 41 No. 5, 1992, pp. 239-41. 2. Soljac˘i´c, I. and Pezelj, D., “Influence of the parameters of front bonding on the tenacity and resistance of the bond in dry cleaning and on the change of colour of the top cloth” (in Croatian), Tekstil, Vol. 42 No. 9, 1993, pp. 489-95. 3. Jevs˘nik, S. and Gers˘ak, J., “Influence of fusing parameters on quality of clothes parts” (in Slovene), Proceedings of the 2nd Symposium on Clothing Engineering ’94, Ljubljana, 8 June 1995, ITKP, Faculty of Technical Sciences Maribor, pp. 109-19. 4. Kawabata, S., “The development of the objective measurement of fabric handle”, in Kawabata, S., Postle, R. and Niwa, M. (Eds), Proceedings on Objective Specification of Fabric Quality, Mechanical Properties and Performance, Textile Machinery Society of Japan, Osaka, Japan, 1982, pp. 31-59. 5. Brandt, H., Fortess, F., Wiener, M. and Furniss, C., “The use of KES and FAST instruments in predicting processability of fabrics in sewing”, International Journal of Clothing Science and Technology, Vol. 2 No. 3/4, 1990, pp. 34-9. 6. Greuel, M., Weisse, F. and Zastrow, U., “Der Griff eines Gewebes – subjektive Beurteilung und objektive Messung”, Bekleidung und Wäsche, Vol. 43 No. 4, 1991, pp. 22-5. 7. Greuel, M., Weisse, F. and Zastrow, U., “Der griff eines Gewebes – subjektive Beurteilung und objektive Messung”, Bekleidung und Wäsche, Vol. 43 No. 5, 1991, pp. 36-45.
Influence of some parameters on stitching velocity of sewing
Stitching velocity of sewing
Darja Z˘unic˘ Lojen Faculty of Mechanical Engineering, Institute for Textile and Garment Manufacture Processes, University of Maribor, Maribor, Slovenia
111
Introduction Stitching velocity is one of the parameters in the technological operation of sewing which influence both total sewing time and quality of stitch form. There are some different parameters which influence the stitch velocity. These parameters are type of sewing operation, type and kind of sewing machine, stitch length, seam length, nominal stitching velocity of the sewing machine and structure of the sewing operation. Knowledge of the influence of these parameters is important for the favourable choice, selection and definition of them, with the aim to gain higher stitching velocities with demanded quality. The determination of the structure of the sewing operation, i.e. the determination of each technological procedure is the base for a different analysis of the sewing operation and for the determination of working methods. The working methods with prescribed feeding of material influence the course of the stitching velocities and on the real reachable stitching velocities. Influenced parameters on stitching velocity of sewing Sewing machines operate with growing velocities. Exploitation of them is dependent on the ability of the worker and on the mode of feeding material. The quality requirements must be considered. For each technological operation exists an optimal area of stitching velocities, at which the worker can normally feed the material, i.e. he/she can reach the required quality of seam. There are different, but connected, parameters which influence the stitching velocity. These parameters are conditioned by the kind of technological operation, sewing machine, workplace design, required quality, structure of the sewing operation and by the worker’s ability. The technological operations differ from one another according to the: ●
form and the length of the seam;
●
function of the seam and place where it appears on the clothing part;
●
number of layers of the sewing material;
●
length of the sewing stitch;
●
quality requirements;
●
working methods with material feeding modes.
International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 111-118. © MCB University Press, 0955-6222
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This represents important parameters and influence on the stitching velocity. For long seams with flat shapes, the stitching velocities are greater than for short seams or for seams with curved shapes[1-3]. Besides seam length, the function of the seam and the place of it on the clothing part must be considered. With respect to these two parameters, is the achieved stitching velocity for equal length seams. The stitching velocity is greater for shorter stitches than for longer stitches and results from material feeding. The influence of the sewing machine on stitching velocity is expressed through the type of sewing machine and its technical capabilities. The nominal stitching velocity of the sewing machine directly influences the course of stitching velocity. The type and technical capability of the sewing machine influences indirectly the working methods and structure of the sewing operation. Higher nominal stitching velocities produce normally higher sewing velocities[2]. It is also interesting to note how the sewing operation structure influences the stitching velocity. The structure of the technological operation is a function of the technological operation type, sewing machine type and the working method. It is evident that it influences the stitching velocity indirectly through material feeding and with determined lengths of the seam segments. The working method prescribes all worker activities, which are, for example: taking the clothing parts or layers of the material, preparing them, followed by the material feeding during the sewing process and depositing the material[4]. Appropriate material feeding in the sewing process influences higher stitching velocities through longer segment lengths of the seam. This means greater reached average stitching velocities and sewing velocity. All these facts influence the exploitation of the nominal stitching velocity[4], as given in the equation: n I n′ naz = .100 per cent (1) nnaz I n naz =
nnaj
.100 per cent
(2)
nnaz
where I 'n naz = degree of exploitation of the stitching velocity with respect to the average stitching velocity in per cent; I n naz = degree of exploitation of the stitching velocity with respect to the greatest stitching velocity in per cent; n¯ = average stitching velocity, stitches per minute; nnaj = the greatest stitching velocity, stitches per minute; nnaz = nominal stitching velocity in stitches per minute.
Method Three parameters which influence the stitching velocity have been researched for this article. These parameters are: the length of the shape of the seam, nominal stitching velocity and sewing operation structure. The influence of the length of the shape seam and nominal stitching velocity was researched by the sewing of two layers of material with a straight shape of seam. The research was done on a cotton fabric in plain weave. The fabric is assigned 01, with the weight per sq. metre 163gm–2 and the thickness 0.42mm. The measurements were accomplished on a basic sewing machine, a Brother DB2-B737-913 mark II with adjustable nominal stitching velocity. The average and maximum reached stitched velocities were measured, dependent on the sewing time. The lengths of the stitches were from 2-4mm and the length of the seam was 50-500mm. The nominal stitching velocity was from 1,000-4,464 stitches per minute. The influence of the technological operation structure on the stitching velocity was also investigated. This investigation was done on a technological operation of hem front sewing of a shirt, with the shape length 735mm and the stitch length 2.7mm. The sewing machine used was Necchi Bagat 885-264 with the nominal sewing velocity of 5,064 stitches per minute. The analysis of the technological operation structure influence on the stitching velocity was done with the existent working method respectively the material feeding and the suggested working method. The tolerances of the seam are ±1mm with respect to the seam shape. In the existing working method, the worker takes the front part from the left side of the working table. He/she bends the hem at the interlining and positions the part under the pressure foot of the sewing machine. After the positioning, he/she starts to sew. The seam is done in four segments (Figure 1). Between each segment, the worker adjusts the sewing material at the interlining. When sewing is finished, the sewing material is deposited on the right side, on the additional working table. The suggested working method for taking sewing material is similar to the existing method. Preparing the sewing material is different. The worker bends the hem at the interlining at the upper side of the brim and then positions it under the pressure foot of the sewing machine. Then the worker bends the hem on the bellowed part and takes it with the left hand. The worker bends the whole hem with the right hand and then seizes it mid-length. The sewing is then done in one segment (Figure 1) in the basic mode with two control points[5]. After sewing, the worker lays down the sewing material on the right-hand additional working table. The technological operation of hem front sewing of a shirt with the existent working method is combined with 11 technological procedures. The suggested working method is combined from five technological procedures. The technological procedures can be classified into three groups: the group of the auxiliary manual technological procedures; basic machine-manual
Stitching velocity of sewing 113
114
technological procedures; and the group of auxiliary machine-manual technological procedures. For the measurements of the stitching velocities and the time of the technological procedures a measurement system was used. This system is composed from hardware and software equipment as shown in Figure 2[6]: ● incremental encoder; ● measurement computer; ● personal computer; ● software SPROS.
1
1
A
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B
2
A
C
3
D
4
2
5
(a)
Figure 1. Number of segments for the technological operation – hem front sewing of a shirt
Figure 2. Measuring system for stitching velocities and times
(b)
Key: (a) — The existent working method (b) — The suggested working method
Incremental encoder
5V 0V
Measurement computer
RS 232
PC
Results The measurement results of the influence of seam length, nominal stitching velocity and the working method are shown in Figures 3-5. The results are shown on the base of measurements of course of the stitching velocities
Stitching velocity of sewing 115
5,000 Stitching velocity/stitches/min
Stitching velocity/stitches/min
5,000
4,000
3,000
2,000
1,000
4,000
3,000
2,000
1,000
1
2
5
t/s
(a)
t/s
(b)
Key: (a) – For the seam length 50mm (b) – For the seam length 500mm Note: The stitch length is 2mm and the nominal stitching velocity of the sewing machine is 4,464 stitches per minute
5,000 Stitching velocity/stitches/min
Stitching velocity/stitches/min
5,000
Figure 3. Course of the stitching velocity dependent on the seam length
4,000
3,000
2,000
1,000
4,000
3,000
2,000
1,000
5
t/s
(a)
Key: (a) – 2,520 stitches per minute (b) – 4,032 stitches per minute Note: The stitch length is 2mm and the seam length 400mm
5 (b)
t/s
Figure 4. Course of the stitching velocity dependent on the nominal stitching velocity of the sewing machine
116
Stitching velocity/stitches/min
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4,000
3,000
2,000
1,000
5
10
15
20
25
t/s 27.93
Stitching velocity/stitches/min
(a)
Figure 5. Course of the stitching velocity dependent on the working time for hem front sewing of a shirt
4,000
3,000
2,000
1,000
5
10
Key:
15
t/s 19.24
(b)
(a) – For existent working method (b) – For suggested working method
dependent on the time of the technological operation. The average and maximum calculated stitching velocities are shown in Table I. The courses of the stitching velocities dependent on the nominal stitching velocity are shown in Figure 4. The course of the stitching velocity for the analysed technological operation of hem front sewing of a shirt with the existent working method, where the seam is partitioned into four segments and the suggested working method, where the seam was done in one segment is shown in Figure 5.
Table I. Results of the maximum and the average calculated values
Degree of exploitation of the nominal stitching velocity (per cent) I'n Innaz naz
Working method
Stitching velocity stitches per minute n¯ nnaj
Existent method
1,190.94
1,772
23.52
34.99
Suggested method
2,140.63
2,724
42.27
53.79
The measured values and calculated average stitching velocities and the degree of exploitation of the nominal stitching velocity of the sewing machine for the analysed technological operation for the existent and suggested working method are given in Table I. Discussion From the analysis of the stitching velocities it can be seen how the parameters influence the stitching velocity. Long seams achieve greater stitching velocities than short seams (Figure 3). The changes of stitching velocities depend on the seam length and are expressed for higher nominal stitching velocities of the sewing machine. From this, the influence of the nominal stitching velocity of the sewing machine on the stitching velocity is seen. Higher nominal stitching velocities produce, in principle, higher stitching velocities than slower nominal velocities, (Figure 4). The structure of the technological operation through the working method also influences the stitching velocity. With respect to the analysed working methods respectively, through material feeding, different structures of the technological operation are determined. Minimization of the sewing procedures from four, for the existent working method, to one, for the suggested working method means that the lengths of the segments for the existent working method are shorter than for the suggested working method, where the worker sews the seam in one segment with no interruptions (Figure 1). The course difference of the stitching velocity between the existent and suggested working method is shown in Figure 5. For the existent working method, it is evident that most of the time is used for the acceleration or deceleration and just a little part of the time for the sewing at the maximum stitching velocity. From the course of the stitching velocity for the suggested method it is evident that the maximum reached stitching velocity is higher than the existent working method. The part of the time for sewing with the maximum stitching velocity is also longer than for the existent working method. The maximum reached stitching velocity for example of sewing the hem front of a shirt at the existent working method, is 1,772 stitches per minute. At the suggested method, the maximum stitching velocity is 2,724 stitches per minute or 53.72 per cent greater. The comparison of the average of the stitching velocity shows 79.74 per cent increasing. The average stitching velocity for the existent method is 1,190.94 stitches per minute and for the suggested method 2,140.64 stitches per minute (Table I). With respect to the course of the stitching velocity dependence on time of the technological procedure (Figure 5) and the calculated greatest and average stitching velocities, it can be concluded, that the course of the stitching velocity for the suggested working method has advantages in comparison with the existent working method.
Stitching velocity of sewing 117
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Conclusions The influence of some parameters are given in the work. The results have confirmed that the seam length and the nominal stitching velocity of the sewing machine are important for the course of the stitching velocity. The influence of the structure of the technological operation through the working methods and material feeding on the stitching velocity is shown. From the analysis of the course of the stitching velocity results of the maximal and the calculated average stitching velocity for a technological operation – sewing a hem front of a shirt – the suggested working method, for material feeding, the maximum stitching velocities are greater for 53.72 per cent than for the existent working method. The average stitching velocities are 79.74 per cent greater. We can conclude, that the designed working methods through longer seam segments enable higher stitching velocities. References 1. Knez, B., Rogale, D. and Dragc˘ evic, Z., “Influence of the structure of the machine time on the degree of exploitation the sewing machines” (in Croatian), Tekstil, Vol. 34 No. 8, 1985, pp. 551-7. 2. Krowatschek, F. and Ludemann, P. (Eds), “Der nähvorgang als kybernetisches system” (in German), Bekleidungstechnische Schriftenreihe, Band 5, Eigenverlag der Forschungsgemeinschaft Bekleidungsindustrie eV, Berlin, 1994. 3. Krankenhagen, H.-J., “Anpasung von schnellnähern an das leistungsvermögen der näherin” (in German), Bekleidungstechnische Schriftenreihe, Band 10, Eigenverlag der Forschungsgemeinschaft Bekleidungsindustrie eV, Cologne, 1975. 4. Z˘unic˘ Lojen, D., “Influence of the structure of the technological operation on the stitching velocity” (in Slovene), Proceedings of the 2nd Symposium On Clothing Engineering ’94, Technical Faculty Maribor, ITKP, Ljubljana, 8 June 1995, pp. 82-91. 5. Deseyve, A., Hopf, H., Liekweg, D. and Schleicher, H., “Optimale nähmethoden” (in German), Bekleidungstechnische Schriftenreihe, Band 30, Eigenverlag der Forschungsgemeinschaft Bekleidungsindustrie eV, Cologne, 1981. 6. Z˘unic˘ Lojen, D. and Gers˘ak, J., Erfasung der technologischen Vorgangszeit als Element des Informationssystems (in German), Aachner Tekstiltagung, Deutsches Wollforschungsinstitut, Aachen, 1994, pp. 531-6.
Resistance to creasing of clothing wool fabrics
Resistance to creasing
T.V. Mihailovi´c, M.D. Nikoli´c and Lj.M. Simovi´c University of Belgrade, Belgrade, Serbia Introduction Studying the behaviour of fabrics during their exploitation is becoming of specific importance where clothing fabrics are concerned[1,2]. Clothing fabrics which are not exposed to conditions of mechanical forces nevertheless require studying under the influence of such mechanical actions, not only because of their durability, but also because of their dimension stability[3,4], which is necessary to fit certain criteria. At present, a series of mechanical characteristics, of aesthetic properties[5,6], such as pilling resistance, dimensional stability, drape and elasticity are considered, because they contribute to a perfect appearance of fabric as well as to its reliability. The combined effect of bending and pressure, which leads to a creasing of material, is one of the most common mechanical actions on clothing fabrics. This leads to the surface of the fabric as well as the general aesthetic impression appearing disturbed. Crease resistance, which depends on elastic properties, can be observed through measuring the crease recovery angle (modified standard method)[7] and determining the quality number. Such measurements can be useful when comparing fabrics. More information about the reaction of fabrics on combined effect of bending force and pressure can be gained by the addition of a standard method of determination of the crease recovery angle[7] with a greater number of measuring intervals, with the aim of establishing the fabric relaxation velocity after the cessation of force action. Experiment Nine commercially-produced all-wool fabrics in three variants of weaves were used as experimental material. Fabrics were made from yarn fineness in measurements of 27.5 × 2 tex to 21.5 × 2 tex and weave density in limits from 160-184 threads per dm. Basic characteristics of investigated fabrics are given in Figure 1. The method, which consists of measuring the angle (α) which appears with the straightening of unloaded fabric, used for determining the crease resistance of fabrics (see Figure 2), represents the modified standard method[7]. Investigated samples in dimensions of 20 × 50mm are folded through the narrow side at 180º (see Figure 3). The length of the folded part, for fabrics whose mass is within the limits of 100-500g/m2, is 10mm. A load of 9.81N is applied on a folding part of fabric in a period of 60 minutes. After the removal
9 Received October 1995 Revised and accepted March 1995
International Journal of Clothing Science and Technology, Vol. 7 No. 4, 1995, pp. 9-16. © MCB University Press, 0955-6222
IJCST 7,4
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Sample
To, tex
Fabric structure
Go, dm–1
Gp, dm–1
Uo, %
Up %
M, g/m2
1
23 x 2 24.5 x 2
183
179
4.92
4.47
183
2
23 x 2
24 x 2
176
172
6.46
5.59
175
3
27 x 2
23 x 2
170
184
4.59
8.48
193
4
24.5 x 2
23 x 2
171
181
5.67
5.42
180
5
23 x 2 27.5 x 2
170
170
5.79
6.22
170
6
27 x 2 23.5 x 2
165
181
5.88
9.54
194
7
23.5 x 2 24.5 x 2
180
175
5.87
6.73
180
8
21.5 x 2
23 x 2
171
160
5.96
7.94
169
23 x 2 23.5 x 2
171
172
5.81
5.90
179
9
Figure 1. Basic characteristics of investigated fabrics
Tp, tex
Key: To, Tp = warp, weft fineness (tex) go, gp = warp ends, weft picks up (dm) uo, up = warp, weft crim, (%) M = fabric weight (g/m2)
α
Figure 2. Crease recovery angle
180o
Figure 3. Determination of crease recovery angle
α = 0o
of the load, angle (α) is measured in degrees after 5 minutes (α 5) and after 60 minutes (α 60). If the angle is bigger, the fabric has less tendency to crease. The angle appearing immediately after unloading the investigated sample, angle of leap (α 0 ), which is hard to measure precisely, is calculated according to the formula: log α 0 = log α 60 − 3.5 • log
α 60 α5
(1)
Modification of the described method of determining the crease resistance of fabrics by introducing the measurements of crease recovery angle ( α) in a period of time of 5, 10, 15, 30, 45, 60 and 1,440 minutes (24 hours ) determines
the elastic, viscoelastic and component as well as the investigation of the velocity after the cessation of bending force and pressure. Results and discussion The results of measuring the crease recovery angle of fabrics in degrees, maximal, minimal, and the mean value of angles in definite time intervals are shown in Figures 4 and 5. On the basis of minimal and maximal value of crease recovery angle (separated parts of pie in Figures 4 and 5), the zone of crease recovery angle values is obtained in degrees, as well as the mean value of zone (dashed line) on the basis of mean value of crease recovery angles of all investigated fabrics for the observed relaxation time (central part in Figures 4 and 5). Figures 4 and 5 (central parts) show that the zone of crease recovery angle values of fabrics becomes narrower with increasing the relaxation time in warp direction as well as in the weft direction. The results of the investigation shown in Figures 4 and 5 enable the determination of the recovery angle size for any time inside monitored intervals from 0-24 hours (for example, 1 minute or 500 minutes) on the basis of limit values of recovery angles as well as the average value of interval. On the basis of crease recovery angle values in definite time intervals, elastic, viscoelastic and plastic deformation component of investigated fabrics are calculated (see Figure 6). The size of elastic deformation was determined on the basis of calculated values of angle of leap (α 0 ) within a moment of unloading the sample (instantaneous recovery). The size of plastic deformation was determined on the basis of measuring the value of crease recovery angle after 24 hours. The size between elastic and plastic deformation was estimated as viscoelastic deformation. On the basis of values shown on Figure 6 it can be stated that: ● Values of elastic deformation component for investigated fabrics are in limits of 27-73 per cent, with maximal frequency in interval 37-57 per cent (shaded part). ● Values of viscoelastic deformation component are in limits of 13.5-57 per cent, with maximal frequency in interval 23-47 per cent (shaded part). ● Values of plastic deformation component are in limits of 9-31.5 per cent. The calculated values of deformation components, which are different for each of the investigated fabrics and whose common zone is separated in Figure 6, highlight the assumption of different resistance to creasing. However, Figure 7 shows that no matter what the individual values of deformation components (elastic, viscoelastic, plastic) or the different constructive parameters of investigated fabrics, the relaxation velocity of all fabrics after 100 minutes is approximately the same. On the basis of measuring results of crease recovery angle in additional time intervals (10, 15, 30, 45 and 1,440 minutes), the relaxation velocity of fabrics was
Resistance to creasing
11
Crease recovery angle (o)
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180 [8] 160 [4] 140
12
[6]
[2]
120 100
[5]
[1]
[7]
[3]
80 60 40 0.0
0.1
(max) 110
71 81.5
75 86 77
75 (min) 55
1 10 100 Relaxation time (minutes)
117
145 114
145 110
100 [3] 10 minutes mean value: 115.5
109
(min) 102 110
107 119
140
107 120
140
117
(max) 152
148 118 [5] 30 minutes mean value: 123.3
(min) 107 110
148
Figure 4. The zone of crease recovery angle values of investigated fabrics in warp direction
118
(max) 148
(min) 100 [4] 15 minutes mean value: 120
(min) 95
107 125
140 (max) 162 [7] 60 minutes mean value: 126.5
118 122
99 (min) 96 106 118
(max) 147
[2] 5 minutes mean value: 110.2
102
136
108
135 107
[1] 0 minutes mean value: 78.5
(max) 148
97 97 99
(max) 136
1,000
(min) 105 110
148
107 120 118 122
[6] 45 minutes mean value: 124.7
158 144 (max) 164 162 [8] 1,440 minutes mean value: 147
(min) 143 147 (min) 123 138 145
Resistance to creasing
Crease recovery angle (o) 180 [8]
160 [4]
[2]
[6]
13
140 120 100 [1] [3]
80
[5] [7]
60 40 0.0
0.1
(max) 131 (min) 49 90 75
68 90 108.5 77
1 10 100 Relaxation time (minutes)
96 (max) 141 102
102 140
115 108
110
[1] 0 minutes mean value: 85.2
100 (max) 145
(min) 87
100 (max) 145
(min) 96
107
135
107
123 118
142 110
127 124
143 125
103
[5] 30 minutes mean value: 122.4
145
(min) 96
137
107
127 125
(max) 155 127 [7] 60 minutes mean value: 124.7
153 156 151 145
(min) 90
102
107
123 117
140 110 [3] 10 minutes mean value: 114.8
132
[4] 15 minutes mean value 118.9
99 (max) 145
[2] 5 minutes mean value: 111
(min) 93
1,000
103
(min) 96
145 137
107
127 125
(max) 150 125
[6] 45 minutes mean value: 123.9
132 134 (max) 160
(min) 135 125 [8] 1,440 minutes mean value: 143.4
Figure 5. The zone of crease recovery angle values of investigated fabrics in weft direction
0
10
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80
Vis co ela 70 sti 60 c de for ma 50 tio n( 40 %) 30 20
90
10
0 0
10
0
10
90
20
80
) (% on 30 ati rm 40 efo cd 50 sti Pla 60 70
14
0
Figure 6 Determination of deformation components of investigated fabrics
10
20
30
40
50
60
70
80
90
100
Elastic deformation (%)
Key: Warp direction Weft direction
determined as a supplement to the investigation of behaviour of fabrics after action of bending force (pressure of 49KPa). The relaxation velocity, in fact, represents the ways (angle expressed like arc measure) in which fabric passes during the period of relaxation in a definite time. The relaxation velocity of investigated fabrics is shown in Figure 7. Figure 7 includes the results of relaxation velocities for all investigated fabrics (vertical lines). In this way, the lower and upper limits of the zone of relaxation velocities in warp direction (solid line) and in weft direction (dashed line) are determined. Also, the mean value of relaxation velocities of fabrics in both structural directions for each observed relaxation time (dots between the limits of zone) is determined. Besides the graphical dependence, for each curve which represents the definite limit of the zone and for the mean value of the zone, the mathematical model was established, which is valid for the relaxation time in interval of 5-1,440 minutes. The width of the zone in both structural directions becomes smaller with the increase in relaxation time. On the basis of the fact that the curves which represent upper limits of relaxation velocities in warp and weft direction are almost coincidental, maximal values of relaxation velocities in both structural directions are almost the same (see Figure 7). For the period of unloading the sample, in other words for the relaxation time of zero minutes, the relaxation velocity has an infinite value. For this reason, the
Resistance to creasing
Relaxation velocity (rad/min) 0.54 0.48 0.42
15 0.36 0.30 0.24 0.18 0.12 0.06 0.00 0
100 Relaxation time (min)
1,000
Key : y = 1.57x –0.959 – lower limit of zone in warp direction y = 2.41x –0.971 – upper limit of zone in warp direction y = 1.39x –0.935 – lower limit of zone in weft direction y = 2.38x –0.973 – upper limit of zone in weft direction y = 1.83x –0.951 – lower value of zone in both directions
time of one minute was chosen, which is the nearest to the beginning of relaxation, to compare the width of zone in warp and weft direction at the starting moment of relaxation. For a period of time of one minute, models of the zones’ limits of relaxation velocities in warp and weft direction are reduced to the value of the coefficient next to the independent variable. In that case, the width of the zone in the weft direction is 17.9 per cent larger than in the warp direction and its amount is 0.99rad/min, while the width of the zone in the warp direction is 0.84rad/min. After a period of relaxation time of 24 hours, the width of the zone in the warp direction is 0.00059rad/min, and in the weft direction 0.00046rad/min, which practically means that any further recovery becomes negligible. Conclusion Imposed investigations, aimed at finding the behaviour of clothing fabrics as a function of their resistance to creasing, showed that for the investigated clothing wool fabrics (region from 170-94g/m 2 ) determination of crease recovery angle gives the information on the basis of which is stated the zone between the limit values of crease recovery angle. This zone becomes narrower,
Figure 7. The zone of relaxation velocities of investigated fabrics
IJCST 7,4
16
especially in the weft direction, with the flow of time, on the basis of which it is possible to conclude that the differences in the size of crease recovery angle between fabrics of different constructive solutions become smaller in time. In spite of the fact that the calculated values of deformation components (elastic, viscoelastic, plastic) are different for each of the investigated fabrics, on the basis of calculated values of relaxation velocities it can be concluded that no matter what the structural characteristics, all fabrics after some period of time, more than 100 minutes, show approximately the same relaxation velocity. With this in mind, it can be stated that the proportion of deformation components has an influence on the starting phase of recovery as well as the differences in crease recovery angle values, but the further fabric recovery is occurring with approximately the same relaxation velocity. Results obtained in such a way point out the possibility that the behaviour of the whole group of fabrics is the same, despite any differences in their construction. An expansion of the method of determination, i.e. resistance to creasing by determining the elasticity through crease recovery angle and relaxation velocity, provides more information about the behaviour of clothing fabrics which can be used to make conclusions about fabrics’ behaviour within the scope of the investigation (even those fabrics which were not used in the experiment). This approach, we hope, enables an expansion of the experiment on a whole series of fabrics with definite parameters, which will enable conclusions to be drawn concerning the relations between parameters and resistance to creasing. References 1. Wortmann, F.J., “Aspects of the crease recovery of wool fabrics”, Melliand Textilberichte, No. 1, 1985, p. 78. 2. Wortmann, F.J., “The construction of a wool fabric as a factor influencing the crease recovery”, Melliand Textilberichte, No. 12, 1985, p. 852. 3. Amirbayat, J., “The buckling of flexible sheets under tension, part I: theoretical analysis”, Journal of the Textile Institute, No. 1, 1991, p. 61. 4. Amirbayat, J. and Bowman, S., “The buckling of flexible sheets under tension, part II: experimental studies”, Journal of the Textile Institute, No. 1, 1991, p. 71. 5. Sklyannikov, V.P., Stroenie i kachestvo tkaney, Legkaya ipishchevaya promyshlennost (Structure and Quality of Woven Fabrics), Light and Food Industry, Moscow 1984. 6. Nikoli´c , M.D. and Mihailovi´c , T.V., “Tensile force effects on wool woven fabrics”, Textile Asia, No. 11, 1994, p. 66. 7. International Organisation for Standardisation (ISO), ISO 2313-1972 (E), ISO, 1972.
Subjective and objective evaluation of men’s shirting fabrics Kit Lun Yick, K.P.S. Cheng and Yan Lai How Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong
Subjective and objective evaluation 17 Received September 1994 Revised and accepted April 1995
Introduction Traditionally, in the textile and clothing industries, the assessment of fabric handle is made subjectively by individual judges. The judgements rely strongly on personal criteria. This situation has brought confusion in mutual communication about fabric quality. Since 1930, various attempts have been made to obtain measures of fabric handle objectively in terms of fabric stiffness and thickness. In particular, Kawabata and Niwa[1,2] have successfully devised a system for the objective measurement of fabric handle for a wide range of men’s suiting materials. However, until now, there has been no concern with shirting materials. This study, therefore, provides some results from a statistical analysis of subjective handle assessments of shirting materials. The relationship between the judges’ preferences and the fabric mechanical parameters will also be studied. Subjective handle assessment Experiment method In the present work, a survey of the fabric handle for men’s shirting materials was undertaken. A total of 50 shirting materials were selected, and fabrics of different constructions of 100 per cent cotton and CVC were included. Details of the fabrics which were all commercially used are given in Table I. Each of these 50 fabrics was independently assessed for fabric handle by individual judges, including sales managers, retail traders, lecturers, experienced technicians, researchers and students of the textile and clothing sectors. The number of assessors required can be calculated statistically to get 95 per cent confidence interval accuracy. From Table II it can be seen that at least 196 judges would be required. In this study, 199 judges participated, where they were divided into two panels based on their academic or industrial experiences in the textile and clothing industry: (1) people who had less than five years of experience (most of them were clothing students at Hong Kong Polytechnic); (2) people who had five years or more experience in the clothing industry.
International Journal of Clothing Science and Technology, Vol. 7 No. 4, 1995, pp. 17-29. © MCB University Press, 0955-6222
IJCST 7,4
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Table I. Details of shirting materials used in handle survey
Table II. Rating scale of men’s shirting fabrics
100 per cent cotton pinpoint Oxford (plain white) 100 per cent cotton pinpoint Oxford (plain other than white) 100 per cent cotton pinpoint Oxford (stripes) 100 per cent cotton poplin, broadcloth, gingham Cotton polyester poplin broadcloth, gingham 100 per cent cotton caseman, Oxford, chambray, denim 100 per cent cotton twill Othersa Total
Number of fabrics
Fabric thickness (mm)
Fabric weight (g/m2)
5
0.247-0.307
128-135
5
0.251-0.292
124-134
5
0.299-0.334
129-139
5
0.217-0.316
92-121
5
0.228-0.262
97-106
5 3 17 50
0.395-0.698 0.255-0.474 0.271-0.894
142-258 108-167 100-179
Note: This included linen sheeting, cotton jacquard, cotton/wool, cotton/polyester dobby, cotton/ polyester pinpoint Oxford and cotton seersucker a
Total hand value (THV)
Evaluation
5 4 3 2 1 0
Excellent Good Average Below average Poor Out of use
All judges were given the following guidelines when asked to assess the fabrics: ● Rate the handle of each fabric according to his or her experience while ignoring the effects of colour and pattern. ● Rate the total hand value (THV) of 50 men’s shirting fabrics according to the scale shown in Table II. The calculation of the total number of judges required is as follows. The accuracy related to either the 95 or 99 per cent confidence interval can be calculated by the formula: N=
4 P (100 – P ) L2
where N = number of observations required; P = percentage time spent on the activity; L = limits of variation expressed as a percentage. In this case, P = 1/50 × 100 + 2 per cent for accuracy at 95 per cent confidence interval; the limits of variation (L) = 2 per cent. Therefore: N=
Subjective and objective evaluation
4(2 )(100 – 2 )
= 196 (2 )2 i.e. for 95 per cent confidence interval accuracy, at least 196 judges would be required.
19
Statistical survey analysis Variation of judges’ assessment The means and standard deviations of the two panels of judges are given in Table III. It can be seen from these figures, that judges of panel 2 tended to be more satisfied with the assessed fabrics (higher mean) and that there was a more consistent result for the same fabric (lower standard deviation). The result consistency may be explained by the years of experience, as panel 2 judges were better trained in the industry, therefore less deviation was found in the assessment. Overall agreement among judges In order to establish whether the individual judges assessed the handle in a consistent manner, the correlation coefficient between each judge’s handle rating and the total mean rating of the remaining 198 judges was computed. The average correlation of all the judges was then calculated, which is a measure of the overall agreement among judges. It was found that the average correlation coefficient was relatively low (0.44). This signifies that the judges had difficulty in rating these shirting samples, even though some of them had more than ten years’ experience in the textile and clothing industries. When comparing the overall agreement of the two panels of judges, judges of panel 2 tended to have a better degree of overall agreement among themselves. As shown in Table IV, a significant test of variances (ANOVA) was used to evaluate the level of agreement between each judge and the total mean of the remaining judges. Panel 2 (more experienced judges) exhibited a higher percentage of significance, and therefore gave a higher level of overall agreement.
Number of judges
Mean
Standard deviation
Panel 1
114
3.03
0.93
Panel 2
85
3.26
0.80
199
3.12
0.88
Total
Table III. The means and standard deviation of the two panels of judges
IJCST 7,4
20
Within-group agreement To establish within-group agreement, the correlation coefficient between each judge’s handle rating and the mean rating of that judge’s panel was calculated. The average correlation was then computed. It was found that the average correlation coefficients of panel 1 and panel 2 were relatively low as well (0.42 and 0.48 respectively). As mentioned above, the coefficients imply that both panels of judges had difficulty in rating the samples. Comparatively, the lower correlations obtained by panel 1 (less trained) judges indicated less agreement, or more spread of handle rating among their group than among the more experienced judges. Between-group agreement To ascertain whether there is agreement between the two panels of judges, the correlation coefficient between the mean handle rating of each group of judges were calculated. As shown in Table V, the high level of between-group correlation coefficient (0.94) represents a very high degree of agreement between the two panels. Panel 1 Total number of judges Number of judges with significant correlations* Percentage Number of judges without significant correlations ** Percentage
Table IV. Significant test (ANOVA) for linear regression of handle rating
114
Panel 2 85
69 (60.5)
58 (68.2)
45 (39.5)
27 (31.8)
Notes: * The correlation is regarded as significant at 99 per cent level when signif-F > 7.19 by ANOVA **The correlation is regarded as not significant at 99 per cent level when signif-F < 7.19 by ANOVA
Type of agreement
Correlation coefficients
Overall agreement
0.44
Within-group agreement:
Table V. Correlation of total handle
Panel 1
0.42
Panel 2 Between-group agreement
0.48 0.94*
Note: * The correlation is significant at 99 per cent level after significant test (by ANOVA)
Furthermore, as shown in Figure 1, it was found that panel 2 tended to be relatively conservative as it seldom gave “0” grade during the survey in that it was much less likely than the other group to assess a fabric as totally unsatisfactory or very poor (handle rating of 0 or 1). Also, both panels were reluctant to rate fabric handle as excellent (handle rating of 4.0-5.0) Basically, the high level of between-group agreement can be explained by their similar fundamental concepts of assessing fabric handle, since all the participating judges were local people with similar geographical, educational and industrial background. This study therefore eliminated national preferences as it was evident that judges from Australia have distinct differences from Japanese and Indian judges when assessing fabric handle[3-5]. Nevertheless, the low level of overall and within-group agreement obviously signified the errors inherent in subjective assessment of fabric handle. Experiment showed that academic or industrial experience apparently did not enhance better agreement. The large variability in fabric assessment may be due to the high level of randomness in the assessment such as pattern and colour preference, pattern and design influence, shade influence, tolerance standard age influence, emotional influence, etc.
Subjective and objective evaluation 21
Relationships between subjective and objective measurements This was intended to determine the extent to which the objective data can explain the subjective handle assessments of fabric characteristics made by the 5
Mean rating of panel 2 judges
4
3
2
1
0 0
1
2
3
Mean rating of panel 1 judges
Note: y = 0.445 + 0.929x
4
5
Figure 1. Graphical representation of the agreement between the mean handle ratings of the two panels of judges
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22
judging panels. First, linear regression analysis was applied to establish whether there was any relationship existing between the preferences of the judges and the fabric mechanical properties derived from the fabric assurance by simple testing (FAST) system. Afterwards, multiple regression analysis was used to work out a suitable equation which can best describe or predict the fabric handle assessments from the fabric mechanical parameters. Linear regression analysis The regression analysis was made by using the total mean rating of fabric handle from 199 judges as a dependent variable to measure the relationship against the FAST parameters, plus fabric weight and fabric sett. This accounted to a total of 17 variables, some of which included warp and weft values. The correlation coefficient values, significant levels and their linear equations are shown in Table VI. It must be noted that the negative correlation coefficient shown in Table VI means the variable was in inverse proportion to the THV, which is an undesirable feature in the assessment of fabric handle. Results and discussion As shown in Table VI, nine parameters exhibited some degree of correlation with the subjective assessment of fabric handle (correlation coefficients ranged from 0.52 to 0.78). The calculated correlation coefficients were verified at 95 per cent significant level, F-test values less than 0.001. Therefore, this provided support for the relationship found above. The rest of the parameters showed low correlation, which implied that there was no relationship between the handle assessment and the respective fabric properties. According to the results of the present work, shear rigidity, bending rigidity (mean) and formability (in warp and mean) seemed to be the most influential properties in subjective assessment because they exhibited the highest correlation coefficients. Besides, as all four coefficients were negative, the higher the fabric shear rigidity, bending rigidity (mean) and formability (in warp and mean), the lower the rating in handle assessment. Therefore, the fabric will have “good handle” if it can be deformed in shear or bend readily and buckled easily when compressed in its own plane. In Table VII, the correlation coefficients between the 22 FAST parameters and the rating of the two panels of judges are presented. The results were similar to those of the total mean rating as both panels gave the highest correlation coefficients in shear rigidity, bending rigidity (mean) and formability (in warp and mean). Therefore, it can be said, experience does not affect the preference for low values of shear rigidity, bending rigidity (mean) and formability (in warp and mean) in subjective assessment. Multiple regression analysis After linear regression analysis, several multiple regression analyses were made to find a suitable equation which can be applied to predict subjective hand
Independent variables
Correlation 95 per cent significant coefficient (r) level
Equations (y = a + bx) a b
Relaxation shrinkage (warp)
0.33
0.021
3.148
0.121
Relaxation shrinkage (weft)
–0.10
0.479
3.170
–0.053
Relaxation shrinkage (mean)
0.19
0.186
3.093
0.107
–0.07
0.625
3.168
–0.038
Hygral expansion (warp) Hygral expansion (weft)
–0.11
0.464
3.163
–0.023
Hygral expansion (mean)
–0.10
0.484
3.171
–0.033
Formability (warp)
–0.75*
0.000
3.296
–0.542
Formability (weft)
–0.43
0.001
3.352
–0.839
Formability (mean)
–0.77*
0.000
3.398
–0.934
Extensibility (warp)
–0.04
0.774
3.157
–0.012
Extensibility (weft)
0.09
0.533
3.043
0.020
Extensibility (mean)
0.05
0.740
3.066
0.018
–0.73*
0.000
3.235
–0.007
Bending rigidity (weft)
–0.53*
0.000
3.331
–0.035
Bending rigidity (mean)
–0.76*
0.000
3.272
–0.013
Shear rigidity
–0.78•
0.000
3.402
–0.005
Thickness
–0.53*
0.000
3.600
–1.315
Surface thickness
–0.18
0.216
3.231
–0.720
Relaxed surface thickness
–0.13
0.372
3.232
–0.740
Weight
–0.70*
0.000
4.322
–0.009
Ends
0.57*
0.000
2.468
0.005
Picks
0.36
0.010
2.300
0.011
Bending rigidity (warp)
Note: * The correlation is significant at 95 per cent level when value < 0.001 (by ANOVA)
value. There were three possible alternative methods, each of which provides different sets of independent variables against the total mean rating. The multiple regression results are shown in Table VIII. The models Model 1. There were a total of 13 parameters shown in the equation. It was found that the model had a strong correlation coefficient (0.944). It can be regarded as highly significant, as the significant F-test at 95 per cent significance level approached zero. This model, therefore, reflects a highly significant regression. Model 2. In this model, ten parameters were shown in the equation. Its correlation coefficient (0.895) and R2 (0.801) were high as well, which implied
Subjective and objective evaluation 23
Table VI. Linear regression result – total hand value against 22 parameters
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Table VII. Linear regression results – comparison of correlation coefficents
Independent variables Relaxation shrinkage (warp) Relaxation shrinkage (weft) Relaxation shrinkage (mean) Hygral expansion (warp) Hygral expansion (weft) Hygral expansion (mean) Formability (warp) Formability (weft) Formability (mean) Extensibility (warp) Extensibility (weft) Extensibility (mean) Bending rigidity (warp) Bending rigidity (weft) Bending rigidity (mean) Shear rigidity Thickness Surface thickness Relaxed surface thickness Weight Ends Picks
Total mean rating 0.33 –0.10 0.19 –0.07 –0.11 _0.10 –0.75* –0.43 –0.77* –0.04 0.09 0.05 –0.73* –0.52* –0.76* –0.78• –0.53* –0.18 –0.13 –0.70* 0.57* 0.36
Panel 1 rating
Panel 2 rating
0.36 –0.13 0.18 –0.09 –0.14 –0.12 –0.73* –0.46 –0.74* –0.07 0.07 0.02 –0.71* –0.56* –0.73* –0.75* –0.54* –0.20 –0.16 –0.71* 0.57* 0.38
0.26 –0.04 0.18 –0.03 –0.03 –0.08 –0.74* –0.37 –0.75* –0.01 0.12 0.07 –0.72* –0.44 –0.74* –0.77* –0.49 –0.15 –0.09 –0.65* 0.53* 0.32
Note: * The correlation is significant at 95 per cent level when value < 0.001 (by ANOVA)
good correlations of the model. The significant F-test also showed that the assumption of model 2 was reliable (approach to zero). Model 3. Nine parameters were selected in this model as they possessed significant correlations in the linear regression analysis. This model achieved strong correlation coefficient and R2 (0.920 and 0.847 respectively). Similar to the above two models, the significant F-test (approach to zero) indicated that the total hand value and the nine parameters were highly correlated. The regression model was remarkably reliable for predicting subjective assessment accurately. Prediction As shown, all the three models demonstrated very strong correlations with the subjective hand value. To verify these models, a new set of 16 fabrics was
Variables (x) Model 1
Coefficient (m)
Variables (x)
Coefficient (m)
F-1 F-2 E100-1 E100-2 B-1 B-2 G
0.272 –1.093 0.029 0.093 –0.004 0.002 –0.003
T2 ST STR W Ends Picks
–1.368 1.381 0.665 0.002 0.002 –0.001
Model 2
F E100 B G T2
–0.728 0.079 0.007 –0.003 –2.577
ST STR W Ends Picks
2.950 0.133 0.002 5E-05 2E-04
Model 3
F-1 F B-1 B-2 B
0.461 –0.411 0.005 0.004 –0.021
G T2 W Ends
–0.003 –0.189 3E-04 0.003
Subjective and objective evaluation 25
Notes: Model 1 All the mechanical properties taken from the FAST parameters plus the fabric sett (13 variables) Total hand value equation: y = 2.805 + (mx)1 +…+(mx)n, where n = 13 in this model Correlation coefficient = 0.944 R2= 0.891 95 per cent significant level* = 1.8E-13 Standard error = 0.147 Model 2 The mean value of the FAST parameters plus the fabric sett (ten variables) Total hand value equation: y = 3.380 + (mx)1 +…+(mx)n, where n = 10 in this model Correlation coefficient = 0.895 R2 = 0.801 95 per cent significant level* = 9.0E-11 Standard error = 0.190 Model 3 The variables with r ≥ 0.5 (nine variables) Total hand value equation: y = 3.020 + (mx)1 +…+(mx)n, where n = 9 in this model Correlation coefficient = 0.920 R2 = 0.847 95 per cent significant level* = 1.3E-13 Standard error = 0.165 * The correlation is significant at 95 per cent level when value < 0.001 (by ANOVA)
Table VIII. Multiple regression results
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26
assessed by a panel of 14 judges all of whom had been members of the original judging panel for the 50 fabrics used in the previous analysis. Table IX shows the results of the handle assessment and the predictions from the three multiple regression models and their respective correlations. For the new set of fabrics, very high correlation coefficients (greater than 0.90) were found from the models. This means all of them were able to be used to predict the trend of handle assessment by fabric mechanical parameters. However, such predictions were not perfect and usually involved different degrees of variation. As shown in Figures 2, 3 and 4, all regression lines had negative intercepts, which were obviously undesirable. The negative intercepts indicated that there was a bias in the predicted values, which, on average, will overestimate the subjectively assessed THV by 1.02 THV units in model 1, 0.40 units in model 2 and 1.03 THV units in model 3. Besides, the steep slopes of the
Fabric SMT-11572 SMT-01713 SMT-01702A SMT-01702B SMT-11613 SMT-01739B SMT-20437 SMT-20427B SMT-20427A SMT-20471A SMT-20471B SMT-20455A SMT-29455B SMT-01516 SMT-01538 SMT-01719A
Table IX. Total hand value predictions from mechanical parameters by using three multiple regression models and the subjective assessments results
Predicted THV from model 1
Predicted THV from model 2
Predicted THV from model 3
Mean assessed THV
3.25 3.25 3.29 3.23 3.22 3.15 3.05 3.19 3.19 2.92 2.99 3.19 3.18 1.34 2.42 2.60
3.22 3.24 3.26 3.17 3.30 3.19 3.22 3.21 3.17 3.11 3.13 3.21 3.18 1.30 2.70 1.62
3.29 3.26 3.30 3.24 3.32 3.20 3.10 3.19 3.21 2.89 2.91 3.16 3.15 1.34 2.72 2.78
3.29 3.29 3.36 3.29 3.14 3.07 2.93 3.21 3.29 2.86 2.93 3.29 3.21 0.93 2.29 1.64
0.97 6.0E-10 –0.398 1.109
0.91 8.2E-07 –1.030 1.300
Regression analysis Correlations with judges’ assessment 0.94 95 per cent significant level* 1.6E-08 Intercept –1.015 Slope 1.312
Note: * The correlation is significant at 95 per cent level when value < 0.001 (by ANOVA)
Subjective and objective evaluation
Assessed total hand value
4
3
27 2
1
0 0
1
2
3
4
Total hand value from model 1
Figure 2. Predictions from model 1
Note: y = –1.015 + 1.312x
Mean assessed total hand value
4
3
2
1
0 0
1
2
3
4
Predicted total hand value from model 2
Note: y = –0.398 + 1.109x
Figure 3. Predictions from model 2
IJCST 7,4 Mean assessed total hand value
28
4
3
2
1
0 0
1
2
3
4
Predicted total hand value from model 2
Figure 4. Predictions from model 3
Note: y = –1.030 + 1.300x
regression lines showed that there were quite large variations from the ideal unity slope (1.00). These variations may affect the accuracy of predicting the response of the subjective assessments to changing fabric parameters. Comparatively, among the three models, model 2 exhibited the strongest correlation coefficient (0.97) for the relationship between the predicted and assessed values with the least variations. Parameters for the least squares linear regression line were an intercept of –0.40 and a slope of 1.11. Therefore, it is suggested that the THV equation determined by model 2 tended to be the most accurate equation as it gave the best fit regression line. Overall, the prediction accuracy for the THV assessments was reasonably good. Most of the judges had linear correlations with the prediction models at 99 per cent significance level. This implied that the judges assessed the fabrics with some consistency in their opinions which were based on mechanical considerations. On the other hand, two judges appeared to have dissimilar characteristics from the main body of judges; these two possessed nonsignificant correlations with the predicted values. This will probably be explained by changing their assessment patterns during the surveys. Conclusion In the present work, a statistical analysis of subjective assessment has been briefly outlined. The results support the view that the judgement depended strongly on personal criteria and there was no common standard about this.
Although the well-trained panel of judges produced better consistency when assessing fabrics, academic or industrial experience did not improve the level of agreement significantly. Besides, the results also indicated that the judges had difficulty in rating the shirting materials as it was more difficult to distinguish the small mechanical differences in shirting materials. By using linear regression analysis techniques for shirting materials, shear rigidity, formability and bending rigidity were found to have significant correlations with fabric handle. Meanwhile, multiple regression analysis was applied to find a suitable equation which could best describe or predict the fabric handle assessments. To some extent, the trend of people’s preferences for assessing fabrics can actually be predicted from fabric objective measurements. However, it was demonstrated that these mechanical measurements were not sufficient and usually contained variations with the mean of the assessments. Besides, the predictions of individuals’ handle preferences was found to be more difficult as there was high level of randomness in fabric assessment and judges may also change their assessment patterns during the surveys. This implied that some other objective data have to be found which will complement the existing test data in order to provide firm accurate predictions. References 1. Kawabata, S. and Niwa, M., “Analysis of hand evaluation of wool fabrics for men’s suits using data of a thousand samples and computation”, The 5th International Wool Textile Research Conference, Aachen, Vol. V, 1975, p. 413. 2. Kawabata, S., “Examination of the effect of basic mechanical properties of fabrics on fabric hand”, in Mechanics of Flexible Fibre Assemblies, The NATO Advanced Study Institute, Greece, 1979, p. 405. 3. Mahar, T.J. and Postle, R., “International fabric handle survey”, in Objective Evaluation of Apparel Fabrics, 1983, pp. 261-72. 4. Mahar, T.J., Dhingra, R.C. and Postle, R., “Comparison of fabric handle assessments in Japan, Australia, New Zealand and India”, in Objective Specification of Fabric Quality, Mechanical Properties and Performance, 1982, pp. 149-59. 5. Stearn, A.E., D’Arcy, R.L., Postle, R. and Mahar, T.J., “A statistical analysis of subjective and objective methods of evaluating fabric handle. Part 1: analysis of subjective assessments”, Journal of the Textile Machinery Society of Japan, Vo1. 34 No. 1, 1988, pp. 13-18.
Subjective and objective evaluation 29
IJCST 7,4
Quick response – ten years later N.A. Hunter
30 Received April 1995 Accepted June 1995
College of Textiles, North Carolina State University, Raleigh, North Carolina, USA, and
P. Valentino VS Associates, Oyster Bay, and Fashion Institute of Technology, New York, USA Background It is ten years since quick response (QR) was formulated as an improved way of conducting business in the US textile/apparel pipeline. Since then, it has been a topic of great interest to the trade; articles have been written, seminars conducted, and talks given, but very little has been achieved. The discussion below examines the major causes for the long delay in implementation, and attempts to identify what must be done to get QR back on track. The information relates mainly to North American industry, but there is every reason to believe that European businesses are in similar situations. The word “pipeline” used above was itself something of a novelty and signified the necessary integration of the various component sectors – fibre, spinning, knitting or weaving, manufacturing, and retail[1]. QR called for a drastic reduction in the time taken to convert fibre to fabric, fabric to a garment and then place it in the hands of the consumer[2,3]. Such a reduction was deemed necessary if apparel supply was to become a consumer-driven, rather than a manufacturing-driven enterprise and called for the use of a wide variety of electronic and mechanical technologies as well as substantial changes in management practices[4]. Further, implementing QR required a much higher level of trust and co-operation between the industry sectors. The projected benefits to the industry of QR were many and varied[1] and included:
International Journal of Clothing Science and Technology, Vol. 7 No. 4, 1995, pp. 30-40. © MCB University Press, 0955-6222
●
reductions in pipeline inventories;
●
the greater probability of garment designs and colours being acceptable to the consumer by moving styling closer to the sales date;
●
the ability to re-estimate stock keeping unit (SKU) demand at retail and make frequent reorders during the season, thus reducing stockouts and markdowns;
●
greater competitiveness for domestic producers facing increased levels of imports.
The reality of the benefits was quickly confirmed by a series of large-scale “partnership” trials carried out in 1985-1986, including the Milliken/Seminole/ Wal-Mart linkage, and by subsequent business partnerships, consultant studies[5] and computer simulation[6,7]. Why, then, when the technologies, both hard and soft, were well understood, and the benefits so significant, has QR taken so long to be implemented on a broad scale? There is little to show in the way of improvements in operating efficiencies throughout the pipeline. Figure 1 shows that the only sector to show a significant improvement in productivity in recent years is retailing (5 per cent per year), with textiles gaining somewhat less (2.4 per cent per year) and manufacturing a mere 1 per cent per year. In Figure 2 the lack of improvement in inventory to sales ratios is demonstrated. The same pattern is found for other apparel retailing segments. Further, although no data are available, any consumer is aware of the high levels of stockouts and the increasing frequency and depth of markdown, as opposed to promotional, “sales”. This lack of customer service is reflected in the pattern of sales and inventories shown in Figure 3. Here, at the holiday sales peak, inventories are sharply down; a measure of the “grab-bag” environment encountered later by the consumer.
Quick response – ten years later 31
Problems In answering the question raised above, it must be said that the early advocates of QR were naïve. We now realize that their expectation levels were far too high. Two related examples will suffice to illustrate this claim. Changes in management practices and corporate culture are notoriously slow to occur and
250 200 150 $ thousands 100 50 0
1
2
3
4
5
6
7
Years 1987-1993
Key: Manmade fibres Textiles Manufacturers Retail all
Source: Based on US Department of Commerce data [8,9]
Figure 1. Industry output per employee
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120
3 2.9
100
2.8 2.7
80
2.6
32
$ millions
60
2.5 2.4
40
Inventory to sales ratios
2.3 2.2
20
2.1 0
1
2
3
4
5
6
2
7
Years 1987-1993
Key:
Figure 2. Apparel speciality stores sales and inventory to sales ratios
Sales Inventory sales
Source : Based on unadjusted US Department of Commerce data for apparel speciality stores 6
16
5.5
14
5 12
4.5
10 $ millions
4 3.5
8
Inventory to sales ratios
3
6
2.5 4
2
2 0
Figure 3. Monthly sales versus inventory/sales ratios: apparel speciality stores
1.5 1 1
2
3
4
5
6
7
8
9
10
11
12
Sales July 1993-June 1994
Key: Sales Inventory/sales ratio
frequently require the ascension of a new generation of leaders. Also, it was assumed that the often adversarial relationships between sectors would change readily, giving way to “partnerships”. One of the problems with the latter is that the major part of the increase in profits coming from the practice of QR goes to the retailer, while the up-stream participants take on most of the cost burden. Without
some sharing of the benefits as well as the costs, amicable partnerships are unlikely to develop. However, there were other, more fundamental reasons, both structural and technical, which were not understood at the time and which are only now being worked out.
Quick response – ten years later
Structural The textile/apparel pipeline is unique in at least three ways. The first is the staggering number of unique SKUs available to the customer at any one time – a large department store can carry 1.2 -1.4 million every four months, an order of magnitude greater than for the food and general merchandise industries. The second way is the overwhelming effect of fashion; the average shelf life of apparel is steadily decreasing as greater percentages of all classes of goods come under its influence. The third aspect is the make-up of the pipeline. In sharp contrast to the automobile, appliance, and electronic businesses, where the fabricator of the endproduct dominates the smaller parts suppliers and distributors, the apparel manufacturer is typically small and with little influence over either the textile supplier or the retailer. There are exceptions – Levi, for example – but of the 14,000 or so apparel and related manufacturing establishments in the USA, over half have fewer than 20 employees, and the largest sector – Women’s and Misses – with 9,200 establishments, averages US$2.3 million per year in sales. In this highly fragmented and competitive environment, capital expenditures are slight and investment in new technology is made reluctantly. Further, such companies cannot support technical staffs and the mostly minimum wage operators are not suitable for the demands of QR. The retail scene is also complex. While there are many large department stores, chain stores, speciality chains, and discounters, there are thousands of small outlets, often lacking in any management or technological sophistication. An additional complication is that the large firms have developed their own buying, inventory management and ordering procedures, making for problems for any manufacturer selling to multiple customers.
33
Technical problems The rapid and accurate acquisition, handling, and transfer of information is central to QR. We will examine these aspects in order. In 1985 barcoding was common among supermarkets and food producers, but it was not clear that uniform product code (UPC) symbology could extend to the more complicated apparel business. As it turned out, there proved to be fairly rapid adoption by larger retailers, accompanied by the use of barcode scanners in both the distribution centres and at point of sale (PoS). One problem remained; that of the accuracy of the barcodes. Some retailers have estimated that up to 70 per cent of electronic data interchange (EDI) transactions, including barcodes, contain wrong or incomplete information, making sales tracking, inventory holdings and re-estimations/reorders error
IJCST 7,4
34
prone. This lack of faith in the manufacturers’ UPC numbers or item marks results in retailers maintaining their own internal numbering schemes to associate the vendor’s identification with their internal number or mark. This mapping of data sets involves duplication of data, is more prone to errors, and increases the retailer’s systems overhead. We hear (informally) that some EU retailers are considering discarding the European article numbering (EAN) system because of the inaccuracy of vendors’ encoding and the lack of integrity checks on the creation and assignment of EAN numbers. Considering that accurate UPC marking was a requirement for a true QR environment, little progress has been made towards having vendors comply with industry standards. Late in 1994, a number of major retailers started to implement penalties for non-compliance. These include Dillards, Dayton Hudson, and Federated Department Stores; most recently, Sears (Canada) has joined the group. We believe this to be a necessity for the health of the industry, and preferable to opting out of UPC (or EAN) standards. There are approximately 1,100 manufacturers in the USA now transmitting UPCs to the price sales catalogue – less than 7 per cent of the market. We have a long way to go. The storage and manipulation of inventory and sales data afforded more problems. In 1985, IBM dominated apparel pipeline computing with its mainframe and mini computers – mainly 34s and 36s. Micro computers had been introduced four years previously and, with their limited memory and speed, were not considered suitable for enterprise data handling. The cost of large computers and their software was, of course, prohibitive for any but the largest companies. For small companies, computing really only became feasible with the appearance of low cost 386 and 486 machines. The third group of problems centred on information transmission; EDI. In 1985 the value added networks (VANS) included General Electric’s GEIS, the IBM Network, Ordernet (now Sterling Software), and British Telecom. They concentrated on the grocery and food businesses. These networks provided an “added value” of in-house translation since there were no standardized documents for the retail-related industries. Standardized formats for orders and invoices for the general merchandise and retail industries were developed early in 1987 by the Voluntary Industry Communications Standards (VICS) committee when it set about the task of establishing EDI formats for the apparel industry[10]. It is no surprise, then, that it was the end of the decade before all the pieces were in place for QR even to be possible. Current position Overview Despite the problems discussed above, all sectors have accepted that the entire system must be consumer-driven via PoS information and there is now a welldefined movement towards one important aspect of QR; the major retailers have assumed leadership in implementing QR. As mentioned earlier, with many of these organizations, suppliers are now on notice that EDI is, or will be in the near future, a condition of doing business. The result is a scramble among medium-
sized and small vendors to acquire EDI technology; the large manufacturers are, for the most part, already equipped. The driving force behind this movement is the vision of inventory reductions at the departments and stores, particularly in the case of basic goods. These are year-round items, or those with long shelf lives, relatively unaffected by fashion. However, once EDI is in place, garments of every seasonality will be affected. Led by Walmart, there is a growing realization that replenishment of basic goods could well become the responsibility of the vendor once he/she has been supplied with accurate PoS data. The consequences for the manufacturer of assuming greater inventory responsibility will be examined in the following section. The interaction between EDI and both seasonal (12-20 weeks of shelf life) and fashion merchandise (ten or less weeks) is more complex. In addition to orders, invoices, and advance shipping notices, full implementation of QR for seasonal goods will require re-estimation/reorder procedures to ensure the correct volume and mix of SKUs on the shelf both during the season, to maximize customer service, and at the end of the season, to minimize the markdown effect[6]. These are some way from being commercially developed. QR impacts fashion goods to a lesser extent than either basics or seasonal merchandise. Though shorter pipelines improve the forecasting and order accuracy of styles and colours, there is insufficient time in the selling season to correct design or buying errors. In the early days of QR, it was hoped that the domestic time contraction would help counteract offshore cost advantages. We now believe this will not be the case. Oriental suppliers have responded well to US retailers’ insistence on shorter lead times. An example of this is the 500-hour rule imposed by The Limited, a large speciality chain. Given the fabric and the pattern, Far East suppliers are to have goods on the shelf in three weeks, with the aid of air transportation. For this important and growing class of merchandise, then, domestic manufacturers must find other ways to develop a competitive advantage. In the next three sections we expand on three factors of greatest importance to the growth of QR. UPC/EAN compliance The requirement that there be a unique item number for each different product is essential if a global, consumer-driven, PoS-controlled system is to be established. The standards for full compliance with this concept have been developed in the USA and Canada by the Voluntary Industry Committee and published by the Uniform Code Council as the Data Communication Guidelines for Retail and General Merchandise. In the case of apparel, the manufacturer must indicate his style number and national retail federation (NRF) colour number and NRF size number. Together, these are associated with one unique 12-digit number represented by UPC/A symbology, i.e. a barcode. It should be noted that the retail and general merchandise industry is the only segment with published standards for product identification. Such standards do not exist for groceries,
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over-the-counter drugs, health and beauty care products and prescription drugs, and hardware. The National Retail Federation is an organization which represents retail interests in the USA and Canada. Its colour system currently recognizes 1,000 colour groups and the Federation has developed various size tables that are category-specific. Thus, a manufacturer must assign an NRF colour number to the specific product within the category, size his product and find the related NRF size number. In this manner, a retailer can compensate for manufacturer nuances when comparing many manufacturers’ sizing differences. In countries other than the USA and Canada, a 13-digit EAN symbology is used. The UPC number becomes an EAN number by adding an “0” in front of the UPC number. This is possible since the USA and Canada share the same country code value of “00”. However, problems are now emerging. In countries other than the USA and Canada, there are no reference colour or size tables, but manufacturers in Europe seeking to publish their product catalogues on various price sales catalogues in the USA and Canada must assign NRF size and colour values to their products. A USA or Canadian manufacturer can create a compliant VICS 832 price sales catalogue document and transmit it as an EDIFACT or as an ANSI document. However, an 832 which is being generated globally according to EAN standards and which is sent to the USA and Canada for posting will be rejected since the global standards do not comply with the NRF standards. The standardization of size and colour reference tables which transcend geographical and country boundaries must become a priority if we are to succeed in creating a quick response environment that supports co-ordinated demand-activated manufacturing responses. The lack or absence of datacompatibility standards among countries that produce apparel and general merchandise products will not provide the so-called “co-ordinated responses” to the retail communities that are seeking to meet their customers’ (the consumers) needs. Given the recent requirement of many USA and Canadian retailers for their domestic suppliers to become UPC compliant, it may be safely assumed that similar pressures on overseas suppliers will heighten. To this end, the value added networks are now considering the business requirement of these suppliers, though they will need the assistance of the appropriate industry or trade groups to set up the necessary standards. EDI standards In addition to barcodes, information that can be conveyed electronically includes orders, invoices, shipping notices, and a variety of other documents. It has been said – with some basis – that the only thing standard about EDI standards is the lack of a standard. Retailers and manufacturers use sub-sets of the existing standards; a retailer or trading partner feeling free to select from the available document those data elements that are applicable to his own operational needs. To give just two examples, among retailers in the USA and Canada, there is no single template for the Advanced Ship Notice (856), and the
three networks that have a price sales catalogue (GEIS, Sterling, Quick Response Services), support different data elements within the 832 document and have different templates or requirements for the structure of the (832) document. The likelihood that there will be “universal documents” in the near future is small indeed; all participants in the EDI process, including the VANS, must make the effort towards greater levels of consistency. VANS The role of the value added networks (VANS) has changed significantly since the inception of quick response in 1985. Then, they were perceived as being only the telecommunication gateway for the trafficking of EDI documents among multiple trading partners. These third-party networks provided “added value” by offering retailers and manufacturers alike the ability to “translate documents” among various versions and computing platforms and transmit them more quickly than the traditional physical forms of delivery. Speed of delivery collapsed order-fulfilment cycle time and this was perceived by many as the meaning of quick response. In a strict sense, EDI is a subset of quick response. The true benefits of an EDI environment are reached when there is application-to-application integration. An example of this would be when orders are electronically received by a manufacturer and the orders are electronically processed by the internal host system of the supplier without human intervention. Such a system would eliminate certain “non-added value processes” and thus decrease labour costs to a manufacturer. In the event that the environment did not reflect APP to APP, the benefits to the supplier would only be to speed up delivery of the documents while the labour costs of the manufacturer can increase. An example of this is to be found in the majority of those smaller manufacturers new to EDI. An order is received, downloaded, and then manually re-entered into the internal control network. This not only costs time and effort, but also increases the chance of errors occurring. Clearly, there is a need for software to link the external and internal systems. Such linkages are mandatory if the manufacturer is to streamline his operational procedures so as to minimize his inventories or make optimal use of his contractors. The future for QR Near term Over the next one to two years, the most pressing task for the industry will be to extend the use of EDI to all manufacturers as well as the smaller retail outlets and to forge better links with the textile producers and other suppliers to the garment industry. The role of the VANS may be the most pivotal in resolving the QR supply chain’s problems as they have the technical expertise to effect change among suppliers. The retail community has, in many respects, already relegated to the VANS the task of implementing EDI solutions among their trading partners. Also, the competitive environment among the VANS has intensified as the
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trafficking of electronic documents has become a price-commodity business. The technologies among the VANS, although slightly different, offer similar speed of delivery in real time terms. Since the documents are non-proprietary in nature, no one VAN provides a particular benefit. Product differentiation among the VANS in the near future will be the delivery of “quality information products”, as contrasted with speed of delivery: the VAN that can provide the most accurate information to its trading partners will have the strategic advantage. To accomplish this objective, which the VANS will need to maintain or gain share of market, will require more “ownership” of the supply chain. They will have to provide computer software solutions that add value to their information products by increasing the accuracy of the data as they are sequentially or concurrently processed, as well as permitting additional data processing and systems integration. In order for the VANS to implement these value added applications and satisfy their retail customers’ needs for quality information products, they will need to change their present training approach to software and solutions to an educational approach. The former stresses how something, be it a product or a process, works, while an educational approach is more concerned with why the product or process exists and how it facilitates or enables improvement or innovation. Currently, Sterling Software’s Network Division is the only VAN that provides educational courses for manufacturers. Sterling started COMMERCE: Institute in 1991 to provide educational courses in supply chain business issues. Many of these courses deal with topics that offer no software or network solutions. What they offer are alternatives and a vision of altered business practices that can lead to operational benefits. A strong case can be made for the retailers to tie into this aspect of the VANS operations by mandating that their suppliers receive this kind of instruction, and awarding some form of certification to those manufacturers who comply. In all respects, the VANS, through their value added applications, will need to champion ownership in the supply chain if the new co-ordinating technologies are not to open the door to a different kind of competitor who will fuse computers and telecommunications. Further, their job will not be complete until they streamline the interconnections, one to another. By this we mean that a manufacturer deals with retailers using different VANS and it should not be his job to sort out to whom he should send what. Already, some progress is being made in this area. Alongside the wider use of EDI must come improvements in the quality of the information being transmitted. UPC errors can be reduced or eliminated through use of such software packages as the bar-code director, now being offered by the major VANS. The reductions just beginning in retail inventories lead to a host of problems for both the short and the long term. We mention only one; that of balancing low retail inventories against desired customer service levels (the complement of stockouts) and supplier and DC lead times. This aspect of QR is not well
understood, but progress is being made and it is a potential major contributor to retail profits. At this point we should introduce the idea of an information field that will become increasingly important as QR develops; accurate colour description. Currently, the colour attributed to his merchandise by the manufacturer in the USA or Canada follows the standards set by the National Retail Federation. These can only be described as arbitrary and pre-Newtonian. There is little excuse for not adopting a more modern and accurate nomenclature such as the CIE system, which specifies colour using three co-ordinates, and which would call for no change in standards or formats. Textile producers and the better dye houses routinely measure the colour of their fabrics and could supply CIE co-ordinates. Their reason for not doing so (or supplying only partial information) is a claim of the need for security. This argument is indefensible – any commission dye house can match a shade in 24 hours. Long term Having taken so long even to get started, it may seem presumptuous to guess how far QR will progress over the next five-plus years. The QR paradigm has become clearer, however, and a number of initiatives have already been taken. The retailer, once he has worked out how to balance lead times, stockouts, and vendor lead times, will start to examine the end-of-season markdown mix of seasonal goods, with its attendant margin loss, and install procedures for reestimation of demand and the appropriate reorders. The problems associated with fashion merchandise, while similar, are more complex and call for sophisticated analyses of fashion and colour trends. These techniques have yet to be developed. While fashion is, almost by definition, impossible to forecast, we believe that certain underlying trends can be projected, but these projections will require extensive historical databases, and these are not yet being accumulated. Successful manufacturers will be those making use of PoS data, who can anticipate seasonal demands, estimate shifts from buyer projections, and keep inventories to a minimum, while keeping their textile and other suppliers informed of their own requirements. This kind of juggling act will require very sophisticated software and innovative management. It will also call for flexible and rapid production techniques. It is in these areas that the manufacturer can add real value to his merchandise and tie himself more closely to his customer. This is the surest way to increase competitiveness – increasing the switching cost for the retailer. It is certain that computing costs and speeds will continue to improve and we will soon be at the point where a new aspect of the apparel business becomes possible on a broad scale. This will be a wider use of CAD, extended to include interactive designing of garments with the customer – the retail buyer. At present, a great deal of time and effort is spent in making sample garments and carrying them round to buying offices. This time can be greatly reduced if the CAD images are reviewed electronically and modified as to colour and style before any cutting is done. A second step will be inclusion of the garment design
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in the price-sales catalogue, thus allowing sales preference data series to be established, and the possibility of true colour and style forecasting to open up. So far, we have made little mention of the textile and fibre producers. This is not an oversight, but reflects our belief that the prime imperative of the pipeline is to broaden and focus the UPC/EDI aspects of the pipeline. Given reliable PoS data and the manufacturer/ retailer systems to make use of them, the industry will be in a position to tie into the very sophisticated up-stream systems capable of supporting demand-activated manufacturing. Until this happens, we believe the most important role these large and disciplined enterprises can play is to partner the VANS in their attempts to broaden acceptance of and compliance with EDI standards. However, there is another aspect to such teamwork: developing a financial infrastructure that will allow the small manufacturer to invest in the software and services needed to participate fully in the EDI process. We noted earlier the size discrepancy between the large majority of manufacturers and both their suppliers and their customers. The EDI ante poses problems for many small companies and some underwriting mechanism would do much to speed up the adoption of EDI. Fibre producers, textile companies, VANS, and retailers alike have a financial stake in the health of garment industry – without it they have no customers. References 1. Hunter, A., Quick Response in Apparel Manufacturing, The Textile Institute, Manchester, 1990. 2. Frazier, R.M., “QR inventory replenishment”, paper presented at the 75th National Retail Merchants Association Conference, 1986. 3. Harding, P.W., “QR in the soft goods pipeline”, paper presented at the Conference on Computer-aided Manufacturing in Textiles, Clemson University, Clemson, SC, 1985. 4. Quick Response Implementation – Action Steps for Retailers, Manufacturers and Suppliers, Technical Report, Kurt Salmon Associates, New York, NY, 1988. 5. Quick Response: A Study of Costs and Benefits to Retailers of Implementing Quick Response and Supporting Technologies, Technical Report, Arthur Andersen Consulting, New York, NY, 1989. 6. Nuttle, H.L.W., King, R.E. and Hunter, A., “A stochastic model of the apparel-retailing process for seasonal apparel”, Journal of the Textile Institute, Vol. 82 No. 2, 1991. 7. Hunter, A., King, R.E. and Nuttle, H.L.W., “An apparel-supply system for QR retailing” Journal of the Textile Institute, Vol. 83 No. 3, 1992. 8. Monthly Retail Trade, November 1994. 9. US Industrial Outlook, 1994. 10. Implementing VICS Technology and Quick Response, Technical Report, Kurt Salmon, New York, NY, 1989.
Computer-aided designers?
Computer-aided designers?
A study of garment designers’ attitudes towards computer-aided design Carolyn H.M. Hardaker and Gary J.W. Fozzard The CAD Laboratory, School of Design and Manufacture, De Montfort University, Leicester, UK
41 Received March 1995 Accepted June 1995
Introduction The human aspects of using computer-aided design (CAD) has been the subject of extensive research[1-4]. Until now, however, this research has concentrated on the use of CAD as an engineering, industrial or architectural design tool. The use of CAD in the clothing and textiles industries is somewhat different. Here the product emphasis is predominantly aesthetic as opposed to predominantly functional in the engineering environment and this has resulted in the development of specialist CAD applications for the clothing and textiles industries. These “new” applications for fashion illustration, textile design, pattern cutting and lay planning are just as important as the traditional uses for technical drafting, three-dimensional modelling, PCB layout and architectural design. It follows, therefore, that potential CAD practitioners can have extremely diverse backgrounds; the engineer with a formal scientific training is a potential CAD user as is the fashion designer and pattern cutter whose previous experience is focused on creativity and skilled craftsmanship. The main part of this article describes an investigation into the attitudes of this relatively new group of CAD practitioners. A garment CAD training programme held at De Montfort University Leicester was used as the basis for the study. This programme consisted of a series of introductory short courses providing an insight into the use of garment CAD. A total of 22 delegates attended the training courses including nine designers/pattern cutters. It was this group of designers who were asked to participate in the attitude study. A quantitative assessment of their attitudes to CAD after an initial exposure to the technology was made. The study investigated the influence of “background variables”; age and computing expertise on the attitudes formed and appropriate statistical tests were used to identify significant trends in the data. Background to the research CAD in the clothing and textiles industries CAD for the clothing and textiles industry has been available commercially for over 20 years. The first systems were pioneered by the Hughes Apparel Systems in 1968, but owing to high costs it was not until the 1980s, when system costs started to fall, that the technology was considered a feasible proposition by the clothing industry. Over the years the benefits of CAD have
International Journal of Clothing Science and Technology, Vol. 7 No. 4, 1995, pp. 41-53. © MCB University Press, 0955-6222
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been well publicized and, when utilized by trained professionals, it is a proven way to improve productivity. The last 15 years have seen considerable changes in market trends and retailer strategies. Zeitlan[5] observed that, “No longer are clothing manufacturers turning out long runs of a small number of garments, today’s manufacturers are designing and manufacturing the widest possible range of styles at the lowest cumulative cost”. This situation is still true today. An effective response to these market pressures can be made by the introduction of new technologies such as computer-aided design. By using CAD many of the repetitive manual tasks facing the professional garment designer, pattern cutter and grader are eliminated. This enables a greater throughput of styles in a shorter time period and an effective response to the competitive market demands. However, even with the recognized benefits of CAD, the number of UK clothing companies which use the technology is still small. In fact a survey of the UK clothing industry performed by the Design Council in 1990 shows that approximately 5 per cent of all UK clothing companies are using the technology[6]. This slow uptake in the past has been attributed to the high cost of CAD equipment, when the technology was in its infancy. However, in recent years rapid advances in computer technology have brought about a rapid decrease in price of both computing equipment and CAD systems, so that now a basic pattern design and lay planning system is well within the budget of most clothing manufacturers. Despite the benefits of CAD and reduced equipment costs penetration into the industry is still somewhat slow. Further reasons for the slow uptake of the technology have been reported by Wilkinson[7]; these were, in order of importance: (1) shortage of computing and electronic expertise; (2) shortage of appropriately skilled labour; (3) difficulties in converting existing staff; (4) shortage of specialized advice from equipment suppliers; (5) cost of training. In summary, it is apparent that the UK clothing and textiles manufacturing industries recognize the importance of introducing new technology and more specifically CAD. However there is a significant hesitation displayed by a large proportion of the industry, when considering an investment. This has been attributed to the lack of computing expertise and appropriately skilled operators. The need for training At the present time, there is a lack of independent training in the use of clothing CAD/CAM (computer-aided manufacturing) systems for professional designers and pattern cutters. It would appear that CAD training exists in the main as part of a system purchase. These courses which are run by the system vendor
consist of five to ten days’ tuition and have the specific aim of training the company to be proficient with the system in the shortest period of time. The lack of independent training facilities for industry has been reported in the literature. Taylor[8] writes, “There is a demand for training in the use of CAD systems by individuals, pattern cutters, graders and designers who are already in industry and who wish to equip themselves for the new positions that are rapidly becoming available. CAD systems suppliers only train employees of the purchaser”. Machin (cited in[9]) adds, “To promote the use of CAD in the clothing and textile industries colleges, companies and manufacturers must all work together to encourage an awareness of systems and provide easier access to them”. Shoben[10] states that as CAD/CAM assumes greater importance to the clothing industry the need for the pattern technologist becomes apparent. This training requirement led to the development of an introductory training course, “The beginners course in garment CAD” at De Montfort University, Leicester. Aimed specifically at professional designers, pattern cutters and manufacturers, the course complements the type of independent CAD training which is available to other professions. Garment CAD training at De Montfort University The “Beginner's guide to garment CAD” was developed as an introduction to the use of CAD systems for fashion illustration, pattern design, grading and lay planning. The course was taught as a series of practical sessions, over five days or eight evenings, to small groups of designers and other clothing professionals. The importance of practical experimentation was emphasized throughout, with ample opportunity provided for the participants to try out the systems for themselves. In the early stages of the course example sheets were used to provide a structure for investigations; however as the course progressed participants were encouraged to work with their own patterns. The Investronica suite of CAD software was used throughout the course, but the course material was impartial with respect to system supplier, with information provided on all leading systems. The training programme ran for one year; nine courses were held with a total of 22 delegates attending. The designer study To investigate reactions to CAD, all the designers and pattern cutters who attended the training course were asked to take part in an attitude study. The use of attitude studies in computing is not new; extensive work has been performed in this area over the last 40 years, by, for example, Breakwell et al.[11] Gardener et al.[12] Yang[13]. However, research into the use of CAD for fashion design and pattern cutting is still very limited. Aldrich[14] investigated the attitudes of final year BA Fashion Design students to CAD using observation methods and a personality questionnaire. However, as these students have little
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industrial experience it is unlikely that their views are representative of practising professionals. In addition to studying the attitudes of the group as a whole the influence of the background variables was investigated. Generally, it is accepted that, when learning or contemplating learning a new system, background variables can influence attitudes. Operator age in particular has been one variable that has received a lot of attention. It is a popular hypothesis that older workers are more resistant to technological change. However Majchrzak et al.[3], when reviewing implementations of CAD systems for mechanical engineering applications, suggest that age is not directly related to resistance to technical change. As such, this conflict with popular theory provided an interesting area for investigation. Other background variables which have proved influential in the past include gender, previous computer experience, personality traits and these were also considered for investigation. Acknowledging this research and considering the limited size of the survey it was decided to test two variables, age and past computing experience. To summarize, the specific aims of the research at De Montfort University were to: ● investigate the reactions of a group of professional pattern cutters and designers towards the use of a computer-aided design system; ● investigate whether a designer’s age or previous computer experience was influential in the forming of attitudes. The survey method Questionnaire design A questionnaire was designed to obtain a quantitative assessment of trainees’ attitudes to CAD after their initial five-day training period. The questionnaire comprised two sections. The first section posed questions pertaining to the subject’s background, these being age, sex, job title, and details of any computing experience prior to the course. This was followed with an attitude assessment section in which the subjects were asked to respond to a series of statements relating to the use of CAD as a garment design tool. A seven-point Likert-type scale was used to catagorize the responses. These categories were described “strongly disagree”, “disagree”, “tend to disagree”, “undecided”, “tend to agree”, “agree”, “strongly agree” and were ranked –3 to +3 respectively. The subject selected a zero score when undecided. A seven-point scale was used instead of the more usual five-point scale to provide more flexibility for responses. Care was taken in the wording for the statements with an equal number of positively and negatively worded items. A total of 20 statements were presented to the subjects and these were classified subjectively into three groups – personal issues, task-related issues, training and operational issues: (1) Personal issues. These statements were designed to elicit the subjects’ personal evaluation of CAD and, specifically, to ascertain whether they
were enthusiastic or apprehensive about using CAD systems, whether it was considered to be a worthwhile tool or whether there was a reluctance to a change in working methods.? Was CAD perceived as adding skill, providing more job satisfaction and ultimately improving job prospects? Computer anxiety was thought to be a key issue here, with those subjects unfamiliar with computing systems apprehensive of the implications of CAD to job security. (2) Task-related issues. These factors investigated how actively effective the subjects thought CAD was as a design tool. Did the subject consider CAD to be quicker than the traditional methods? Would using CAD result in greater productivity, reduce the time spent on repetitious tasks, enable the designer to meet shorter lead times and improve the quality of the product? (3) Training and operational issues. These statements focused on the performance of the CAD system. As the subjects had only used CAD for a five-day period it was apparent that they needed more experience of using a system before being able to answer in-depth questions on operational characteristics, therefore this section concentrated on initial impressions and attitudes towards the training process. The subjects were asked to rate the ease of use and versatility of CAD and the degree of frustration they experienced while using CAD. Other factors in this section asked the subjects to indicate whether they wanted more training, whether they felt it would be beneficial to be experienced with computing techniques and whether they thought it would take a long time to become competent in the use of CAD. Questionnaire administration The questionnaire was administered to the nine professional garment designers and pattern cutters who attended the “Beginners guide to garment CAD” training course at De Montfort University. The survey was subject to several controls: ● All subjects had no prior experience of CAD before the course, although some had used other computing applications. ● The questionnaire was administered on the final day of the course. ● Subjects had no prior knowledge that they were to be surveyed. Results Analysis of the results Descriptive statistics were used in the initial investigation of the results. To obtain a measure of location and dispersion of the group response to each statement, the sample mean and corresponding 95 per cent confidence interval for the population was calculated. Although the construction of a confidence interval assumes an underlying normal distribution, it was felt that some
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measure of variability within the group would be useful at the descriptive stage. Initial checks for bimodality in the data had been conducted with negative results. The main analysis considered the effect of background variables on attitudes towards CAD. Two separate statistical tests were performed to investigate whether there was a relationship between the subjects’ age or previous computing experience and subsequent attitudes to CAD. The following null hypotheses were tested: H01: There is no correlation between subjects’ ages and their attitudes to CAD. H02: A subjects' previous computing experience had no effect on their attitudes to CAD. To test the first null hypothesis (H01), a correlation analysis was performed. The scores recorded for each statement were plotted against the subject’s age in years and the correlation coefficient determined. If there was no correlation between these two variables, then the correlation coefficient for the population, ρ is zero. However, because of the sampling process the corresponding correlation coefficient r for the sample is rarely zero. The normal distribution is used to derive the critical value of r when ρ = 0. Hence for a sample size of 9, testing at a 95 per cent significance level the critical value for positive correlation is r > 0.666 and for negative correlation r < –0.666. To test the second null hypothesis (H02) the group was divided into two subgroups; those subjects with no prior computing experience and those subjects who had some general computing experience. It should be noted that none of the students had prior CAD experience. The mean response to each questionnaire statement for each sub-group was calculated and statistical methods were used to determine whether there was a significant difference between sub-groups. The small size of the sub-groups meant that normality could not be assumed and that it was necessary to use non-parametric methods, with the Mann Whitney U test being appropriate. Group response to CAD The results of the descriptive analysis of the data can be seen in Figures 1-3. Considering responses to the personal attitudes to CAD in Figure 1, it is apparent that on average there is a positive response to all statements, with “CAD experience improves job prospects” having the highest mean over all categories of statements. Further high scores were recorded for “CAD is worthwhile”, “Enthusiastic about CAD” and “CAD would add skill”. These results imply that the group was receptive to CAD and was aware of its potential. The corresponding low variabilities recorded for these statements also showed that the group were in agreement. Lower scores were recorded for “CAD would increase job satisfaction”, “Keen to use CAD” and “Confident about using CAD” revealing that there is some hesitation about actually using a CAD system.
–1
–2
CAD improves product quality
CAD enables shorter lead times
CAD reduces repetitious tasks
The designer would be more productive with CAD
CAD would improve job prospects
CAD would increase job satisfaction
CAD would add skill
Eager to use CAD
Confident about using CAD
Enthusiastic about CAD
–2
Traditional design skills are needed
CAD is worthwhile
–1
CAD is affordable
Rating scale
Rating scale 4 4
3 3
2 2
1 1
0 0
–2
3 3
2 2
1 1
0 0
–2
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–1
Figure 1. Mean response to personal issues (95 per cent confidence interval)
–1
Figure 2. Mean response to task-related issues (95 per cent confidence interval)
0
–1
–2
Figure 3. Mean response to operational issues (95 per cent confidence interval)
It would take a long time to become competent CAD user
0 Would like more training
1
Computing skills are not required to use CAD
1
CAD is satisfying to use
2
CAD stimulates ideas
2
CAD is quicker than traditional methods
3
CAD is more versatile than traditional methods
3
CAD is easy to use
48
Rating scale
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–2
Evaluation of the task-related factors shown in Figure 2 revealed a positive mean response for all statements except “CAD is affordable”. In the latter case the group were unsure that a CAD system would be financially viable. “Traditional skills are needed” had the highest mean response. This statement also had the smallest confidence interval of all, implying that the group were in agreement. Positive means were recorded for the other task-related statements, although the corresponding high variabilties provided evidence that there were differences of opinion within the group. Both positive and negative means were recorded for the statements in the training and operational category (Figure 3). The greatest positive mean was recorded for the statement, “Would like more training”. This statement had also a low variability indicating a strong agreement in the group. The following statements: “CAD is easy to use”, “CAD is satisfying to use” and “Computing skills are not required to use CAD” recorded negative means. Inspection of the confidence intervals indicated differences of opinion within the group. The remaining statements in this category recorded positive means but again displayed high variability indicating differences of opinion in the group. The influence of background variables The results of the two tests for this section are shown in Tables I and II; significant results are highlighted.
Questionnaire statement
Correlation coefficient r
Personal issues CAD is worthwhile Enthusiastic about CAD Confident about using CAD Eager to use to CAD CAD would add skill CAD would provide more job satisfaction CAD would improve job prospects CAD is affordable
–0.611 –0.589 0.032 0.013 –0.148 –0.193 –0.408 –0.480
Task-related issues Traditional design skills are needed The designer would be more productive with CAD CAD reduces repetitious tasks CAD enables shorter lead times CAD improves product quality
0.335 –0.156 –0.224 –0.038 0.271
Training and operational issues CAD is easy to use CAD is more versatile than traditional methods CAD is quicker than traditional methods CAD stimulates ideas CAD is satisfying to use Computing skills are not required to use CAD Would like more training It would take a long time to become competent CAD user Note: * Significant differences between subgroups at p = 0.05
–0.763* –0.195 –0.874* –0.162 –0.611 0.114 0.419 –0.005
The designer’s age The results in Table I indicate that two of the statements revealed a negative correlation (r < –0.666). These statements, “CAD is easy to use” and “CAD is quicker than traditional methods” indicated that younger subjects found CAD easier to use and potentially quicker than traditional methods, compared with the older subjects. For the remaining statements the first null hypothesis (H01) was not rejected. Prior computing experience The Mann Whitney U test indicated that only three out of the 20 statements tested produced significant differences in attitudes between the two computer
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Table I. Correlation between subjects’ age and their response to the questionnaire statements
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Mean response for each subgroup
Questionnaire statement
50
Previous computing experience
Probability of exceeding test statistic, Z with H0 true
1.33 1.50 –0.33 –0.17 1.83
3.00 3.00 1.00 2.67 3.00
0.05* 0.27 0.08 0.89 0.05*
0.33 2.17
1.67 3.00
0.20 0.22
–1.17 2.17
0.67 2.33
0.15 0.78
1.16 –0.17 1.17 0.33
2.23 2.67 2.67 1.00
0.23 0.08 0.11 0.69
–0.50
1.67
0.15
2.33
0.11
–0.50 1.16 –1.16
2.67 1.67 0.67
0.03* 0.59 0.18
–0.67 2.33
–0.33 1.67
0.78 0.41
Personal issues CAD is worthwhile Enthusiastic about CAD Confident about using CAD Eager to use to CAD CAD would add skill CAD would provide more job satisfaction CAD would improve job prospects Task-related issues CAD is affordable Traditional design skills are needed The designer would be more productive with CAD CAD reduces repetitious tasks CAD enable shorter lead times CAD improves product quality Training and operational issues CAD is easy to use CAD is more versatile than traditional methods CAD is quicker than traditional methods CAD stimulates ideas CAD is satisfying to use Computing skills are not required to use CAD Would like more training It would take a long time to become competent CAD user
Table II. Mann Whitney U Test – previous computing experience subgroups
No previous experience
0
1.00
0
0.42
Notes: H0, the null hypotheses state that the responses from each subgroup come from the same population *Significant differences between subgroups at p = 0.05
skill levels, with one further statement approaching the p < 0.05 significance (see Table II). Two of the statements that indicated significant differences between the groups were from the evaluative or personal issues section. The statements “CAD is worthwhile” and “CAD would add skill” revealed that previous computing experience was influential in forming a more favourable attitude towards CAD. It is evident that this prior knowledge had provided these users with an insight into the potential of CAD. A further factor, “Confident about using CAD”, which recorded a 0.08 significance level, provided the same insight. Another significant difference between the groups was found for the statement “CAD is quicker than traditional methods” from the training and operational issues section. Again, the experienced users responded in support of CAD, showing that these users were more appreciative of the efficiency of the new system after their initial training period. Discussion The attitude survey reported here presents one of the first quantitative attitude profiles of professional designers and pattern cutters to CAD. The group as a whole was supportive about the benefits of CAD both from a personal and task-related viewpoint. CAD was recognized as a way in which personal skill levels could be increased, which would improve job satisfaction and ultimately job prospects. Although the group recognized the advantages that CAD can offer it was not seen as an affordable technology. The designers taking part in the study were all employed by small manufacturing enterprises, none of which had CAD facilities. This lack of exposure to computing appears to have given the impression that a basic CAD system was beyond the budget of the small manufacturer. A fear that often surrounds new technology is that it will be seen as a job replacement and not as an aid to productivity. Clearly this was not the case for the group tested, who felt strongly that their traditional skills were needed. The group recognized that the CAD system could not replace human expertise but in fact complemented their normal working methods. This is in agreement with Brödner’s[15] comment that, “It should not be the computer but the computer-aided craftsman who emerges as the new hero of production”. After the short training period the group as a whole returned positive feedback with regard to the potential of CAD, although there did appear to be some difficulties experienced in learning how to use the system. This was not unexpected; indeed CAD suppliers quote that when training new users, competence is achieved gradually over a six-month period. The second part of the analysis involved a study of the designers’ perception of CAD on an individual basis: specifically to investigate whether the designer’s age or previous computing experience affected their attitude towards CAD. It was found that in answer to most statements, there was no evidence that these factors affected attitudes. Hence, for example, it would appear that the
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designer’s age and prior computing experience had no influence on their willingness to use CAD, or their opinion on how CAD would improve product quality and enable them to be more productive. However, in each test there were a small number of significant results and therefore to comment that age and prior computing experience do not have any influence on attitudes to CAD may not be completely true. When considering if “CAD was quicker than traditional methods” and “Easy to use” there was a correlation between age of the designer and their response to these statements. The younger designers were apparently more appreciative of time savings that can be achieved when using CAD. Whether this difference of opinion is directly due to the age of the operator is a question for debate. The result tends to agree with previous work by Majchrzak et al.[3] and Czaja et al.[16], which reports that although older workers often appear to be more resistant to change, it may not be the operator’s age that is the important factor. As older workers have a great deal of expertise and experience in their particular profession they are extremely adept at using conventional methods and when exposed to a new system, it is common for them to express a reluctance and reject the new method, feeling that it will be detrimental to their efficiency. Hence it may be that design experience is the key variable to test and not the designer’s age. This is an area for further study at a later date. A designer’s previous computing experience was a significant factor in three of the statements tested. Here the designers with prior computing knowledge, irrespective of application, were in support of using CAD. Again this result is to be expected; these designers, by already having overcome the initial apprehension often associated with learning a new skill, were in fact at a more advanced stage in the learning process and so adapted to the new method more readily. The study reported here opens up avenues for further investigations with a larger group of designers. It was apparent from this study that the background variables were not tested in isolation. For example most, but not all, of the younger designers had some computing experience, whereas the older designers were, in most cases, unfamiliar. A larger study could be envisaged where the influence of combinations of background variables could be tested. Conclusion The review of the CAD literature reveals the need for independent CAD training in the area of fashion design and pattern cutting. Other professional design occupations are well served with training centres, and competitively priced courses are widely available. In an attempt to fill this gap, the series of introductory training courses held at De Montfort University proved a very successful awareness exercise, dispelling some preconceptions concerning the technology and providing the companies which attended with an impetus to invest in and utilize CAD.
The attitude study provides a quantitative measure of professional garment designers’ and pattern cutters’ attitudes towards CAD. The outcome of the survey showed that the group of designers who took part were positive about using CAD. Further, it was found that the designers’ age or prior computing experience did not appear to influence their general opinion of CAD, although some differences of opinion were recorded. In these cases the younger designers and those designers who had prior computing experience were in favour of CAD. The overall favourable attitudes towards CAD appear to coincide with the industry’s general perception of the technology. It is thus an area for further research to determine why a larger proportion of garment manufacturers are not taking advantage of CAD techniques. References 1. Bradley, G., Steere, M. and Stromqvist, G., “Engineers using computer aided design and knowledge based systems: changing professional roles”, International Journal of Human Factors in Manufacturing, Vol. 1 No. 3, 1991, pp. 221-32. 2. Burnes, B. and James, H., “Human factors the need for a consistent strategy”, International Journal of Human Factors in Manufacturing, Vol. 2 No. 1, 1992, pp. 67-79. 3. Majchrzak, A., Chang, T., Barfield, W., Eberts, R. and Salvendy, G., Human Aspects of Computer Aided Design, Taylor and Francis, Philadelphia, PA and London, 1986. 4. Wilson, J.R., “Critical human factors: contributions in modern manufacturing”, International Journal of Human Factors in Manufacturing, Vol. 1 No. 3, 1991, pp. 281-97. 5. Zeitlan, J., “The UK clothing industry in transition”, Textile History, Vol. 19 No. 2, 1988, pp. 211-38. 6. Gray, S., The Benefits of Computer Aided Design, The Design Council, London, 1990. 7. Wilkinson, F., Survey of the Leicestershire Knitwear Industry, a report commissioned by Leicester City Council, 1992. 8. Taylor, P., Computers in the Fashion Industry, Heinemann, London, 1990. 9. Aldrich, W.M. (Ed.), CAD in Clothing and Textiles – A Collection of Expert Views, BSP Professional, Oxford, 1992. 10. Shoben, M., “Clothing craft skills in the 90s”, Apparel International, No. 1, 1993, pp. 30-1. 11. Breakwell, G.M., Fife-Shaw, C., Lee, T. and Spencer, J., “Occupational aspirations and attitudes to new technology”, Journal of Occupational Psychology, Vol. 60, 1987, pp. 169-72. 12. Gardener, E.P., Young, P. and Ruth, S.R., “Evolution of attitudes towards computers: a retrospective view”, Behaviour and Information Technology, Vol. 8 No. 2, 1989, pp. 89-8. 13. Yang, Y., “Survey steered design: evaluating user recovery and command reuse support by questionnaire”, Behaviour and Information Technology, Vol. 8 No. 6, 1989, pp. 437-59. 14. Aldrich, W.M., “New technology and clothing design”, PhD thesis, 1990. 15. Brödner, P., “In search of the computer aided craftsman”, A.I. and Society, Vol. 3, pp. 39-46. 16. Czaja, S.J., Hammond, K., Blascovich, J. and Swede, H., “Age-related differences in learning to use a text editing system”, Behaviour and Information Technology, Vol. 3, pp. 309-19.
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Communications An analysis of the apparel patternmaking process
54 Received March 1995 Accepted July 1995
Thong-Hwee Koh and Eng-Wah Lee Gintic Institute of Manufacturing Technology, Singapore, and
Yong-Tsui Lee Nanyang Technological University, Nanyang, Singapore Introduction Pattern making is the process of transforming a fashion design into its constituent flat pattern pieces and then drafting them out. In recent years, several research efforts have been focusing on computerizing the process[1-4]. As reported[1-3], the garment is designed in three dimensions before being unfolded into flat pattern pieces. These three-dimensional (3D) pattern design systems have computerized human models, and their approach is mainly based on fitting the human torso without the four limbs. They are presently trying to model geometrically and visualize a limp and elastic fabric in three dimensions. Subsequently, they also have to map this fabric from the 3D space onto the 2D space. A number of ways have been devised to achieve this. Moreover, they are also trying to address the problem of modelling ease (The amount added to measurements so that there is room to move and breathe in the finished garment. This amount varies according to both the garment type and the fashion style.) and the wide range of fashion features available[2]. On the other hand, Stein and Magrab[4] presented an expert system to interpret stored sets of pattern maker’s instructions for modifying patterns. This system requires a pattern maker to record a sequence of steps for changing men’s jackets from stored pattern pieces. It is among the first systems to attempt to codify existing pattern-making rules on a computer. Process definition The job of a pattern maker is to interpret the fashion designer’s illustration into sample pattern pieces and draft them out. Pattern pieces represent a piece of garment in sections and they contain information such as seam and hem allowances, grainline, size, balance marks, placement for buttons,
International Journal of Clothing Science and Technology, Vol. 7 No. 4, 1995, pp. 54-64. © MCB University Press, 0955-6222
The authors wish to thank Professor Ding-Yuan Liu and Professor Jia-Ye Wang of Gintic Institute of Manufacturing Technology and Masjuri bin Maswan of the Institute of Technical Education, Singapore, for their input to the work reported in this article.
Communications
Pleat backing Cut 1
Skirt front cut 2 Stitch to here
Pleat fold
Centre front
buttonholes, pockets[5]. Figure 1 shows an example of a garment and its pattern pieces. The pattern pieces are subsequently sewn together to obtain a sample garment, a prototype of that fashion illustration, for verification. This process of interpretation is achieved by applying rules and procedures, collectively known as pattern-making rules, mostly acquired through years of experience. All pattern pieces are modified or derived from their respective basic blocks. A basic block or sloper is a plain, flattened, outlined area which represents the respective shape of one’s body dimensions, for example, the sleeve block and the skirt block. It has only darts to make it fit the bumps and hollows of the body but no other fashion feature, and it is the basis from which the desired pattern pieces are developed[6]. A basic block is usually without seams, since seam allowances can sometimes interfere with proportioning and developing design variations[7]. Most fashion designs are made into garments from these basic blocks. Different types of garment can have different fashion features. In accordance with the definition of “feature” by Shah[8], a fashion feature bears the engineering meaning of the geometry of a part of a garment. It is a physical constituent of the garment and can be mapped to a generic shape. It has predictable properties and is of engineering significance. Examples of fashion feature include pleats, darts, design lines (also known as lines of illusion[6]), sleeve in raglan style, sleeve in regular style, long sleeve, short sleeve, no sleeve, collarless bodice, flare skirt, zipper fly opening, tapered trousers and so on. That is, anything that affects the geometry of a garment is considered a fashion feature. Consequently, a fashion style can be defined as the appearance of a piece of garment obtained after a combination of fashion features have been applied to it. All fashion features either partially affect the shapes and dimensions of their basic blocks, or have markings on the blocks to indicate the positions of the features. They can also give rise to facings and other attachments which in turn translate to additional pattern pieces (Figure 2).
55
Figure 1. Pattern pieces of a garment
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56 Figure 2. Cross-pocket fashion feature
Fashion feature
Cut
Pocket Pocket edge facing facing pattern pattern
Pocket bag pattern
Front panel basic block
Additional pattern pieces
Therefore, the presence of a fashion feature in a fashion design can affect the relevant basic blocks in three ways: (1) It can affect their shapes and physical dimensions. (2) It can create markings on them to locate its positions. (3) It can create additional pattern pieces in its implementation. As a result, every fashion feature in a garment type has its pattern-making rules which specifically state how it affects the drafting of the needed pattern pieces. Pattern designing is the creative process that refers to all the stages of folding, cutting, copying, modifying and experimenting with the relevant basic blocks to obtain the first set of pattern pieces that reflects the fashion design illustrated. On the other hand, pattern drafting refers to the measuring and drawing of the final pattern pieces. Pattern makers group their rules of interpretation according to the types of garment. Owing to the diversity and proliferation of such rules, each pattern maker normally specializes in making only a few types of garment. A pattern maker will classify a new fashion design into the relevant garment type so as to apply its known set of pattern-making rules to obtain a close intermediate style. Fashion features are then added, modified or deleted from this intermediate style to get the final design. Pattern making is thus a three-step process that covers fashion analysis, pattern design and pattern drafting. In the analysis process, a new fashion design is decomposed into an intermediate style (which closely resembles this new design) and the new fashion features needed through appropriate classification of that garment type (Figure 3). Pattern-making rules are then applied to subdivide this 3D garment into sections that are more easily unfolded into 2D pattern pieces. Subsequently, these pattern pieces are modified to take into account those new fashion features in the design. Only then are the pattern pieces drafted out. The intermediate style is made up of several sections of a garment that can easily be reduced to their secondary forms (Figure 4). The pattern pieces of these secondary forms, known as secondary pattern pieces, usually have only
New fashion design
Close immediate style
Communications
Fashion features
Fashion analysis
Garment section
57 Secondary pattern pieces
Therefore new fashion design
Secondary pattern pieces
Fashion features
Modified pattern pieces
Pattern design
Figure 3. The pattern-making process
Pattern drafting
the essential features of darts, pleats, flares and cuts added to the basic blocks to give the required shapes of those garment parts. As a result they are still flexible enough to be used to generate slightly different fashion styles for that particular type of garment. Hence, to speed up the pattern-making process, some fashion designs can be better adapted from their secondary pattern pieces, instead of from their basic blocks. These are usually designs that have only minor variations in their fashion features from those of the corresponding secondary forms. Form number 1
Flats number 1
Form number 4
Flats number 4
Form number 2
Flats number 2
Form number 5
Flats number 5
Form number 3
Source: [9]
Flats number 3
Form number 6
Form number 7
Flats number 7
Form number 8
Flats number 8
Form number 9
Flats number 9
Flats number 6
Figure 4. The secondary pattern pieces of some secondary forms
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Although fashions come and go, the principles of pattern-making do not change. Pattern pieces are always designed and drafted following the same rules and methods, regardless of the current length or looseness. General principles of pattern making The principles of pattern-making can be divided into two main categories, namely, the general and the fashion-specific. The former can be used for any garment design and they are: ● Truing. This is the process of connecting all points on a pattern piece and checking for accuracy of the physical dimensions, dartlines, seamlines, balance marks, shape of seamlines, etc.[5]. ● Close and cup. This is used when truing darts. One dartline is creased. The creased fold is matched to the opposite dartline and the dart is pinned closed. The pattern piece is folded under at dart point, and the seamline crossing dart is then trued[5]. ● Moving darts. Two principles may be applied in moving darts. In the pivot principle, a dart can be moved around provided it still runs from the original dart point to any other edge of the pattern piece[6]. In the transfer principle, a dart is moved while keeping to the same pattern edge, or when there is no pivot point. A dart can also be divided, with a part in one position and the rest in another. (Moreover, it can be stitched as tucks or even as gathers if preferred; the shaping produced will remain the same. A dart is thus transformed when the aesthetic or functional need arises in the design.) ● Cut and spread. Additional width in a pattern piece can be introduced by cutting it, or the copy of the block, and spreading it to the required width[6]. When an even amount is added right through the pattern piece, it is known as an even insertion. To add flare to the pattern piece a wedge-shaped insertion can be added instead. Moreover, if the insertion is to be uniform right across then the pattern piece can be cut in several places. It is often used to create fashion features like darts, pleats, tucks, shirrings and gatherings. ● Cut and overlapped. The edge of a pattern piece can be shortened by cutting to the opposite edge and overlapping to remove a wedge shape[6]. Alternatively, the pattern piece can be cut part of the way and then horizontally to each edge. As before, overlapping is done in order to outline the new shape, curving the edges and maintaining the same grain as the original. ● Introducing seams. Additional functional or decorative seams simply added may look plain especially if they are straight[6]. Hence, they are usually slightly curved and, if possible, some shape is introduced by putting a dart or part of a dart into one end. Pattern pieces are always made slightly wider at the hem or, if horizontal, deeper at centre-front to keep the folds flatly pressed together.
Eliminating seams. Two adjacent pattern pieces with straight edges Communications between them can be joined together to make a new pattern piece provided the grain on the final shape is acceptable. A new seam would often be inserted not too far away. If there is any shaping between the two edges, it can be made into a dart[8]. ● Balancing. Balancing is the process of matching the two sides of a seam 59 for position of grainline, length of seam and amount of flare or fullness introduced[5]. ●
The pattern makers usually apply these general principles of pattern making with the other more familiar drafting operations in the course of their work. These operations are the basic geometric operations for creating, manipulating and modifying an object in 3D space, such as drawing, moving, rotating, duplicating, subdividing, scaling up or down a flat shape. Incidentally, these operations (including the general principles of pattern making) have been incorporated in the pattern design subsystems[10] of some commercial apparel CAD systems. Fashion specific pattern-making rules The principles of pattern-making that are fashion-specific are numerous and they are closely linked to the type of garment to be produced and even to the style for each of the types. Pattern-making rules are used to transform the secondary forms into their secondary pattern pieces as shown in (A) in Figure 5. Rules are also required during fashion analysis to classify a fashion design Pattern-making rules (B)
New fashion design
Close intermediate style
Fashion features Pattern-making rules (A)
Garment section Pattern-making rules (A) Secondary pattern pieces
Therefore new fashion design
Secondary pattern pieces
Modified pattern pieces
Fashion features
Figure 5. Application of patternmaking rules
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into the appropriate intermediate style and the corresponding fashion features as in (B) in Figure 5. The set of pattern-making rules at (A) can be further subdivided into four other categories. They are: (1) Rules for designing a pattern piece according to standard body forms. These rules have been articulated often in pattern-making books[57,11,12]. They are the step-by-step instructions for designing and drafting the desired pattern piece for a particular garment style. (2) Rules for checking compatibility between related pattern pieces. These rules are required for checking that adjacent pattern pieces can be sewn together to give the garment the expected look. They can be from pattern-making books but most of them are more likely to be heuristic in nature. These heuristic rules are normally formulated through practice and experience. An example is the rule that specifies for shirt: Length of crown = Armhole + Armhole + Ease (Sleeve) (Back bodice) (Front bodice) (e.g. 1cm) (3) Rules for modifying a pattern piece for figure faults. These rules are more often used for custom tailoring, making adjustments for people whose body proportions are slightly out of the average. Examples include hunch-back, erect, corpulent, sloping shoulder, and bowleggedness, for which special compensations are necessary[11]. (4) Rules for modifying related pattern pieces which are mutually incompatible. These rules are for adjustments made on pattern pieces that have failed the rules in (2) above. They specify how incompatible pattern pieces are to be corrected so that they can fit in with the rest of the garment. They are formulated through practice and experience and are, thus, heuristic in nature. Finally, the last category of pattern-making rules, which is at (B), is: (5) Rules for modifying secondary pattern pieces for new fashion features. These rules are used to alter the pattern pieces from the intermediate style to incorporate new fashion features to them. They are mainly adapted from those rules in (1) above and, again, are formulated through practice and experience. Hence the application of apparel pattern-making rules of (A1), (A2), (A4) and (B5) are necessary for creating each pattern piece of any garment for mass production. On the other hand, all these rules of (A1), (A2), (A3), (A4) and (B5) are often necessary for every pattern piece of tailored garments. Instead of doing an exhaustive study on the pattern-making rules in each category, a study into the motivations behind the rules of each category has been done.
Factors affecting the pattern-making process Communications For each category of the pattern-making rules, there are several factors that need to be considered. These factors concern the properties (i.e. shape and other attributes) of an as yet non-existent garment to guarantee that it can be manufactured and that it will meet certain performance criteria, in terms of functionality and aesthetics. 61 (1) Rules for designing the pattern piece according to standard body forms. Factors: ● the main function of the pattern piece (or the part of the garment for which the pattern piece is for), e.g. front panel, sleeve, fly facing, and pocket bag; ● the shape of the pattern piece; ● the physical dimensions of the pattern piece. (2) Rules for checking the compatibility between related pattern pieces. Factors: ● the relationships between those affected pattern pieces; ● the shape of each of those pattern pieces; ● the dimensions, including ease and fit, of each of those pattern pieces; ● the physical correspondence between those pattern pieces, i.e. balance marks; ● the type of seams or sewing needs between those pattern pieces; ● the accuracy needed at each of those seams. (3) Rules for modifying the pattern piece for figure faults. Factors: ● the additional physical measurements needed from that human figure; ● the extra pattern pieces to be added or taken away (if any); ● the shape of each affected pattern piece; ● the physical dimensions of each affected pattern piece. (4) Rules for modifying related pattern pieces when they are incompatible with one another. Factors: ● the main function of those affected pattern pieces; ● the relationships between those pattern pieces; ● the shape of each of those pattern pieces; ● the dimensions, including ease and fit, of each of those pattern pieces; ● the physical correspondence between those pattern pieces, i.e. balance marks;
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the type of seams or sewing needs between those pattern pieces; ● the accuracy needed at each of those seams. (5) Rules for modifying secondary pattern pieces for new fashion features. Factors: ● the main function of those affected pattern pieces; ● the extra pattern pieces needed to be added or taken away (if any); ● the shape of each of those pattern pieces; ● the dimensions, including ease and fit, of each of those pattern pieces; ● the relationships between those affected pattern pieces; ● the markings (if any) needed to position each of the fashion features on those pattern pieces; ● the physical correspondence between these markings on those related pattern pieces; ● the additional functional needs to account for, e.g. pen-slot on the pocket flap (i.e. the new feature) of patch pocket on shirt; ● the design lines and other aesthetic needs. ●
Furthermore, the following factors pertaining to the characteristics of the fabric also need to be considered constantly while working with any of the above rules: ● the weight of the fabric, e.g. silk, wool, denim, cotton; ● the amount of stretch of the fabric and in which direction, i.e. along the grain, on bias, etc. ● the ease with which the fabric reverts to its original shape; ● the nature of its crease when the fabric is crushed; ● the type of weave used on the fabric, e.g. open, close, matt, shiny, smooth, hairy; ● the colour and print patterns of the fabric. Paper pattern pieces can never be a failure because the principles are laid down and the sequences are logical. However, a garment made from the pattern pieces will not necessarily be an unqualified success unless the nature of the fabric has also been taken into consideration[6]. Thus, developed sample pattern pieces must be tested for style, harmony of line, fit and proportion before using them to cut the garment[12]. Finally, these general factors need to be considered too while designing the pattern pieces: ● the garment manufacturing process requirements, e.g. stone-washing, bleaching;
●
the context of use for the garment, i.e. sports wear, underwear, casual Communications wear, formal wear, spring time, summer time.
All the factors listed above are consequently the focus of the pattern-making process. The objective of apparel pattern-making is thus to interpret a fashion illustration into working pattern pieces that not only capture the essence of the aesthetic look and functionality of the garment as a whole, but also address those factors of garment manufacturing and the fabric characteristics in order to realize the fashion design. Furthermore, these pattern pieces are developed through a set of procedures and operations that can be summed up in this hierarchy (Figure 6).
63
Discussion and conclusions This article explicitly categorizes the pattern-making process which hitherto has been largely based on rules of thumb and personal experiences of the pattern makers. In order that a working sample garment can be realized, the pattern-making process needs to be iterated constantly through these three steps: fashion analysis, pattern design and pattern drafting. It is in these alterations that the demands of the various factors affecting the final garment are gradually being met. Consequently, pattern-making principles or rules have been formulated, gradually through experience, to help to address these factors. They define the procedures required to develop a set of coherent pattern pieces given a particular fashion illustration. The final pattern pieces are obtained only after changes have been made to the appropriate basic blocks or secondary pattern pieces to account for these numerous factors of pattern design through those three steps. The hierarchy of pattern-making rules and operations derived here can be used in the design of a computer-aided pattern-making system. In fact a testbed system has been implemented based on this analysis[13]. Even in conventional pattern making, applying these rules more systematically at each stage of the process will yield higher quality flat pattern pieces. Moreover, it will reduce the time taken for the whole process itself. Pattern-making knowledge Men’s, ladies’, boys’, girls’ garment Shirt, pants, overalls, bermudas, dress, etc. Front part, back part, sleeve, collars, etc. Fullness, openings, pockets, yokes, facings, etc. Shape (cuttings), pleats, gathers, seams, etc.
Pattern makers only know their own categories Fashion-specific pattern-making rules All pattern makers know these
General principles of pattern making 2D geometric entities and operations
Trueing, close and cup, moving darts, cut and spread, cut and overlapping, introducing seams, eliminating seams, balancing
Figure 6. Hierarchy of patternmaking rules and operations
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References 1. Hinds, B.K. and McCartney, J., “Three-dimensional design of garments and automated pattern production”, The Application of Computers to Manufacturing Engineering (ACME) Directorate, Science and Engineering Research Council, Research Conference Proceedings, Sheffield University, Sheffield, 1993, pp. 2-7. 2. Ng, R., Chan, C.K., Au, R. and Pong, T.Y., “Computational technique for 3-D pattern design”, Textile Asia, September, Sheffield, 1993, pp. 62-4. 3. Okabe, H., Imaoka, H., Tomika, T. and Niwaya, H., “Three-dimensional apparel CAD system”, Computer Graphics, Vol. 26 No. 2, July 1992, pp. 105-10. 4. Stein, D.C. and Magrab, E.B., “Expert system for the design and manufacture of made-tomeasure clothing”, Manufacturing Review, Vol. 4 No. 2, June 1991, pp. 126-38. 5. Kopp, E., Rolfo, V., Zelin, B. and Gross, L., Designing Apparel through the Flat Pattern, Fairchild Publications, New York, NY, 1982. 6. Ladbury, A., Complete Pattern Designing, Sidgwick & Jackson, London, 1989. 7. Kopp E., Rolfo, V., Zelin, B. and Gross, L., How to Draft Basic Patterns, Fairchild Publications, New York, NY, 1984. 8. Shah, J.J., “Features in design and manufacturing”, in Kusiak, A. (Ed.), Intelligent Design and Manufacturing, John Wiley & Sons, New York, NY, 1992, pp. 39-71. 9. Willett, S., “Syntax and semantics of an image communications language for design management”, Design Theory and Methodology, DTM 1990, presented at the 1990 ASME design technical conferences – 2nd International Conference on Design Theory and Methodology, Chicago, IL, 16-19 September 1990, pp. 27-32. 10. Jo, J.S., “The development of an educational computer aided design system for garment manufacture”, PhD thesis, University of Leeds, August 1989. 11. Gan, P., Drafting, Cutting and Sewing in Dressmaking, Yen Yi Dressmaking School, Singapore, 1985. 12. Kopp, E., Rolfo, V. and Zelin, B., New Fashion Areas for Designing Apparel through the Flat Pattern, Fairchild Publications, New York, NY, 1972. 13. Koh, T.H., “Computer aided apparel pattern-making application”, Master’s degree thesis, Nanyang Technological University, Singapore, 1994.
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10 Received May 1995 Revised and accepted July 1995
Modelling the dynamic drape of fabrics on synthetic humans A physical, lumped-parameter model G. Stylios, T.R. Wan and N.J. Powell Department of Industrial Technologies, University of Bradford, Bradford, UK Introduction It can be argued that in globalization of the world markets competitiveness will depend on technological advances. The next generation of textile and garment manufacturing and automated retailing systems will need to predict the true 3-D behaviour of the fabric and garment design and wear. It is generally accepted that one of the most important requirements for the development of a 3-D garment computer-aided design (CAD) system is how to obtain the real shape of the garment in 3-D space from the original 2-D design patterns. It was realized that the deformable behaviour of textile materials would play a very important role in this area. Researchers have studied various methods to achieve this, such as the finite element method, the finite difference method, etc.; however, unlike other engineering areas, the successes of such modern methods are very limited in textile engineering. Commercial CAD systems for clothing are still not able to meet those requirements. The major task is to find a precise and efficient approach to determine the real 3-D deformed shape of a cloth according to real fabric properties and to deal with complex 3-D design patterns. Those solutions should be efficient in terms of computer processing time, and easy to use for practical industrial applications. In this article, we present a physical-based approach using the deformable bar-node and the lumped-parameter concept[1-3] to model complex deformation of fabrics. Our model is based on a physical analogue to deep shell system. The governing differential equations of motion and deformation of cloth are derived from the discretization of system energy over all rigid bar-deformable nodes on the surface of the cloth. Overview of related work Over the last two decades, in the textile engineering discipline, a great deal of effort has been made in modelling and analysing fabric deformation[4-7]. Although most of this work is concentrated on the relationship between the fabric structure and mechanical properties, not on the prediction of overall
International Journal of Clothing Science and Technology, Vol. 7 No. 5, 1995, pp. 10-25. © MCB University Press, 0955-6222
This project was conducted at the Center of Objective Measurement Technologies, which is supported by RETREX 1 project. The visualization tool has been kindly provided by ALIAS Inc.
shape of fabrics, it formed the basis for exploring and developing new approaches. One such important approach was reported by Shanahan et al.[4]. They concluded that a textile material displays a macrostructure (this may be defined as a kind of discontinuous structure when examining a very small piece of cloth) in terms of a measure of strain, since original displacement between the two element points can be made arbitrarily small, so that there is a lower limit to element size and, below this limit, the strain cannot be considered as being continuously distributed within the element. They also concluded that when the strains are finite, the definition and application of the stress-strain relationship present considerable difficulties, even for simple isotropic, elastic materials. They also pointed out that even for small strains, textile materials show viscoelastic behaviour and frictional slip and thus have a response which is non-linear, imperfectly recoverable, and time-dependent. Although they thought that it might not be likely to be practical to model a fabric in detail as an assembly of its constituent fibres or yarns, they presented a linear elastic theory of textile materials by treating fabrics as continuum sheets, as an initial framework for further consideration. Later, Lloyd first used the finite element method to study the deformable behaviour of fabrics and his application has shown some success[5]. However Lloyd concluded, in another report[6], that the finite element method was not naturally suited to the highly non-linear, large deformation of fabrics but rather to the simplest fabric deformation. Elastic theory, in contrast, handles larger deformations in a natural way. Lloyd’s conclusion was later contradicted by Amirbayat and Hearle[7], who have stated that the elastic theory of plates and shells is of little help in dealing with complex fabric deformation such as fabric buckling, and that the terminology and methodology are not easily applicable. Over the last decade, in the area of computer graphics, great effort has also been made to model the complex deformable behaviour of fabrics by using physical approaches. A successful physical-based model was first developed by Terzopoulos et al.[8] and Terzopoulos and Fleischer[9]. In principle, this model is based on the deformable behaviour of cloth, determined by a group of partial equations (constitutive), which are derived from Newtonian mechanics and the externally applied forces with the internal forces due to the movement and the deformation of cloth in a Lagrangian form. The ability to control deformable shapes is determined by parameters derived from differential geometric consideration. Any deformation of cloth is judged by observing the difference of metric tensor and the curvature tensor of the cloth surface compared to their initial, unstrained form. By employing and extending this approach, many other successful projects have been reported[10-12], but their work was mainly presenting visual effects of an animated cloth. There are considerable difficulties in applying these approaches to making an accurate model of real textile materials. In addition, it is not easy to incorporate mechanical
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parameters employed in engineering, such as Young’s modulus and Poisson’s ratio, and the procedures for solving the model equations are computer intensive and not practical for industrial use. Later, Aono[13] presented a physical-based model by using elastic theory. It is claimed that this model can take account of damping factors, anisotropy factors and viscoelastic effects for both static and dynamic forces. One advantage of this model is that it uses engineering parameters directly. However, the formulation of this model was established in a 3-D Cartesian coordinate system (x, y, z) and derived on the basis of the fundamental assumption that neither tensile nor shear dilatation along the z axis exist in the cloth. This assumption can be interpreted as: (∂u/∂z) = (∂v/∂z) = (∂w/∂z) = 0, where u, v and w are displacements along the x, y and z axis respectively. Note that this model is determined in a Cartesian co-ordinate system it clearly indicates that the assumption made is only appropriate for shallow deformation cases of cloth and its application is therefore limited in dealing with complex cloth deformation and cannot be applied to the surface co-ordinate system[12]. Recently, Breen et al.[14,15] developed an interacting-particle model for producing draping simulations of woven fabrics. In their approach, the deformation of cloth is represented and determined by the locations of particles, which conceptually represent the crossing points of warp and weft yarns. The location of each particle is only connected and influenced by neighbouring particles through the definition of group energy functions. The simulation is implemented as a two-step process from a given initial condition. The first step only models the effect of gravity and collisions between the cloth model and surrounding objects. The second step enforces interparticle constraints and finds the local energy minimum position before continuing with the next process. This particle-based model can also incorporate the fabric test data by using piecewise polynomial functions to convert them to energy functions, although in their work they only considered the low deformation for shear and bending in the range of first curve segment (from zero deformation to maximum deformation, hence neglecting any hysterics or history dependent effects). In their reports, two kinds of tablecloths were simulated. It can be seen, from their implementations, that they have still a long way to go for practical use in garment design. Examples of this can be found in their circle tablecloth simulation, in which the tablecloth has a jagged edge, and in their square tablecloth simulation the simulated samples appear to have much more stiffness, as if being draped over round edges. One possible difficulty with this method is that the approach of conceptually representing the macrostructure of cloth may lead to problems in dealing with arbitrary or complex boundary conditions. Another difficulty is in the use of force-displacement relationships which lead to more difficulty in describing the accurate shape of deformed objects than in the case of using elastic continuum theory, which uses basic stress-strain
relationship. These procedures are also very time consuming. The implementation of the square and circular tablecloth simulation (50 × 50 grid) need one CPU-week of computation (on DEC station 3100 class workstation). Assumptions and considerations In our approach, fabric is assumed as a continuum shell. This is true when the basic elements of the cloth for stress-strain analysis are large compared with its macrostructure unit[4]. Locations of the points in the cloth can be defined by a curvilinear surface co-ordinate system α1, α2, and α3 and can also be defined by the Cartesian co-ordinate system (x, y, z) as shown in Figure 1. The relationships between the two co-ordinate systems can be given by: x = f1(α1, α2, α3), y = f2(α1, α2, α3), z = f3(α1, α2, α3).
Modelling the dynamic drape of fabrics 13
(1)
The position vector r of a point P0 in a fabric can be determined by: r(α1, α2, α3) = f1(α1, α2, α3) • e1 + f2(α1, α2, α3) • e2 + f3(α1, α2, α3) • e3 (2) where e 1, e 2 and e 2 are the unit vectors along directions of x, y and z axes respectively (Figure 1). Our approach is developed based on physical concepts through a discretized potential energy formulation. In principle, the formulation of the governing equations of this model can be briefly illustrated as follows: the surface of cloth can be divided as series of node-bar elements according to the mesh layout employed, where the elements can be equal or unique sizes. Here, for simplification, we limit our partition to orthogonal linear co-ordinates which coincide with the lines of the principal curvature of the surface. The deformable
α3
α2
z dr
p1
p0
r
α1 r + dr
x
y
Figure 1. Location of points in fabric
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node-bar elements are defined as consisting of one deformable node with a number of rigid bars, as shown in Figure 2. As can be seen in this Figure, the patch of cloth is divided into a grid. In this way, the patch is divided as a series of elements, which can be equal or unequal sizes. In this approach, we treat each patch element as the unit of a deformable node with a number of rigid bars. The deformation of each node-bar element can be described by u and v displacements, along the first two surface co-ordinates α1, α2 within the tangent plane, and w displacement along the third surface coordinate α3 (the normal direction). The material properties of the continuum in all elements can be lumped at these deformable nodes by integrating all the energies within those elements. Note that we chose the perpendicular lines of principle curvature as coordinates α1, α2, and the normal to the surface as the third co-ordinate α3; thus we have three dimensional strain and stress components. Assuming that the fabric surface is homogeneous, isotropic and linearly elastic on which Hooke’s law can be applied, we have[16]:
σ = {E} • ε. (3) In the expression above, σ is the stress vector and defined by: σ = [σ11σ22σ33σ12σ13σ23]T (4) where σ 11 , σ 22 , σ 33 are the normal stress along the directions of α 1 , α 2 , α 3 , respectively and σ12, σ13, σ23 are the shear stress within the tangential planes of ( α 1 , α 2 ), ( α 1 , α 3 ) and ( α 2 , α 3 ) respectively. (Note that for complete stress components, σ12 = σ21, σ13 = σ31, σ23 = σ32). ε is the strain vector and defined by: ε = [ε11ε22ε33ε12ε13ε23]T (5) where ε11,ε22,ε33 are the normal strains along the directions of α1,α2,α3 respectively and ε12,ε13,ε23 are the shear stress within the tangential planes of 3
W
V
2 0
4
u
Figure 2. Configuration of a node-bar element in fabric
1
(α1,α2), (α 1,α 3) and (α 2,α 3) respectively, and {E} is the relation matrix for isotropic material and defined by: 2G + λ λ λ 0 0 0 λ λ 2G + λ 0 0 0 λ λ 2G + λ 0 0 0 E = 0 G 0 0 0 0 0 0 0 G 0 0 0 0 0 0 G 0
{} where
λ=
(6 )
µE
(1 + µ)(1 − 2 µ)
is Poisson ratio, E is the tensile modulus and Gij is the shear modulus. It is generally known that the cloth is anisotropic and non-linear and the deformations are history-dependent. In order to incorporate anisotropic material properties, we expand the strain-stress relationship above as follows: 2G + λ λ2 1 1 λ1 2G2 + λ2 λ1 λ2 E = 0 0 0 0 0 0
{}
λ3
0
0
λ3
0
0
2G3 + λ3
0
0
0
G12
0
0
0
G13
0
0
0
0 0 0 . 0 0 G23
(7 )
We assume that all components in E are independent from one another. It can be seen that this treatment will not only be able to incorporate the real material properties of fabric but would also be convenient for using real measurements of fabric parameters. G1, G2 and G3 are tensile modulus along α1, α2 and α3 respectively, λ1, λ2 and λ3 are corresponding Poisson ratios, and G2 is the shear modulus within the cloth surface and G13 and G23 are shear moduli within the plane of α 1 and α 3, and the plane of α 2 and α 3 respectively, which can be evaluated by the measure of bending properties at both planes. Energy expressions and fabric model The general strain-displacement relationships in the surface co-ordinates described above are given[16] as follows:
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∂Ai u j ∂ ui ⋅ + , i = 1, 2, 3. Ai j = 1 ∂α j Aj ∂α i Ai The shear strain is : ∂ ui A j ∂ u j A ⋅ ε ij = i ⋅ , i , j = 1, 2, 3; i ≠ j + Aj ∂α j Ai Ai ∂α i Aj ε ii =
1
3
∑
(8 )
(9 )
where Ai2 =
∂r
⋅
∂r
∂α i ∂α i
, i = 1, 2, 3.
Consider the local area of fabric, since we limit our shell co-ordinate system to coincide with the lines of principal curvature of the surface and the normal direction and the deformation of each node-bar element can be described as the u and v displacements, which are along the first two curvilinear co-ordinates within the tangent plane, and w displacement along the normal direction. It is therefore reasonable to assume that, for local stress-strain analysis, the fundamental form parameters A 1 ≈ A 2 ≈ A 3 ≈ 1. The strain-displacement relationships are therefore simplified and ε can be evaluated by: ε = ∂u ∂α 2 ∂u ∂α 3 ∂v ∂α 3
∂u ∂α 1 ∂v ∂α 2 ∂w ∂α 3 + + +
= v – v 4 2 ∂v l ∂α 1 α 2 w – w0 ∂w 1 l 2 ∂α 1 α 1 w – w0 ∂w 4 l 2 ∂α 2 α 2
u1 – u3 lα 1 v2 – v4 lα 2 0 + + +
u1 – u3 lα 1 w3 – w0 lα 1 2 w2 – w0 lα 2 2
(10 )
where, ui, vi and wi are the displacements of the node i, as shown in Figure 2. It must be emphasized here that according to the above discussion, it would only be true to treat the fabric as being of continuum material if the basic fabric element sizes chosen for modelling are large, compared with the microstructure
of the fabric. However, this requirement is normally met by the selection of size of basic cloth elements. The cloth motion equations can be derived from the energy functions of the system as follows. Consider the deformation distribution within one basic fabric element. The question is how to find the strain energy density function or strain energy distribution. We assume that the virtual energy density of strain will change continually and smoothly within the basic fabric element. This indicates that the energy density function, which is related to stresses σij and strains εij, is continuous. We also assume that they have continuous derivatives everywhere within the basic element and that the resultant effect on the whole shape of fabric, especially at locations of each node, will be identical to the effect of using lumped node-bar element treatment (Figure 3). Under the above assumptions, we treat the shape of fabric as being a continuum shell. Now, we are ready to examine one infinitesimal element, as shown in Figure 3. The strain energy stored in an infinitesimal element within one basic fabric element that is acted by stress σij is: dU =
1
σT ε dV .
Modelling the dynamic drape of fabrics 17
(11)
2 The strain energy density is therefore:
Nodei
An infinitesimal element
Figure 3. An infinitesimal element in a basic fabric element
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χ1 =
1
χ2 =
1
σT ε. 2 The kinetic energy density is: ρ (u˙ 2 + v˙ 2 + w˙ 2 )
(12 )
(13 )
2 where ρ is mass density and the dot indicates a time derivative. The energy density introduced by body forces is:
χ3 = q1u + q2v +q3w
(14)
where q1, q2 and q3 are forces per unit volume. The energy density introduced by possibly applied boundary forces is:
χ4 = N1u + N2v + N3w
(15)
where N1, N2 and N3 are the resultants of the boundary forces. Hence, the total energy density is: L = χ1 + χ2 + χ3 + χ4.
(16)
It can be seen that we actually define an energy field. Using Euler-Lagrange equations[17], the cloth motion can be determined by 3 ∂ ∂L ∂ ∂L =∑ – (i = 1, 2 , 3 ). ∂ψ i k =1 ∂X k ∂ (∂ψ i / ∂uk ) ∂t ∂ψ i
∂L
(17 )
where ψ i are general functions of energy component, representing ui (i = 1, 2, 3). Using matrix notation ψ and X gives:
ψ = [uvw]T
(18)
and X = [α1α2α3 t]T
(19)
where t is the time variable. The detailed derivation of all terms in the fabric motion equations may be obtained in our internal report[18]. Viscoelastic properties In general, textile materials contain viscoelastic properties. In order to represent the behaviour of cloth precisely, the model must be able to incorporate the viscoelastic properties of those materials. At the current stage, we use the linear Kelvin model[19] for describing material viscoelastic properties. The stressstrain relationships are now given by: . σ = {E} ε + {β}ε (20) where
β 11 β21 β β = 31 0 0 0
{}
β12 β22 β32 0 0 0
β13 β23 β33 0 0 0
0
0
0
0
0
0
β12 0 0
0
β13 0
Modelling the dynamic drape of fabrics
0 0 0 0 0 β23
(21)
19
where components βi,j are damping terms, which may be estimated by various methods. . The terms of ε may be estimated as follows: ˙ε = ∂u˙ ∂α 2 ∂u˙ ∂α 3 ∂v˙ ∂α 3
∂u˙ ∂α 1 ∂v˙ ∂α 2 ∂w˙ ∂α 3 + + +
= v˙ – v˙ 4 2 ∂v˙ l ∂α 1 α 2 w˙ – w˙ 0 ∂w˙ 1 l /2 ∂α 1 α 1 w˙ – w˙ 0 ∂w˙ 4 l /2 ∂α 2 α 2
u˙1 – u˙3 lα 1 v˙2 – v˙4 lα 2 0 + + +
u˙1 – u˙3 . lα 1 w˙ 3 – w˙ 0 lα 1 / 2 w˙ 2 – w˙ 0 lα 2 / 2
(22 22)
Visualization implementation and discussion In order to verify our fabric model, two sets of drape simulations have been applied and implemented. One of them is a fabric drape simulation on the m3 system which is a computer control static and dynamic drape system, developed in our centre for testing and predicting the drape behaviour of fabric. The other is a simulation of the dynamic draping of a skirt, which is worn (attached to) by a synthetic lady. Drape prediction of fabric materials The Merylin Monroe Meter (m3)[20] was developed in the centre for testing and evaluating the static and dynamic drape behaviour of fabric. The drape behaviour of a fabric measured by the meter will depend on the mechanical
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Figure 4. Rendered images of drape simulation and actual photographs of fabrics A and B
properties of the fabric sample. It is of interest to develop a model to s imulate the real drape m3 system. It is expected that the successful model may be used for providing a prediction of the drape behaviour of real fabric samples by means of inputting their mechanical parameters to the model and that the draping behaviour will vary with any changes of those mechanical parameters. In the current work, the drape system is modelled using 720 deformable nodesbar elements. The initial states of fabric samples are defined as flat. During draping, the fabric will drape over a cylinder. Figures 4a and 4b show the final instances of the simulation using different fabric materials. Both materials are blends of cotton and polyester. The mechanical parameters were measured at the centre. The tensile properties of both materials are the same. The difference between them is that the bending properties of the material shown in Figure 4a is double of that of the material in
(a) A rendered image of drape simulation of fabric
(b) A rendered image of drape metre simulation of fabric B
(c) Actual photograph of fabric A
(d) Actual photograph of fabric B
Figure 4b. It is clear that the two simulations provide a very different drape behaviour. Figure 4a (fabric A) shows that the number of drape folds is four whereas Figure 4b (fabric B) shows that the folds are five. In order to make comparison between these simulations and the real world, we took the actual photographs from the real drape of fabrics A and B as shown in Figures 4c and 4d. It can be seen that there is a good agreement between the draping simulations and real drape of those fabrics. It should be stated that the drape simulation model may provide more detailed information than the real drape system m3, since it is able to provide the entire shape of the fabric.
Modelling the dynamic drape of fabrics 21
Drape behaviours of a skirt The skirt was modelled involving 1,440 (20 × 72) deformable node-bar elements, and was used to model a skirt over a synthetic human lady. The fabric is a blend of polyester and cotton. The material parameters, such as tensile and shear, were measured by using routine equipment at the centre (COMIT). Two wire form instances of the draping simulation are shown in Plates 1 and 2. For convenience, the surface of the cloth consists of two basic parts: one is the top part (the part above the waist) and the other is the bottom part (the remaining part of the surface). Generally speaking, we assume that the whole process of the conversion (determination of the final shape) can be regarded as two stages, draping preparation and draping. We start with the two-dimensional garment pattern coded according to the cutting patterns. During the first stage, the 2-D patterns are joined together to form a complete surface of cloth in threedimensional space according to the seaming information. It should be mentioned that from the point of view of fashion design, we are mainly
Plate 1. A wire form instance of draping simulation (an early stage)
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Plate 2. A wire form instance of draping simulation (a later stage)
interested in the real 3-D shape of a cloth. The major task is consequently concerning the transformation from the 2-D patterns to a real threedimensional shape. Since the final drape stage is mainly determined by material properties and body shape, after the 2-D design patterns are defined, and the initial drape state is not critical, the initial drape state (the start point of a draping) can be defined somewhat arbitrarily, on condition that it is a reasonably true reflection of its geometrical and physical configuration. In the current work, our main interest is first to examine the dynamic draping behaviour of the model rather than the garment design itself (this work is ongoing at present). The initial state of the top is constructed by a set of elliptical curves, and the initial state of the lower part is determined as a flat circular piece of cloth. The constraints used at this stage are only forces applied to the edges of each panel. In the second stage the shape of cloth will be deformed and draped over the synthetic lady’s body according to the laws of physics. The constraints used at this stage are the forces due to gravity and the forces from collisions[12], which are applied to each deformable nodes. The rendered instances of the simulation are shown in Figure 5. Figure 5a shows a very early stage in the draping simulation, Figure 5b shows the final stage of draping, Figures 5c and 5d show two intermediate stages. The computation of one whole draping simulation takes one hour CPU on Iris Indigo 2 workstation (excluding image rendering). Compared with other models, in which a draping
Modelling the dynamic drape of fabrics 23
(a)
(c)
(b)
(d)
simulation (nodes: 51 × 51) took one CPU-week on their workstation[14,15], our model is more efficient and less computer intensive. Plates 3 and 4 show the two instances of another draping simulation, in which a shorter skirt worn by a synthetic lady with a relatively larger hip is represented. Conclusion The deformable node-bar model based on a physical analogue to a deep shell system has been developed, and is capable of dealing with the complex deformation of cloth. The major advantages of this model over other models are that its configuration is based on the surface co-ordinate system, so it is
Figure 5. Rendered images of draping simulation
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Plate 3. A rendered image of draping simulation (an early stage)
Plate 4. A rendered image of draping simulation (final stage)
convenient for modelling complex fabric deformations and it can use fabric engineering mechanical parameters directly. In addition, this model appears to be more efficient and capable of incorporating the viscoelastic properties of fabric materials. The model has been verified with the simulation of real drape system and the modelling of a synthetic skinned lady. Therefore this approach
has provided a new method of simulating true 3-D garments for the next generation of CAD and retailing systems[20]. References 1. Griffin, D.S. and Kellogg, R.B., “A numerical solution for axially symmetrical and plane elasticity problem”, International Journal of Solid Structure, Vol. 3 No. 5, 1967. 2. Mohraz, B. and Schnobrich, W.C., “The analysis of shallow shell structures by a discrete element system”, Civil Engineering Studies, SRS 304, University of Illinois, March 1966. 3. Schnobrich, W.C. and Pecknold, D.A., “The lumped-parameter or bar-node model approach to thin shell analysis”, in Perrone, F. and Schnobrich, R. (Eds), Numerical and Computer Methods in Structural Mechanics, Academic Press, London, 1973, pp. 377-402. 4. Shanahan, W.J., Lloyd, D.W. and Hearle, J.W.S., “Characterizing the elastic behaviours of textile fabrics in complex deformation”, Textile Research Journal, Vol. 48, 1978, pp. 495-505. 5. Lloyd, D.W., “The analysis of complex fabric deformation”, in Hearle, J.W.S., Thwaites, J.J. and Amirbayant, J. (Eds), Mechanics of Flexible Fibre Assemblies, NATO Advanced Study Institute Series E, Applied Science No. 38, Sijthoff and Noordhoff, 1988, pp. 311-42. 6. Lloyd, D.W., “An integrated approach to the mechanical modeling of one, two and threedimensional textile structures”, in Carnaby, C.A., Wood, E.J. and Story, L.F. (Eds), The Application of Mechanics and Physics in the Wool Industry, Wool Research Organization of New Zealand, Special Publications Vol. 6, 1988, pp. 21-42. 7. Amirbayat, J. and Hearle, J.W.S., “The complex buckling of flexible sheet materials – Part 1, Theoretical Approach”, International Journal of Mechanical Sciences, Vol. 339, 1986. 8. Terzopoulos, D., Platt, J., Barr, A. and Fleischer, K., “Elastically deformable models”, Computer Graphics, Vol. 21, 4 July 1987. 9. Terzopoulos, D. and Fleischer, K., “Modelling inelastic deformation: viscoelasticity, plasticity, fracture”, Computer Graphics, Vol. 22 No. 4, August 1988. 10. Carignan, M., Yang, Y., Magnenat-Thalmann, N. and Magnenat-Thalmann, D., “Dressing animated synthetic actors with complex deformable clothes”, Computer Graphics, Vol. 26 No. 2, 1992, pp. 99-104. 11. Magnenat-Thalmann, N. and Yang, Y., “Techniques for cloth animation”, in MagnenatThalmann, N. and Thalmann, D. (Eds), New Trends in Animation and Visualisation, John Wiley & Sons, Chichester, 1991, pp. 243-56. 12. Werner, H.M., Magnenat-Thalmann, N. and Magnenat-Thalmann, D., “User interface for fashion design”, in Mudur, S.P. and Pattanaik, S.N. (Eds), Proceedings of the International Conference on Computer Graphics, Jaico, Bombay, February 1993, pp. 165-71. 13. Aono, M., “A wrinkle propagation model for cloth”, Proceedings of the International Conference on Computer Graphics, Springer-Verlag, Tokyo, 1990, pp. 96-115. 14. Breen, D.E., House, D.H. and Getto, P.H., “A particle-based computational model of cloth draping behaviours”, in Patrikalaksis, N.M. (Ed.), Scientific Visualization of Physical Phenomena, Springer-Verlag, Tokyo, 1991, pp. 113-34. 15. Breen, D.E., House, D.H. and Wozny, M.J., “A particle-based model for simulating the draping behaviours of woven cloth”, Textile Research Journal, November 1994. 16. Werner, S., Vibrations of Cells and Plates, 2nd ed., Marcel Dekker, New York, NY, 1993. 17. Moiseiwitsch, B.L., Variational Principles, John Wiley & Sons, London, 1966. 18. Stylios, G. and Wan, T.R., “A physical-based cloth model for garment design and manufacture”, Internal Report, COMIT, University of Bradford, 1995. 19. Cook, R.D., Concepts and Applications of Finite Element Analysis, 2nd ed., John Wiley & Sons, Toronto, 1981. 20. Stylios, G. and Zhu, R., “The definition of fabric drapability as an aesthetic property investigating aesthetic and dynamic drape of limp materials”, (forthcoming).
Modelling the dynamic drape of fabrics 25
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10 Received May 1995 Revised and accepted July 1995
Modelling the dynamic drape of fabrics on synthetic humans A physical, lumped-parameter model G. Stylios, T.R. Wan and N.J. Powell Department of Industrial Technologies, University of Bradford, Bradford, UK Introduction It can be argued that in globalization of the world markets competitiveness will depend on technological advances. The next generation of textile and garment manufacturing and automated retailing systems will need to predict the true 3-D behaviour of the fabric and garment design and wear. It is generally accepted that one of the most important requirements for the development of a 3-D garment computer-aided design (CAD) system is how to obtain the real shape of the garment in 3-D space from the original 2-D design patterns. It was realized that the deformable behaviour of textile materials would play a very important role in this area. Researchers have studied various methods to achieve this, such as the finite element method, the finite difference method, etc.; however, unlike other engineering areas, the successes of such modern methods are very limited in textile engineering. Commercial CAD systems for clothing are still not able to meet those requirements. The major task is to find a precise and efficient approach to determine the real 3-D deformed shape of a cloth according to real fabric properties and to deal with complex 3-D design patterns. Those solutions should be efficient in terms of computer processing time, and easy to use for practical industrial applications. In this article, we present a physical-based approach using the deformable bar-node and the lumped-parameter concept[1-3] to model complex deformation of fabrics. Our model is based on a physical analogue to deep shell system. The governing differential equations of motion and deformation of cloth are derived from the discretization of system energy over all rigid bar-deformable nodes on the surface of the cloth. Overview of related work Over the last two decades, in the textile engineering discipline, a great deal of effort has been made in modelling and analysing fabric deformation[4-7]. Although most of this work is concentrated on the relationship between the fabric structure and mechanical properties, not on the prediction of overall
International Journal of Clothing Science and Technology, Vol. 7 No. 5, 1995, pp. 10-25. © MCB University Press, 0955-6222
This project was conducted at the Center of Objective Measurement Technologies, which is supported by RETREX 1 project. The visualization tool has been kindly provided by ALIAS Inc.
shape of fabrics, it formed the basis for exploring and developing new approaches. One such important approach was reported by Shanahan et al.[4]. They concluded that a textile material displays a macrostructure (this may be defined as a kind of discontinuous structure when examining a very small piece of cloth) in terms of a measure of strain, since original displacement between the two element points can be made arbitrarily small, so that there is a lower limit to element size and, below this limit, the strain cannot be considered as being continuously distributed within the element. They also concluded that when the strains are finite, the definition and application of the stress-strain relationship present considerable difficulties, even for simple isotropic, elastic materials. They also pointed out that even for small strains, textile materials show viscoelastic behaviour and frictional slip and thus have a response which is non-linear, imperfectly recoverable, and time-dependent. Although they thought that it might not be likely to be practical to model a fabric in detail as an assembly of its constituent fibres or yarns, they presented a linear elastic theory of textile materials by treating fabrics as continuum sheets, as an initial framework for further consideration. Later, Lloyd first used the finite element method to study the deformable behaviour of fabrics and his application has shown some success[5]. However Lloyd concluded, in another report[6], that the finite element method was not naturally suited to the highly non-linear, large deformation of fabrics but rather to the simplest fabric deformation. Elastic theory, in contrast, handles larger deformations in a natural way. Lloyd’s conclusion was later contradicted by Amirbayat and Hearle[7], who have stated that the elastic theory of plates and shells is of little help in dealing with complex fabric deformation such as fabric buckling, and that the terminology and methodology are not easily applicable. Over the last decade, in the area of computer graphics, great effort has also been made to model the complex deformable behaviour of fabrics by using physical approaches. A successful physical-based model was first developed by Terzopoulos et al.[8] and Terzopoulos and Fleischer[9]. In principle, this model is based on the deformable behaviour of cloth, determined by a group of partial equations (constitutive), which are derived from Newtonian mechanics and the externally applied forces with the internal forces due to the movement and the deformation of cloth in a Lagrangian form. The ability to control deformable shapes is determined by parameters derived from differential geometric consideration. Any deformation of cloth is judged by observing the difference of metric tensor and the curvature tensor of the cloth surface compared to their initial, unstrained form. By employing and extending this approach, many other successful projects have been reported[10-12], but their work was mainly presenting visual effects of an animated cloth. There are considerable difficulties in applying these approaches to making an accurate model of real textile materials. In addition, it is not easy to incorporate mechanical
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parameters employed in engineering, such as Young’s modulus and Poisson’s ratio, and the procedures for solving the model equations are computer intensive and not practical for industrial use. Later, Aono[13] presented a physical-based model by using elastic theory. It is claimed that this model can take account of damping factors, anisotropy factors and viscoelastic effects for both static and dynamic forces. One advantage of this model is that it uses engineering parameters directly. However, the formulation of this model was established in a 3-D Cartesian coordinate system (x, y, z) and derived on the basis of the fundamental assumption that neither tensile nor shear dilatation along the z axis exist in the cloth. This assumption can be interpreted as: (∂u/∂z) = (∂v/∂z) = (∂w/∂z) = 0, where u, v and w are displacements along the x, y and z axis respectively. Note that this model is determined in a Cartesian co-ordinate system it clearly indicates that the assumption made is only appropriate for shallow deformation cases of cloth and its application is therefore limited in dealing with complex cloth deformation and cannot be applied to the surface co-ordinate system[12]. Recently, Breen et al.[14,15] developed an interacting-particle model for producing draping simulations of woven fabrics. In their approach, the deformation of cloth is represented and determined by the locations of particles, which conceptually represent the crossing points of warp and weft yarns. The location of each particle is only connected and influenced by neighbouring particles through the definition of group energy functions. The simulation is implemented as a two-step process from a given initial condition. The first step only models the effect of gravity and collisions between the cloth model and surrounding objects. The second step enforces interparticle constraints and finds the local energy minimum position before continuing with the next process. This particle-based model can also incorporate the fabric test data by using piecewise polynomial functions to convert them to energy functions, although in their work they only considered the low deformation for shear and bending in the range of first curve segment (from zero deformation to maximum deformation, hence neglecting any hysterics or history dependent effects). In their reports, two kinds of tablecloths were simulated. It can be seen, from their implementations, that they have still a long way to go for practical use in garment design. Examples of this can be found in their circle tablecloth simulation, in which the tablecloth has a jagged edge, and in their square tablecloth simulation the simulated samples appear to have much more stiffness, as if being draped over round edges. One possible difficulty with this method is that the approach of conceptually representing the macrostructure of cloth may lead to problems in dealing with arbitrary or complex boundary conditions. Another difficulty is in the use of force-displacement relationships which lead to more difficulty in describing the accurate shape of deformed objects than in the case of using elastic continuum theory, which uses basic stress-strain
relationship. These procedures are also very time consuming. The implementation of the square and circular tablecloth simulation (50 × 50 grid) need one CPU-week of computation (on DEC station 3100 class workstation). Assumptions and considerations In our approach, fabric is assumed as a continuum shell. This is true when the basic elements of the cloth for stress-strain analysis are large compared with its macrostructure unit[4]. Locations of the points in the cloth can be defined by a curvilinear surface co-ordinate system α1, α2, and α3 and can also be defined by the Cartesian co-ordinate system (x, y, z) as shown in Figure 1. The relationships between the two co-ordinate systems can be given by: x = f1(α1, α2, α3), y = f2(α1, α2, α3), z = f3(α1, α2, α3).
Modelling the dynamic drape of fabrics 13
(1)
The position vector r of a point P0 in a fabric can be determined by: r(α1, α2, α3) = f1(α1, α2, α3) • e1 + f2(α1, α2, α3) • e2 + f3(α1, α2, α3) • e3 (2) where e 1, e 2 and e 2 are the unit vectors along directions of x, y and z axes respectively (Figure 1). Our approach is developed based on physical concepts through a discretized potential energy formulation. In principle, the formulation of the governing equations of this model can be briefly illustrated as follows: the surface of cloth can be divided as series of node-bar elements according to the mesh layout employed, where the elements can be equal or unique sizes. Here, for simplification, we limit our partition to orthogonal linear co-ordinates which coincide with the lines of the principal curvature of the surface. The deformable
α3
α2
z dr
p1
p0
r
α1 r + dr
x
y
Figure 1. Location of points in fabric
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node-bar elements are defined as consisting of one deformable node with a number of rigid bars, as shown in Figure 2. As can be seen in this Figure, the patch of cloth is divided into a grid. In this way, the patch is divided as a series of elements, which can be equal or unequal sizes. In this approach, we treat each patch element as the unit of a deformable node with a number of rigid bars. The deformation of each node-bar element can be described by u and v displacements, along the first two surface co-ordinates α1, α2 within the tangent plane, and w displacement along the third surface coordinate α3 (the normal direction). The material properties of the continuum in all elements can be lumped at these deformable nodes by integrating all the energies within those elements. Note that we chose the perpendicular lines of principle curvature as coordinates α1, α2, and the normal to the surface as the third co-ordinate α3; thus we have three dimensional strain and stress components. Assuming that the fabric surface is homogeneous, isotropic and linearly elastic on which Hooke’s law can be applied, we have[16]:
σ = {E} • ε. (3) In the expression above, σ is the stress vector and defined by: σ = [σ11σ22σ33σ12σ13σ23]T (4) where σ 11 , σ 22 , σ 33 are the normal stress along the directions of α 1 , α 2 , α 3 , respectively and σ12, σ13, σ23 are the shear stress within the tangential planes of ( α 1 , α 2 ), ( α 1 , α 3 ) and ( α 2 , α 3 ) respectively. (Note that for complete stress components, σ12 = σ21, σ13 = σ31, σ23 = σ32). ε is the strain vector and defined by: ε = [ε11ε22ε33ε12ε13ε23]T (5) where ε11,ε22,ε33 are the normal strains along the directions of α1,α2,α3 respectively and ε12,ε13,ε23 are the shear stress within the tangential planes of 3
W
V
2 0
4
u
Figure 2. Configuration of a node-bar element in fabric
1
(α1,α2), (α 1,α 3) and (α 2,α 3) respectively, and {E} is the relation matrix for isotropic material and defined by: 2G + λ λ λ 0 0 0 λ λ 2G + λ 0 0 0 λ λ 2G + λ 0 0 0 E = 0 G 0 0 0 0 0 0 0 G 0 0 0 0 0 0 G 0
{} where
λ=
(6 )
µE
(1 + µ)(1 − 2 µ)
is Poisson ratio, E is the tensile modulus and Gij is the shear modulus. It is generally known that the cloth is anisotropic and non-linear and the deformations are history-dependent. In order to incorporate anisotropic material properties, we expand the strain-stress relationship above as follows: 2G + λ λ2 1 1 λ1 2G2 + λ2 λ1 λ2 E = 0 0 0 0 0 0
{}
λ3
0
0
λ3
0
0
2G3 + λ3
0
0
0
G12
0
0
0
G13
0
0
0
0 0 0 . 0 0 G23
(7 )
We assume that all components in E are independent from one another. It can be seen that this treatment will not only be able to incorporate the real material properties of fabric but would also be convenient for using real measurements of fabric parameters. G1, G2 and G3 are tensile modulus along α1, α2 and α3 respectively, λ1, λ2 and λ3 are corresponding Poisson ratios, and G2 is the shear modulus within the cloth surface and G13 and G23 are shear moduli within the plane of α 1 and α 3, and the plane of α 2 and α 3 respectively, which can be evaluated by the measure of bending properties at both planes. Energy expressions and fabric model The general strain-displacement relationships in the surface co-ordinates described above are given[16] as follows:
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∂Ai u j ∂ ui ⋅ + , i = 1, 2, 3. Ai j = 1 ∂α j Aj ∂α i Ai The shear strain is : ∂ ui A j ∂ u j A ⋅ ε ij = i ⋅ , i , j = 1, 2, 3; i ≠ j + Aj ∂α j Ai Ai ∂α i Aj ε ii =
1
3
∑
(8 )
(9 )
where Ai2 =
∂r
⋅
∂r
∂α i ∂α i
, i = 1, 2, 3.
Consider the local area of fabric, since we limit our shell co-ordinate system to coincide with the lines of principal curvature of the surface and the normal direction and the deformation of each node-bar element can be described as the u and v displacements, which are along the first two curvilinear co-ordinates within the tangent plane, and w displacement along the normal direction. It is therefore reasonable to assume that, for local stress-strain analysis, the fundamental form parameters A 1 ≈ A 2 ≈ A 3 ≈ 1. The strain-displacement relationships are therefore simplified and ε can be evaluated by: ε = ∂u ∂α 2 ∂u ∂α 3 ∂v ∂α 3
∂u ∂α 1 ∂v ∂α 2 ∂w ∂α 3 + + +
= v – v 4 2 ∂v l ∂α 1 α 2 w – w0 ∂w 1 l 2 ∂α 1 α 1 w – w0 ∂w 4 l 2 ∂α 2 α 2
u1 – u3 lα 1 v2 – v4 lα 2 0 + + +
u1 – u3 lα 1 w3 – w0 lα 1 2 w2 – w0 lα 2 2
(10 )
where, ui, vi and wi are the displacements of the node i, as shown in Figure 2. It must be emphasized here that according to the above discussion, it would only be true to treat the fabric as being of continuum material if the basic fabric element sizes chosen for modelling are large, compared with the microstructure
of the fabric. However, this requirement is normally met by the selection of size of basic cloth elements. The cloth motion equations can be derived from the energy functions of the system as follows. Consider the deformation distribution within one basic fabric element. The question is how to find the strain energy density function or strain energy distribution. We assume that the virtual energy density of strain will change continually and smoothly within the basic fabric element. This indicates that the energy density function, which is related to stresses σij and strains εij, is continuous. We also assume that they have continuous derivatives everywhere within the basic element and that the resultant effect on the whole shape of fabric, especially at locations of each node, will be identical to the effect of using lumped node-bar element treatment (Figure 3). Under the above assumptions, we treat the shape of fabric as being a continuum shell. Now, we are ready to examine one infinitesimal element, as shown in Figure 3. The strain energy stored in an infinitesimal element within one basic fabric element that is acted by stress σij is: dU =
1
σT ε dV .
Modelling the dynamic drape of fabrics 17
(11)
2 The strain energy density is therefore:
Nodei
An infinitesimal element
Figure 3. An infinitesimal element in a basic fabric element
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χ1 =
1
χ2 =
1
σT ε. 2 The kinetic energy density is: ρ (u˙ 2 + v˙ 2 + w˙ 2 )
(12 )
(13 )
2 where ρ is mass density and the dot indicates a time derivative. The energy density introduced by body forces is:
χ3 = q1u + q2v +q3w
(14)
where q1, q2 and q3 are forces per unit volume. The energy density introduced by possibly applied boundary forces is:
χ4 = N1u + N2v + N3w
(15)
where N1, N2 and N3 are the resultants of the boundary forces. Hence, the total energy density is: L = χ1 + χ2 + χ3 + χ4.
(16)
It can be seen that we actually define an energy field. Using Euler-Lagrange equations[17], the cloth motion can be determined by 3 ∂ ∂L ∂ ∂L =∑ – (i = 1, 2 , 3 ). ∂ψ i k =1 ∂X k ∂ (∂ψ i / ∂uk ) ∂t ∂ψ i
∂L
(17 )
where ψ i are general functions of energy component, representing ui (i = 1, 2, 3). Using matrix notation ψ and X gives:
ψ = [uvw]T
(18)
and X = [α1α2α3 t]T
(19)
where t is the time variable. The detailed derivation of all terms in the fabric motion equations may be obtained in our internal report[18]. Viscoelastic properties In general, textile materials contain viscoelastic properties. In order to represent the behaviour of cloth precisely, the model must be able to incorporate the viscoelastic properties of those materials. At the current stage, we use the linear Kelvin model[19] for describing material viscoelastic properties. The stressstrain relationships are now given by: . σ = {E} ε + {β}ε (20) where
β 11 β21 β β = 31 0 0 0
{}
β12 β22 β32 0 0 0
β13 β23 β33 0 0 0
0
0
0
0
0
0
β12 0 0
0
β13 0
Modelling the dynamic drape of fabrics
0 0 0 0 0 β23
(21)
19
where components βi,j are damping terms, which may be estimated by various methods. . The terms of ε may be estimated as follows: ˙ε = ∂u˙ ∂α 2 ∂u˙ ∂α 3 ∂v˙ ∂α 3
∂u˙ ∂α 1 ∂v˙ ∂α 2 ∂w˙ ∂α 3 + + +
= v˙ – v˙ 4 2 ∂v˙ l ∂α 1 α 2 w˙ – w˙ 0 ∂w˙ 1 l /2 ∂α 1 α 1 w˙ – w˙ 0 ∂w˙ 4 l /2 ∂α 2 α 2
u˙1 – u˙3 lα 1 v˙2 – v˙4 lα 2 0 + + +
u˙1 – u˙3 . lα 1 w˙ 3 – w˙ 0 lα 1 / 2 w˙ 2 – w˙ 0 lα 2 / 2
(22 22)
Visualization implementation and discussion In order to verify our fabric model, two sets of drape simulations have been applied and implemented. One of them is a fabric drape simulation on the m3 system which is a computer control static and dynamic drape system, developed in our centre for testing and predicting the drape behaviour of fabric. The other is a simulation of the dynamic draping of a skirt, which is worn (attached to) by a synthetic lady. Drape prediction of fabric materials The Merylin Monroe Meter (m3)[20] was developed in the centre for testing and evaluating the static and dynamic drape behaviour of fabric. The drape behaviour of a fabric measured by the meter will depend on the mechanical
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Figure 4. Rendered images of drape simulation and actual photographs of fabrics A and B
properties of the fabric sample. It is of interest to develop a model to s imulate the real drape m3 system. It is expected that the successful model may be used for providing a prediction of the drape behaviour of real fabric samples by means of inputting their mechanical parameters to the model and that the draping behaviour will vary with any changes of those mechanical parameters. In the current work, the drape system is modelled using 720 deformable nodesbar elements. The initial states of fabric samples are defined as flat. During draping, the fabric will drape over a cylinder. Figures 4a and 4b show the final instances of the simulation using different fabric materials. Both materials are blends of cotton and polyester. The mechanical parameters were measured at the centre. The tensile properties of both materials are the same. The difference between them is that the bending properties of the material shown in Figure 4a is double of that of the material in
(a) A rendered image of drape simulation of fabric
(b) A rendered image of drape metre simulation of fabric B
(c) Actual photograph of fabric A
(d) Actual photograph of fabric B
Figure 4b. It is clear that the two simulations provide a very different drape behaviour. Figure 4a (fabric A) shows that the number of drape folds is four whereas Figure 4b (fabric B) shows that the folds are five. In order to make comparison between these simulations and the real world, we took the actual photographs from the real drape of fabrics A and B as shown in Figures 4c and 4d. It can be seen that there is a good agreement between the draping simulations and real drape of those fabrics. It should be stated that the drape simulation model may provide more detailed information than the real drape system m3, since it is able to provide the entire shape of the fabric.
Modelling the dynamic drape of fabrics 21
Drape behaviours of a skirt The skirt was modelled involving 1,440 (20 × 72) deformable node-bar elements, and was used to model a skirt over a synthetic human lady. The fabric is a blend of polyester and cotton. The material parameters, such as tensile and shear, were measured by using routine equipment at the centre (COMIT). Two wire form instances of the draping simulation are shown in Plates 1 and 2. For convenience, the surface of the cloth consists of two basic parts: one is the top part (the part above the waist) and the other is the bottom part (the remaining part of the surface). Generally speaking, we assume that the whole process of the conversion (determination of the final shape) can be regarded as two stages, draping preparation and draping. We start with the two-dimensional garment pattern coded according to the cutting patterns. During the first stage, the 2-D patterns are joined together to form a complete surface of cloth in threedimensional space according to the seaming information. It should be mentioned that from the point of view of fashion design, we are mainly
Plate 1. A wire form instance of draping simulation (an early stage)
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Plate 2. A wire form instance of draping simulation (a later stage)
interested in the real 3-D shape of a cloth. The major task is consequently concerning the transformation from the 2-D patterns to a real threedimensional shape. Since the final drape stage is mainly determined by material properties and body shape, after the 2-D design patterns are defined, and the initial drape state is not critical, the initial drape state (the start point of a draping) can be defined somewhat arbitrarily, on condition that it is a reasonably true reflection of its geometrical and physical configuration. In the current work, our main interest is first to examine the dynamic draping behaviour of the model rather than the garment design itself (this work is ongoing at present). The initial state of the top is constructed by a set of elliptical curves, and the initial state of the lower part is determined as a flat circular piece of cloth. The constraints used at this stage are only forces applied to the edges of each panel. In the second stage the shape of cloth will be deformed and draped over the synthetic lady’s body according to the laws of physics. The constraints used at this stage are the forces due to gravity and the forces from collisions[12], which are applied to each deformable nodes. The rendered instances of the simulation are shown in Figure 5. Figure 5a shows a very early stage in the draping simulation, Figure 5b shows the final stage of draping, Figures 5c and 5d show two intermediate stages. The computation of one whole draping simulation takes one hour CPU on Iris Indigo 2 workstation (excluding image rendering). Compared with other models, in which a draping
Modelling the dynamic drape of fabrics 23
(a)
(c)
(b)
(d)
simulation (nodes: 51 × 51) took one CPU-week on their workstation[14,15], our model is more efficient and less computer intensive. Plates 3 and 4 show the two instances of another draping simulation, in which a shorter skirt worn by a synthetic lady with a relatively larger hip is represented. Conclusion The deformable node-bar model based on a physical analogue to a deep shell system has been developed, and is capable of dealing with the complex deformation of cloth. The major advantages of this model over other models are that its configuration is based on the surface co-ordinate system, so it is
Figure 5. Rendered images of draping simulation
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Plate 3. A rendered image of draping simulation (an early stage)
Plate 4. A rendered image of draping simulation (final stage)
convenient for modelling complex fabric deformations and it can use fabric engineering mechanical parameters directly. In addition, this model appears to be more efficient and capable of incorporating the viscoelastic properties of fabric materials. The model has been verified with the simulation of real drape system and the modelling of a synthetic skinned lady. Therefore this approach
has provided a new method of simulating true 3-D garments for the next generation of CAD and retailing systems[20]. References 1. Griffin, D.S. and Kellogg, R.B., “A numerical solution for axially symmetrical and plane elasticity problem”, International Journal of Solid Structure, Vol. 3 No. 5, 1967. 2. Mohraz, B. and Schnobrich, W.C., “The analysis of shallow shell structures by a discrete element system”, Civil Engineering Studies, SRS 304, University of Illinois, March 1966. 3. Schnobrich, W.C. and Pecknold, D.A., “The lumped-parameter or bar-node model approach to thin shell analysis”, in Perrone, F. and Schnobrich, R. (Eds), Numerical and Computer Methods in Structural Mechanics, Academic Press, London, 1973, pp. 377-402. 4. Shanahan, W.J., Lloyd, D.W. and Hearle, J.W.S., “Characterizing the elastic behaviours of textile fabrics in complex deformation”, Textile Research Journal, Vol. 48, 1978, pp. 495-505. 5. Lloyd, D.W., “The analysis of complex fabric deformation”, in Hearle, J.W.S., Thwaites, J.J. and Amirbayant, J. (Eds), Mechanics of Flexible Fibre Assemblies, NATO Advanced Study Institute Series E, Applied Science No. 38, Sijthoff and Noordhoff, 1988, pp. 311-42. 6. Lloyd, D.W., “An integrated approach to the mechanical modeling of one, two and threedimensional textile structures”, in Carnaby, C.A., Wood, E.J. and Story, L.F. (Eds), The Application of Mechanics and Physics in the Wool Industry, Wool Research Organization of New Zealand, Special Publications Vol. 6, 1988, pp. 21-42. 7. Amirbayat, J. and Hearle, J.W.S., “The complex buckling of flexible sheet materials – Part 1, Theoretical Approach”, International Journal of Mechanical Sciences, Vol. 339, 1986. 8. Terzopoulos, D., Platt, J., Barr, A. and Fleischer, K., “Elastically deformable models”, Computer Graphics, Vol. 21, 4 July 1987. 9. Terzopoulos, D. and Fleischer, K., “Modelling inelastic deformation: viscoelasticity, plasticity, fracture”, Computer Graphics, Vol. 22 No. 4, August 1988. 10. Carignan, M., Yang, Y., Magnenat-Thalmann, N. and Magnenat-Thalmann, D., “Dressing animated synthetic actors with complex deformable clothes”, Computer Graphics, Vol. 26 No. 2, 1992, pp. 99-104. 11. Magnenat-Thalmann, N. and Yang, Y., “Techniques for cloth animation”, in MagnenatThalmann, N. and Thalmann, D. (Eds), New Trends in Animation and Visualisation, John Wiley & Sons, Chichester, 1991, pp. 243-56. 12. Werner, H.M., Magnenat-Thalmann, N. and Magnenat-Thalmann, D., “User interface for fashion design”, in Mudur, S.P. and Pattanaik, S.N. (Eds), Proceedings of the International Conference on Computer Graphics, Jaico, Bombay, February 1993, pp. 165-71. 13. Aono, M., “A wrinkle propagation model for cloth”, Proceedings of the International Conference on Computer Graphics, Springer-Verlag, Tokyo, 1990, pp. 96-115. 14. Breen, D.E., House, D.H. and Getto, P.H., “A particle-based computational model of cloth draping behaviours”, in Patrikalaksis, N.M. (Ed.), Scientific Visualization of Physical Phenomena, Springer-Verlag, Tokyo, 1991, pp. 113-34. 15. Breen, D.E., House, D.H. and Wozny, M.J., “A particle-based model for simulating the draping behaviours of woven cloth”, Textile Research Journal, November 1994. 16. Werner, S., Vibrations of Cells and Plates, 2nd ed., Marcel Dekker, New York, NY, 1993. 17. Moiseiwitsch, B.L., Variational Principles, John Wiley & Sons, London, 1966. 18. Stylios, G. and Wan, T.R., “A physical-based cloth model for garment design and manufacture”, Internal Report, COMIT, University of Bradford, 1995. 19. Cook, R.D., Concepts and Applications of Finite Element Analysis, 2nd ed., John Wiley & Sons, Toronto, 1981. 20. Stylios, G. and Zhu, R., “The definition of fabric drapability as an aesthetic property investigating aesthetic and dynamic drape of limp materials”, (forthcoming).
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Physiological and perceptual responses to forearm immersion in cold water Lisa A. Shanley, David D. Pascoe, Layne Anderson and Teresa Bellingar Department of Consumer Affairs, Auburn University, Alabama, USA Introduction To prevent loss of dexterity and cold injury during work in cold environments, optimum hand and foot temperature should remain at 21°C and fall no lower than 15°C[1]. However, materials used for warmth are often bulky and restrict movement and task performance. Personnel at work in hazardous situations will often forgo the use of protective clothing to improve task performance[2]. Accidental immersion in cold water presents an even greater threat. Water is a far more effective conductor of heat than air, removing heat 17 times faster[3]. In polar regions, where water temperatures range from 0°C to –1°C, survival may be only a matter of minutes and may be dependent on an individual's ability to use his/her hands to deploy survival equipment. In –1°C water, unprotected hands can freeze in two to three minutes[1]. In many cold environments, the threat of fire is also very real. In Antarctica, static electricity generated by machinery in this extremely dry environment has often ignited fuel fumes present in the air[4]. Oil riggers, forest rangers, pilots, etc., are often in danger from both the cold and fire. Many thermoplastic materials used for warmth cannot be used in fire protection because they will not withstand high temperatures or an open flame. Some of the so-called fire resistant materials are also incapable of withstanding high temperatures when used in loose fibre form such as non-woven insulative batts. Aramid battings were found to give only 13.9 seconds of protection from a second-degree burn when exposed to a convective energy level of about 2.0cal/cm2 (8.3W/cm2)[5]. Materials, coatings, and chemical treatments that are self-extinguishing may burn, char, shrink, or otherwise lose their integrity, and not prevent open flame from contacting the body. Personnel using protective materials also require that these materials should be comfortable and should not enhance heat stress. To be most effective, insulation should provide the maximum thermal resistance for the minimum weight.
International Journal of Clothing Science and Technology, Vol. 7 No. 5, 1995, pp. 26-32. © MCB University Press, 0955-6222
Purpose of research The purpose of this study was to determine the physiological and perceptual responses to forearm cold water immersion as influenced by the use of an
experimental fireproof carbonaceous insulation (ECI), Thinsulate (a commercial insulation), and Nomex flight gloves. The primary objective was to determine if ECI could provide adequate protection from cold water immersion while providing superior protection from fire. The following research questions were developed to guide the study: ● Are there significant differences in physiological responses to forearm immersion in –1°C water among ECI, Thinsulate insulation, Nomex, and a no-glove condition? ● Are there significant differences among treatment conditions in subjects’ perception of thermal comfort during forearm immersion? ● Are there significant differences among treatment conditions in subjects' grip strength post-immersion?
Forearm immersion in cold water 27
Experimental design Independent variables The experiment followed a 4 × 3 factorial design. The independent variables manipulated were no-glove, Nomex flight glove, an experimental carbonaceous insulation (ECI) glove, and a Thinsulate glove. Treatment conditions were randomized to avoid trial order effects. Each subject served as his/her own control and performed all four treatment conditions. The three testing conditions were a five-minute pre-trial period for baseline steady state values; a 15-minute forearm immersion in 1°C water; and a 15-minute recovery. Both forearms were immersed simultaneously during each testing condition. Water temperature was maintained by adding ice and salt to the container. Room temperature was held constant between 22°C and 25°C with no convective air movements. Subjects were clothed in shorts and cotton T-shirts. The glove shell for both ECI and Thinsulate was constructed from 100 per cent impermeable coated nylon. The ECI and Thinsulate gloves were designed as one size fits all subjects. The gloves covered the forearms up to the elbow. The seams were sealed to prevent the intrusion of water. Physical properties of ECI, Thinsulate, and Nomex are provided in Table I. Properties Thickness Weight (g/m2) Thermal conductance (K) Thermal resistance (R)
ECI
Thinsulate
Nomex
1.05cm (0.4in.) 75 0.26 1.58
0.64cm (0.25in.) 124 0.23 1.09
0.48cm (0.1875in.) 43g n/a n/a
Notes: K-value = hr × ft2 × °F BTU R-value = thickness (inches)/K-value
Table I. Physical properties of glove insulations
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Experimental carbonaceous insulation (ECI) The carbonaceous fibres are derived from heat-set stabilized polyacrylonitrile (PAN). The PAN fibres have been oxidized at temperatures greater than 600°C and yield a carbon content greater than 65 per cent. The carbonaceous fibres are present in the blends in an amount from 7.5 to 92.5 per cent. Oxygen index values greater than 40 were obtained[6]. The non-linear fibres provide high loft and improved thermal insulating properties to materials utilizing them and a porosity which inhibits the spread of fire. Owing to its high carbon content, the carbonaceous fibre offers several advantages over normal organic fibres. These include: ●
ignition resistance;
●
the capacity to render blends non-flammable, in some cases when less than 25 per cent of the fibre is used (up to 34 seconds protection from second degree burn)[5];
●
less brittleness than is usually associated with carbon fibres;
●
exceptional resilience of non-woven batts when bonded with small amounts of thermoplastic fibre;
●
minimal water retention after soaking and draining; and
●
improved heat retention.
Thinsulate insulation Thinsulate insulation is a non-woven batt composed of 65 per cent polyolefin and 35 per cent polyester fibres. It is breathable and moisture resistant (fibres absorb less than 1 per cent by weight of water). Polyolefin fibres typically have a low melt point of approximately 163°C[7]. Dependent variables The dependent measures included one core temperature measurement (rectal) and ten skin sites (forehead, pectoral, palms, index fingers, the little fingers, and backs of the hands). Temperatures were automatically recorded every 60 seconds by a Grant 1200 series squirrel meter logger. Subjects were asked to give a rating of perceived thermal discomfort every five minutes. A representative mark of perceived discomfort was chosen along a 10cm line from no perception of pain or discomfort to very intense pain and discomfort. Grip strength was determined pre- and post-immersion, and after 15 minutes of recovery using a grip dynamometer (Takei Kiki Kogyo). Participants Ten males and ten females (mean age = 23 years) completed the immersion trials. Mean percentage of body fat was 16.8 per cent for males and 21.8 per cent for females.
Analysis of data To collapse the data, right and left palm, little finger, index, and back of the hand temperatures were combined to create a mean hand temperature. Multivariate analysis of variance (MANOVA) with repeated measures was used to determine significant differences among the baseline, immersion, and recovery mean hand, forehead, pectoral, and core temperatures. A 4 × 3 (treatment by time) analysis of variance (ANOVA) and Duncan’s multiple range test were used to analyse the perceptions of thermal comfort. Student’s paired t-test was used to analyse the pre- and post-grip strength data.
Forearm immersion in cold water 29
Results Physiological responses Analysis of variance revealed that mean hand temperatures for ECI were significantly higher than those for the Thinsulate (F = 16.8, p < 0.0008) (see Table II and Figure 1 for means). This is consistent with the K-values and
Baseline Immersion Recovery
ECI
Thinsulate
Nomex
No-glove
31.7 25.1 30.9
31.5 20.8 28.0
30.4 5.4 23.2
28.2 4.5 23.3
Table II. Mean hand temperatures (°C)
Temperatures 35
30
25
20
15
10
5
0 0
1
2
3
4
5
10
15
30
Minutes
Key: Experimental fireproof carbonaceous insulation Thinsulate Nomex No-glove
Figure 1. Mean hand temperatures
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R-values found for each of the insulations. This finding indicates that ECI can provide greater warmth with less weight. Mean hand temperatures for the Nomex flight glove were not significantly different from the no-glove condition (F = 15.87, p < 0.09). The Nomex flight gloves are not waterproof and provided only a slightly better barrier than wearing no glove at all. Of those subjects that completed the trials, half of the subjects indicated that they were more comfortable with the Nomex while the other half preferred the bare arm trial. Some subjects indicated that Nomex provided some barrier while others felt that it prevented their hands from getting numb and therefore it was more painful. There was no significant difference between the mean baseline temperature and the final recovery temperature for ECI (F = 1.87, p < 0.18). Subjects were able to recover completely after 15 minutes post-immersion. Baseline temperatures were significantly higher than recovery temperatures for the Thinsulate (F = 22.32, p < 0.0002), Nomex (F = 10.12, p < 0.005), and no-glove (F = 210.9, p < 0.0001) conditions. Subjects were not able to return to normal temperatures after 15 minutes post-immersion. This finding suggests that there could be some decrement in performance even after the subject is removed from the cold water. Recovery temperatures were significantly higher for ECI than for Thinsulate (F = 5.6, p < 0.02). Recovery temperatures were not significantly different for the Nomex versus the no glove condition (F = 0.0, p < 0.9). No significant changes were found with respect to rectal, pectoral, or forehead temperatures. Some subjects anecdotally reported that their faces felt flushed during the Nomex and no-glove conditions. Perceptual responses Thinsulate was perceived to provide only slightly greater thermal comfort than ECI (Table III and Figure 2). The Thinsulate glove was heavier and bulkier than the ECI glove and may have therefore been perceived to be warmer. There were no significant differences in comfort levels between the Nomex trials and the no-glove trials. Subjects rated the no-glove trial as significantly more uncomfortable at minute 15 than at minute 5 or 10. ECI and Thinsulate provided negligible loss of grip strength. Grip strength was significantly lower for the Nomex and no-glove trials (Table IV). Subjects
Minute 5
Table III. Mean perceptions of thermal comfort during immersion
Minute 10
ECI 1.077 A 1.760 A Thinsulate 0.725 A 0.933 A Nomex 4.412 B 4.470 B No-glove 5.265 B 5.445 B Note: Means with the same letter are not significantly different
Minute 15 2.228 A 1.425 A 4.137 B 5.580 C
Forearm immersion in cold water
10 8 6
31
4 2 0 5
10
15
Minutes
Key:
Figure 2. Perceptions of thermal comfort during immersion
Experimental fireproof carbonaceous insulation Thinsulate Nomex No-glove
Females
ECI Thinsulate Nomex No-glove
Males
Before
After
Mean decrease
47.2 41.9 62.3 71.2
43.7 38.3 47.2 50.4
7.5 8.6 24.3 29.3
Before
After
Mean decrease
86.2 85.3 110.5 123.3
88.9 83.7 92.1 91.0
0.0 1.9 16.7 26.2
were able to return to pre-trial grip strength at the end of the recovery period for the ECI, Thinsulate, and Nomex trials. Subjects did not return to pre-trial grip strength after the no-glove trial. Conclusions The experimental carbon insulation product is lighter in weight than Thinsulate and may provide manufacturers with the opportunity to produce better survival gloves and garments without impeding performance. ECI also offers superior protection from fire[5]. Thinsulate is a thermoplastic material and will melt at low temperatures. The use of a Nomex flight glove provided virtually no protection from cold water immersion and little fire protection. Subjects were unable to return to baseline temperatures after 15 minutes postimmersion. Through the development of lighter weight, less bulky, insulative material, greater protection can be provided for those who are at risk of cold injury and fire (i.e. pilots, sailors, ocean oil rig workers).
Table IV. Mean percentage decrease in grip strength post-immersion
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References 1. Fogel, L., Biotechnology: Concepts and Applications, Prentice-Hall, Englewood Cliffs, NJ, 1973. 2. Reeps, S., Larson, T. and Kaufman, J., Analysis of the Threat and Development of Proposed Requirements for Naval and Marine Corp. Extreme Cold Weather Aircrew Clothing and Survival Equipment, Naval Air Development Center, Warminster, PA, 1989. 3. Watkins, S., Clothing: The Portable Environment, Iowa State University Press, Des Moines, IA, USA, 1984. 4. Marano-Goyco, J., The Important Elements and Design Criteria in the Evaluation of Protective Materials for Burn Injury, Naval Air Development Center, Warminster, PA, 1989. 5. Shanley, L., Slaten, B., Shanley, P., Broughton, R., Hall, D. and Baginski, M., “Thermal properties of novel carbonaceous fiber battings”, Journal of Fire Sciences, Vol. 12 No. 3, 1994, pp. 238-45. 6. McCullough, F. and Hall, D., Flame Retarding and Fire Blocking Carbonaceous Fiber Structures and Fabrics, United States Patent, No. 4950533, 1990. 7. Lyle, D., Modern Textiles, John Wiley & Sons, New York, NY, 1982.
Algebraic modelling of pattern design: the abelian pattern semi-group Roger Ng, C.K. Chan, T.Y. Pong and Raymond Au
Algebraic modelling of pattern design 33
The Hong Kong Polytechnic University, Hong Kong Introduction To understand the pattern design process from a fundamental perspective, a study started from the decomposition process of a garment into a collection of pattern pieces. Consider a finished garment being represented by a single pattern, which can be decomposed into smaller patterns that correspond to the sub-garment, such as front and back panels. In turn, the pattern for the subgarment can be further decomposed into even smaller sub-sub-garments, say left and right front panels. This process can continue until all the building blocks have been identified (see Figure 1). Then, we can use this set of building blocks to generate the real garment through the manufacturing process. Since there are many possible ways of decomposing the patterns, we can impose a limit for decomposition up to a pre-defined resolution. The collection of all the possible patterns is a set S. Each element in S represents a state of the garment under the decomposition process. The set S together with the composition process forms an algebraic structure known as semi-group. The construction of such semi-group will be presented with proofs and examples. Furthermore, the associated lattice structure will be presented with examples. Finally the lattice structure will be used to solve the minimal cost problem of a production line. Abelian pattern semi-group (APSG) Group theory is a study of an operation on a set of elements[1]. When each element represents a state, the binary operator is interpreted as the transformation of the states. For example, the set of all possible rotations of an object can form a group. Finite group, which means a group with finite number of elements, has many applications in the study of physics[2]. A less restricted version of group is called the semi-group. It is less restricted because it does not require the existence of inverse elements for each element. Semi-group theory has also been applied in many disciplines such as modelling the automata in computer science, and modelling genetic code in biology[3]. Definition of the abelian semi-group A semi-group G is a collection of both a set S and an associative binary operation l• [4]. Mathematically, we write G = (S, l• ), S is a set, l• is a binary operation: S ×S→S, and that means: ∀ x, y, z ∈ S,
International Journal of Clothing Science and Technology, Vol. 7 No. 5, 1995, pp. 33-43. © MCB University Press, 0955-6222
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Garment
Garment decomposition Back
1. Split garment into front and back panels 2. Split front panel into left and right panels 3. Further decompose until building blocks are identified
Front
34 Front right
Figure 1. Garment decomposition
Further decomposition
Front left
:
:
xl • y ∈ S (closed), x l• (y l• z) = (x l• y) l• z (associative). Furthermore, if the operation l• commutes, the semi-group is called an abelian semi-group: x l• y = y l• x (commutative). Some special elements are also defined for a semi-group; these are: identity element, e; zero element, o; and idempotent element, i. Identity element, e property: e l• x = x, ∀x ∈ S. Zero element, o property: o l• x = o, ∀x ∈ S. Idempotent element, i property: i l• i = i, i ∈ S. An additional type of element, which the authors defined, is the complement element: Complement element of x, xP property: xl• xP = o. Definition of a semi-group table A semi-group table is a table that records the domain and range of the operation l• . Given S = {a, b, c}, a two-dimensional semi-group table is as shown in Table I. The semi-group table for an abelian semi-group is symmetric about the diagonal line of i l• i, ∀i ∈ S, since a l• b = b l• a, ∀a, b ∈ S. Subsemi-group Subsemi-group S ′ is defined as: ● S ′ ⊂ S. ● S ′ is a semi-group.
In general, intersection of subsemi-groups is again a subsemi-group[3, p. 342, theorem 3.18]. Definition of the abelian pattern semi-group Let P 2 and P 3 (P stands for pattern) be a set of patterns, which is a set of plane area in R2 (for 2-D pattern design) and a set of surface area in R3 (for 3-D pattern design) respectively. Since P 2 and P 3 represent the same thing, though in different content, we refer P to the set of patterns in the following discussion. The element g is chosen to represent the pattern for the finished garment. The operation l• is chosen to be the composition of patterns. Since the set P contains all possible combination of the building blocks, we say P is generated by the building blocks. The subset in P that contains all the building blocks is called the basis. Let APSG = (P, l• ), and g, p1, p2, p3 be elements in P. Each element p in P represents a set of areas, and need not be connected. l• is defined as: l• : P × P → P
Algebraic modelling of pattern design 35
and explicitly, p1 l• p2 = p3 {p3 is the pattern that corresponds to the total area that p1 and p2 represent}. Note: l• is by definition closed. To see that l• is associative, let p1, p2, p3 represent areas a1, a2, and a3 respectively: p1 l• (p2 l• p3) = {a1 ∪ {a2 ∪ a3}} represents the same areas as = {{a1 ∪ a2} ∪ a3} = (p1 l• p2) l• p3 (associative). Note: the operation ∪ is the set union in terms of area. Therefore, l• is associative. Furthermore: pi l• pj = {ai ∪ aj} = {aj ∪ ai} = pj l• pi (commutative) for i = 1, 2, 3; j = 1, 2, 3.
l•
a
b
c
a
al • a
al • b
al • c
b
bl • a
bl • b
bl • c
c
cl • a
cl • b
cl • c
Table I. The semi-group table
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Therefore, l• is also commutative. This completes the construction of the abelian pattern semi-group. Furthermore, the zero element is g, the final pattern, while all elements in P are idempotent patterns. The identity element is the empty set. Since the operator commutes, the semi-group table is symmetric over the diagonal.
36
Two useful properties of APSG (1) An APSG has a finite number of elements. Proof: since APSG is generated by a finite number of generators, or building blocks, every element in APSG can be expressed as a finite combination of these building blocks: Let p1 = b1m1 l• b2m2 l• , …, l• bnmn where b 1 … b n are the building blocks, and m1, …, mn are the corresponding indices. To see that APSG has finite number of elements, consider the possible values of the indices. Since all elements, except g, are idempotent, mi can either be 0 or 1. When the building block has index 0, it is reduced to the identity element. Therefore, the largest element in APSG can be written as b1 l• b2 l• , …, l• bn. This special element is g. Consequently, using this power set construction method, the total number of elements in APSG is n finite, and is equal to 2n. (2) In an APSG, every element has a complement element under l• . Proof by construction: the complement of p1, p1 p can be constructed by combining the building blocks of p1 whose index is 0. Then by n definition, p1 l• p1p = g. Also, gp is e. Examples See Appendix for definition: Example 1: APSG for a tank top (Table II): P = {e, fp, bp, g}, with building block = {bp, fp}. Example 2: APSG for a tank top with a centre split at front (Table III): P = {e, bp, flp, frp, fp, bflp, bfrp, g}, with building block = {bp, flp, frp}. Example 3: APSG for a T-shirt: P = {e, bp, fp, lsp, rsp, bfp, blsp, brsp, bflsp, bfrsp, blrsp, flsp, frsp, flrsp, lrsp, g}, with building block = {bp, fp, lsp, rsp}.
Table II. Semi-group table for Example 1
l•
e
bp
fp
g
e bp fp g
e bp fp g
bp bp g g
fp g fp g
g g g g
l•
e
bp
flp
frp
fp
bflp
bfrp
g
e
e
bp
flp
frp
fp
bflp
bfrp
g
bp
bp
bp
bflp
bfrp
g
bflp
bfrp
g
flp
flp
bflp
flp
fp
fp
bflp
g
g
frp
frp
bfrp
fp
frp
fp
g
bfrp
g
fp
fp
g
fp
fp
fp
g
g
g
bflp
bflp
bflp
bflp
g
g
bflp
g
g
bfrp
bfrp
bfrp
g
bfrp
g
g
bfrp
g
g
g
g
g
g
g
g
g
g
The size of the semi-group table is of size 16 × 16. Subsemi-group of APSG Clearly, we can see that example 1 can be considered as a subsemi-group of example 3, because the building blocks in example 1 are also present in example 3. Now that the APSG has been constructed, it is canonical to study the associated lattice structure. Each arc in the lattice corresponds to an assembly step. It is considered as the partial relation between each pair of elements in P. By assigning attributes to each arc, the weighted lattice can be used to solve optimization problem. One class of problem is associating cost of assembly to each arc. The solution is then the most economical production sequence. Partial ordered set Definition of a partial ordered set A partial ordered set (poset) is a set with a binary relation ≤, which is reflexive, transitive and antisymmetric[1]. Therefore obviously the binary relation is a subset of the direct product on the set. Mathematically, we write: O = (S, ≤), S is a set, ≤ is a partial order on S a subset of S × S, and ∀ x, y, z ∈ S : ●
x ≤ x, ∀ x ∈ S (reflexive);
●
if x ≤ y, y ≤ z, then x ≤ z (transitive);
●
if x ≤ y and y ≤ x, then x = y (antisymmetric).
Some special elements, if they exist[5], are also defined for a partial ordered set: ●
Greatest element, g property: x ≤ g, ∀ x ∈ S.
●
Smallest element, s property: s ≤ x, ∀ x ∈ S.
Algebraic modelling of pattern design 37
Table III. Semi-group table for Example 2
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38
●
Least upper bound, sup (S ) property: x ≤ sup (S ) , ∀ x ∈ S.
●
Greatest lower bound, inf (S ) property: inf (S ) ≤ x, ∀ x ∈ S.
●
Least upper bound for a pair of elements, sup (x, y) property: x ≤ sup (x, y) and y ≤ sup (x, y) , x, y ∈ S.
●
Greatest lower bound for a pair of elements, inf (x,y) property: inf (x, y) ≤ x, and inf (x, y) ≤ y, x, y ∈ S.
●
Complement element for a partial relation x ≤ xy, com (x, xy) property: com (x, xy) = y.
Definition of a hasse diagram A hasse diagram is a tree diagram (Figure 2) representing the relation ≤ in the partial ordered set, with x on top of y if and only if y ≤ x. Therefore if the tree has only one root, it is the greatest element. Conversely, if the tree has only one end leaf, it is the least element. Given S = {e, a, b, c, ab, ac, bc, abc}, with the following relation: e ≤ a ≤ ab ≤ abc, a ≤ ac ≤ abc, e ≤ b ≤ ab ≤ abc, b ≤ bc ≤ abc, e ≤ c ≤ ac ≤ abc, c ≤ bc ≤ abc.
abc = sup (ab, bc), sup (ac, bc) sup (ab, ac)
Figure 2. Hasse diagram
ab = sup (a, b)
ac = sup (a, c)
bc = sup (b,c)
a = inf (ab, ac)
b = inf (ab,bc)
c = inf (ac, bc)
e = inf (a, b), inf (a, c), inf (b, c)
Total order A relation ≤ is a total order, if ≤ satisfies the additional property of either x ≤ y or y ≤ x, ∀x, y ∈ S. The hasse diagram of a total ordered set is a straight line. For practical reasons, it is useful sometimes to induce more relationships into a partial order to make it a total order.
Algebraic modelling of pattern design 39
Lattice A lattice is a special kind of poset, which requires the existence of sup (x, y) and inf (x, y) for all pairs of elements in the set (see figure 2). Lattice structure of the pattern set P in APSG Let P denote the set of patterns in APSG, and ≤ be the assembly sequence relation between patterns in P. Production sequence A production sequence is an imposed total order chain in the lattice (P, ≤) with selected elements. Although there are many assembly steps that can be handled in a concurrent arrangement, the total order is imposed to include those assembly lines that can only handle the steps in sequential order. Under a total order the hasse diagram of P reduces to a straight line. Since P is generated by the building blocks, the immediate nodes above the only leaf, namely e, of the hasse diagram of P are obviously these building blocks, where the assembly process begins. For practical reasons, we require building blocks to be connected areas. We further restrict our attention to those patterns connected in area. It is very unfortunate to discover the fact that the set of connected patterns is not a semi-group under composition in general. However, this property becomes an advantage because many unnecessary arcs in the hasse diagram can be ignored during the searching. Examples Example 4: lattice for a tank top (see Figure 3): P = {e, fp, bp, g}, with building block = {bp, fp} e ≤ fp ≤ g, bp ≤ g. g = sup (fp, bp)
fp
bp
e = inf (fp, bp)
Figure 3. Hasse diagram for example lattice (APSG for tank top)
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Example 5: lattice for a tank top with a centre split at front (see Figure 4): P = {e, bp, flp, frp, fp, bflp, bfrp, g}, with building block = {bp, flp, frp}, e ≤ bp ≤ bflp, bfrp ≤ g, e ≤ flp ≤ fp, bflp ≤ g, e ≤ frp ≤ fp, bfrp ≤ g, e ≤ fp ≤ g. Example 6: lattice for a T-shirt (see Figure 5) P = {e, bp, fp, lsp, rsp, bfp, blsp, brsp, bflsp, flrsp, bfrsp, flsp, frsp, flrsp, lrsp, g} with building block = {bp, fp, lsp, rsp}. g
bflp
bfrp
fp
bp
Figure 4. Hasse diagram for example lattice (APSG for tank top with a centre split at front)
frp
flp
e g
flsp
Figure 5. Hasse diagram for example 6 (APSG for T-shirt)
flrsp
blrsp
frsp
lrsp
lsp
bflsp
blsp
fp
rsp
e
bfrsp
brsp
bp
bfp
Solution to the minimal cost problem in apparel production line Definition of problem Given an APSG, P, with a set, C, of cost attribute attached to the partial order arcs, find the sub-tree containing e, g and the basis B with minimal cost. The solution is any path from e to g within this sub-tree. This problem can be solved by using the Dijkstra algorithm[7].
Algebraic modelling of pattern design 41
Example Example 7: a tank top with a centre split at front: P = {e, bp, flp, frp, fp, bflp, bfrp, g} with B = {bp, flp, frp}. Suppose the weighted lattice is: {((e ≤ bp), 0), ((e ≤ flp), 0), ((e ≤ flp), 0), ((bp ≤ bflp), 200), ((bp ≤ bfrp), 200), ((flp ≤ fp), 10), ((flp ≤ bflp), 200), ((frp ≤ fp), 10), ((frp ≤ bfrp), 200), ((fp ≤ g), 10), ((bflp ≤ g), 200), ((bfrp ≤ g), 200)}. The minimal cost subtree is indicated in Figure 6. There are two possible minimal cost production sequence. One such sequence is e ≤ flp (frp) (10) ≤ fp (bp) (10) ≤ g. Another possible sequence is e ≤ frp (flp) (10) ≤ fp (bp) (10) ≤ g. These two sequences represent the same production sequence in reality, because each pair of patterns during assembly process corresponds. Both flp and frp are sewn up to form the fp. Thus either sequence is a valid solution. Example 8: a T-shirt: P = {e, bp, fp, lsp, rsp, bfp, blsp, brsp, bflsp, flrsp, bfrsp, flsp, frsp, flrsp, lrsp, g}, B = {bp, fp, lsp, rsp}. g
g
bfrp
bflp
fp (bp)
bp
flp
e Key : The manufacturing sequence
frp (flp)
fp (bp) frp (flp)
e
Figure 6. One possible optimal manufacturing sequence for tank top with a centre split at front
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Suppose the weighted lattice is: {((e ≤ bp), 0), ((e ≤ fp), 0), ((e ≤ lsp), 0), ((e ≤ rsp), 0), ((bp ≤ fbp), 10), ((fp ≤ fbp), 10), ((fbp ≤ fblsp), 10), ((lsp ≤ fblsp), 10), ((fbp ≤ fbrsp), 10), ((rsp ≤ fbrsp), 10), ((lsp ≤ g), 10), ((fbrsp ≤ g), 10), ((rsp ≤ g), 10), ((fblsp ≤ g), 10)} with the rest of the arcs having weights of 100. The minimal cost subtree is indicated in Figure 7. In this example, there are two distinct solutions. One solution is: e ≤ bp (fp) (10) ≤ bfp (rsp) (10) ≤ bfrsp (lsp) (10) ≤ g. Another solution is: e ≤ bp (fp) (10) ≤ bfp (lsp) (10) ≤ bflsp (rsp) (10) ≤ g. It is expected that the solution is not unique because of the symmetry over left and right. There is no difference between setting the left sleeve first and setting the right sleeve first. Consequently, any total order from this sub-tree is a valid solution to the optimal problem. Furthermore, in order to speed up the searching process, it is possible to consider only arcs that connect patterns side by side. Conclusion Pattern design has been shown to have its own semi-group structure. Many theorems in semi-group theory can then be applied to the APSG to derive both helpful insight and useful information. The notion of pattern composition brings out the lattice structure of the APSG, which provides a modelling for solving the minimal cost problem in an apparel production line. g
flsp
flrsp
blrsp
frsp
lrsp
g
bflsp
blsp
bfrsp (lsp)
brsp
bfrsp (lsp)
bfp (rsp) bfp (rsp)
lsp
Figure 7. One possible optimal manufacturing sequence for T-shirt
fp
rsp
e Key : The manufacturing sequence
bp (fp)
bp (fp)
e
Notes and references 1. Lang, S., Undergraduate Algebra, 2nd ed., Springer-Verlag, New York, NY, 1990. 2. Lomont, J.S., Applications of Finite Groups, Dover Publication, Toronto, 1987. 3. Lidl, R. and Pilz, G., Applied Abstract Algebra, Springer-Verlag, New York, NY, 1984. 4. Howie, J.M., An Introduction to Semi Group Theory, Academic Press, London, 1976. 5. Special elements may or may not exist in any poset. For example {a > b > c, and d > b > c} defines a poset of elements {a, b ,c, d}. In this case, the least element is c, and the greatest element does not exist. 6. In this case, e is ignored because e does not correspond to any real pattern. 7. Evans, J.R. and Minieka, E., Optimization Algorithms for Networks and Graphs, Marcel Dekker, New York, NY, 1992. Appendix: definition of abbreviations Abbreviation
Pattern pieces
bflp bflsp bfp bfrp bfrsp blrsp blsp bp brsp e flp flrsp flsp fp frp frsp g lrsp lsp rsp
Back panel + front left panel Back panel + front panel + left sleeve panel Back panel + front panel Back panel + front right panel Back panel + front panel + right sleeve panel Back panel + left and right sleeve Back panel + left sleeve panel Back panel Back panel + right sleeve panel Null pattern Front left panel Front panel + left + right sleeve panel Front panel + left sleeve panel Front panel Front right panel Front panel + right sleeve panel Final pattern Left + right sleeve panel Left sleeve panel Right sleeve panel
Algebraic modelling of pattern design 43
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44 Received November 1994 Accepted July 1995
Communications: quick response in clothing merchandising A study of the buying office using network analyses Chun-sun Leung and Merinda Yeung Institute of Textiles and Clothing, Hong Kong Polytechnic, Hong Kong
International Journal of Clothing Science and Technology, Vol. 7 No. 4, 1995, pp. 44-55. © MCB University Press, 0955-6222
The strategy of global sourcing for garment products in the 1970s triggered foreign companies from developed countries to set up subsidiary buying offices in Hong Kong. Companies have included resident buying offices, retailing chain stores and even garment manufacturers. Hong Kong has then evolved to become a sourcing base for garments owing to a number of reasons: manufacturing expertise, marketing and merchandising expertise, good infrastructure, and valuable human assets, to mention a few. The rapid-changing consumers’ taste and increasing market fragmentation have rendered seasonal sales forecasts in garment retailing more and more difficult, leading to immense stock-holding costs, high mark-downs and eroding profits. Since 1986 Kurt Salmon Associates, in Europe and in the USA, have developed strategies with manufacturers and retailers to improve response time throughout the textile supply chain using the quick response strategy[1], by which retailers aim to supply consumers with the right garment product in the right quantity, and at the right time. This has resulted in more “seasons” of clothing merchandising, shorter production runs and shorter delivery lead times. The results in Europe and the USA in the quick response management have been very encouraging, with impressive reductions in cycle time and costs. According to the report of the techno-economic and market research study of the Hong Kong textile and clothing industry[2], which was commissioned by the Industry Department of Hong Kong Government, results showed that overseas customers indicated that they were sceptical whether Hong Kong could be an effective quick response supplier in the true sense[3]. A major reason is because of the geographical distance that requires longer shipping time. Although this can be solved partially by air shipments, adding costs to the operation, other reasons, like real quick response co-operation between garment manufacturer and fabric supplier and long production lead times in fabric and garment manufacturing, have yet to be resolved. Fundamentally, it is required that a structure is built with relevant components in order to make the quick response management system[4] a success. Numerous articles have been published to discuss how the quick response system can enhance a company’s efficiency in terms of time, usually in a
descriptive manner. Very seldom the study is analytical and quantitative, and relates to those companies in the clothing industry in Hong Kong. This study aimed to examine the merchandising process of a large buying office in Hong Kong, which is a pioneer in speed sourcing in garment merchandising, to achieve the quick response ideology. The tasks or activities of the merchandising process were analysed using an operations management tool, programme evaluation and review technique (PERT)[5]. The purpose of using such a technique, which has widely used in other industries, was to determine quantitatively the shortest possible sourcing lead time in the merchandising process, and to identify critical activities in the process. Recommendations for improving and shortening the lead time were also proposed. The sourcing lead time (or the delivery lead time) is defined as the cycle time from the receipt of an order by the buying office to the point where the garment lot is ready for shipment at the manufacturer’s factory. The approach of this study Primary data gathering was done by in-depth interviews with key persons responsible for merchandising in the buying office under study. One of the researchers had completed an internship with the company for about one year, being posted in the merchandising department as a merchandiser trainee. Using information recalled from her previous work experience with the company, an activities analysis of the merchandising process for woven garments was first made by the researcher. This produced a draft of the activities network for the merchandising process. The draft was shown to the merchandising manager of the particular division under study during the interview for confirmation of the merchandising activities which were actually practised by the buying office. The merchandising manager was also asked to provide time estimates needed to complete each merchandising activity (see the section “Network analysis of the merchandising process” which follows). It was noted that there were some activities, such as pre-production sample preparation, which were governed by the company’s policy regarding completion time and time allowance. The merchandising manager was also asked about any corporate or company policies which might affect the merchandising activities and the garment delivery lead time. Another interview was carried out with the chief executive of the buying office to explore the aforementioned policy matters and where possibilities of reducing the delivery lead time could be brought about. After data gathering, the current merchandising process of the buying was analysed using PERT software on a personal computer. A new merchandising process was then recommended after re-engineering the process, taking into consideration the corporate and company policies. The new merchandising process was again subject to a PERT analysis on the computer. The merchandising process The buying office studied was a US garment buying office[6], which was set up in Hong Kong in the 1970s as the Far East regional office. Like other regional buying
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offices, it supplied a huge fleet of garment retail stores in the USA. The marketing department of the organization was located at the headquarters in the USA, which was responsible for part of the product development such as meeting with store buyers to enquire about their needs, and preparing the garment development specifications. The remaining tasks of product development were performed by the merchandising department in Hong Kong’s buying office. If the product development was successful, an order would be placed by the store buyer. The marketing department in the headquarters would then pass the order to the buying office. The latter was responsible for order production such as sourcing suppliers for fabrics, trimmings and garment manufacturers for production. Other responsibilities include ensuring the delivery of products of an acceptable quality and specifications to the hands of US store buyers. The buying office would receive a commission based on the sales figure of a completed order. The merchandising process employed in the buying office could generally be divided into three stages: product development, pre-production and production. The different merchandising activities carried out in the pre-production stage and production stage for a woven garment product are shown in Figure 1. It should be noted that the activities in the product development stage were excluded from the network analysis for this particular buying office. It was because the product development itself was a continuous, ongoing and separate process. The style adopted in the current season might be filed for next season or it might be rejected. The process of product development did not guarantee any garment delivery at any particular date. Therefore, the network analysis carried out for this study regarding the merchandising process concerned only the lead time spanning from the receipt of an order to the garment delivery. Product development stage Product development was the design and engineering of garment products to make them serviceable, producible, saleable and profitable. The product development stage normally began with sample-order receipts from retail buyers or the marketing department in the USA, where design specifications were being created, copied or modified. The buying office in Hong Kong then carried out an intensive materials (fabrics and accessories) search, sample development, lab-dipping, etc. The end of this stage was usually marked by the retail buyers’ decision as to whether a particular style was accepted or rejected. Garment samples prepared in this stage were called development or prototype samples. Most of the time, the approved style ended up with an order placement, but there were exceptions. For example, if the developed style was not added to that particular season’s line, it was kept for future use. Unacceptable styles could be modified or eliminated directly. Pre-production stage The pre-production stage extended from the moment the order was placed to that when the style was ready for production. Nearly half of the merchandiser’s time in the buying office was spent on this stage. Work involved frequent and detailed
Receive order
Source trimming
Hand-written order to factories
Lab-dipping approval
Lab-dipping
Prepare bulk fabric order
Order confirmation
Woven-yardage for fit sample
K
H
A
G
F
E
B
L
I
Fabric weaving/ processing
Create cost sheet
Make fit sample
Trimming approval
N
C
M
J
Dye-lots approval
Fit approval
Trimming delivery
O
Generate manufacturer purchase order
D
Q
Fabric delivery
Duplicate pattern
P
R
S
Make pre-production sample
Lab-testing
V
U
T
Garment delivery
Quality inspection
Garment manufacture
Pre-production sample approval
Quick response in clothing merchandising 47
Figure 1. Merchandising activities network – woven products (critical path in bold line)
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liaison and follow-up with the garment manufacturer for a clear understanding of buyer requirements. As the marketing departments in the headquarters and the buying office acted as middlemen between retail buyers and garment manufacturers, various documents had to be prepared and despatched to parties concerned. Two types of garment sample were to be made for approval before production started, namely fit approval samples and pre-production samples. Production stage The production stage ran from the time the fabric was cut to the point when the garments were packed and waited for shipment. During this stage, the merchandisers of the buying office make sure that fabric cutting has taken place according to schedule, and the assortment of garments cut correspond to the quantity ordered. In the event of short-cuts and surplus, the buying office alerts the marketing department or the retail buyers. The first production garments had to be reviewed (top-of-production samples) to make sure they conformed to both the fit, quality specifications, pressing and packaging. Co-ordination between the merchandising department and the quality assurance department during this stage was important as all the inspections (in-line and final) were being done by the latter. Network analysis of the merchandising process Programme evaluation and review technique (PERT) is a quantitative analysis and modelling technique for the planning, scheduling and controlling of activities in project or operations management[7]. Therefore, it readily finds application in the merchandising process, in which the various activities have to be scheduled and deadlines have to be adhered to. The ultimate purpose is to control the date of delivery of the garment product. The technique also enables an analysis of the various merchandising activities before the process starts. Critical activities can be identified and so a better monitoring system can be planned in advance. The basis of PERT is the preparation of a network diagram in which each activity or task or activity to be performed is represented (see Figure 1 for example). The arrangement of these activities indicates their sequence relative to one another. The network resulted represents the whole merchandising process which the buying office adheres to and forms the model for this network analysis. The next step in the PERT technique is to assign estimates of the time required to complete each activity. For repeated processes or projects like the clothing merchandising process, each company may have historical data, which can be used to form the time estimates. However, it is sometimes difficult to make accurate time estimates for new projects or processes in a new situation. Therefore, we have to resort to a range of estimate values. This range is typically defined as three time estimates for an activity’s duration: (1) optimistic time (a) = time duration within which the activity is completed under ideal conditions;
(2) mostly likely time (m) = most realistic time estimate to complete the activity under normal conditions; (3) pessimistic time (b) = time an activity would take assuming very unfavourable conditions PERT often assumes time estimates follow the beta distribution. Using the beta distribution, we have: ● The expected activity time, t = (a + 4m + b)/6; ● The standard deviation of each activity, s = (b – a)/6; and ● The standard deviation of the whole merchandising process: sw = (∑s2 )1/2. Once the expected completion time for each activity is determined, it is accepted as the actual time required by that task. The critical path (CP) consists of the sequence of activities which forms the longest time path through the network and, therefore, dictates the expected overall time required to complete the whole process or project. Thus the expected time to complete the whole process is calculated by summing the expected time, t, of every individual critical activity on the critical path. In the garment merchandising process, this expected overall time will determine the delivery lead time of the garment once an order is placed. The activities on the critical path are important and need monitoring in order to meet the delivery deadline. Findings and discussion In this study, in-depth interviews were conducted with the merchandising manager of a woven garment division and the chief executive of the buying office. The detailed individual activities were identified in the clothing merchandising process and an activities network was constructed (see Figure 1). Time estimates of merchandising activities Table I shows the 22 activities and time estimates in the merchandising process performed by the buying office under study for sourcing bulk-order woven garments in Hong Kong. It can be seen that activity N (fabric weaving/ processing) and activity U (garment manufacture) are activities which take up most of the time in the whole merchandising process. The most likely time (m) to complete these activities is 30 days and 25 days, respectively. The third and fourth longest activities are activity J (trimming delivery) and activity R (lab testing – fabric), with ms of eight days and five days, respectively. Other activities have much shorter duration, with ms of four days or less. Also, it can seen that both the optimistic time (a) and the pessimistic time (b) differ from m by only about half to two days. This may imply that the buying office has high expectations for small deviations and that a rather tight control in the merchandising activity schedules is being enforced.
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Table I. Time estimates and calculated expected time for merchandising activities – woven garment bulk orders (days)
Activity Activity name A B C D E F G H I J K L M N O P Q R S T U V
Optimistic Most likely Pessimistic Expected time, a time, m time, b time, t
Hand-written order to factories 0.5 Order confirmation 1.0 Create cost sheet 0.5 Generate manufacturer purchase order 1.0 Prepare bulk fabric order 0.5 Lab-dipping 3.0 La-dip approval 1.0 Source trimming 1.0 Trimming approval 2.0 Trimming delivery 6.0 Woven yardage for fitting sample 1.0 Make fitting sample 3.0 Fitting approval 2.0 Fabric weaving/processing 28.0 Dye-lots approval 0.5 Fabric delivery 0.5 Duplicate pattern 0.5 Lab testing (fabric) 4.0 Make pre-production sample 1.5 Pre-production sample approval 0.5 Garment manufacture 23.0 Quality inspection 0.3
1.0 2.0 1.0 1.5 1.0 4.0 2.0 1.5 2.5 8.0 2.0 4.0 3.0 30.0 1.0 1.0 1.0 5.0 2.0 1.0 25.0 0.6
1.5 3.0 1.5 2.0 1.5 5.0 3.0 2.0 3.0 10.0 2.5 4.5 4.0 40.0 1.5 1.5 2.0 6.0 3.0 2.0 30.0 1.0
1.0 2.0 1.0 1.5 1.0 4.0 2.0 1.5 2.5 8.0 1.9 3.9 3.0 31.3 1.0 1.0 1.0 5.0 2.1 1.1 25.5 0.6
Note: For test orders, the time estimates (a, m, b) and expected time, respectively, are the same except: activity N = 10, 14, 21; 14.5 (days) and activity U = 10, 14, 21, 13.5 (days)
Apart from bulk orders, the buying office may receive test orders, which are usually much smaller in terms of quantity. For test orders, the time estimates for the activities are the same except for activities N and U (see the note in Table I). This is because, for small-order quantities, it takes proportionately shorter time for weaving and processing the fabric material, and for garment manufacture. The delivery lead time Table II shows the results of the network analyses using PERT for the woven bulk order and woven test order. The critical path analysis indicates, for both types of orders, that there are nine critical activities. The critical activities in sequence are F (lab-dipping), G (lab-dip approval), N (fabric weaving/processing), O (dye-lots approval), P (fabric delivery), S (make pre-production sample), T (pre-production sample approval), U (garment manufacture), and V (quality inspection). The calculated expected delivery lead time is 68.6 days for bulk orders, with a standard deviation of 2.4 days. This analysis indicates that the two major activities, namely fabric weaving/processing and garment manufacture, accounts for about 46 per cent and 37 per cent, respectively, of the 68.6 days of garment delivery lead time. Only a meagre portion of about 17 per cent of the delivery lead time is held responsible by the remaining seven critical activities.
Order type Number of activities
Bulk order
Test order
22
22
Number of critical activities
9
9
Number of slack activities
13
13
Critical path
F-G-N-O-P-S-T-U-V
F-G-N-O-P-S-T-U-V
Expected delivery lead time (days)
68.6
39.8
Standard deviation (days)
2.4
2.1
Delivery lead time two standard deviations later than expecteda
73.4
44.0
Ninety-five per cent confidence interval for expected delivery lead time (days)
63.8-73.4
35.6-44.0
Note: a The probability of such happenings is only about 0.023, when assuming normal distribution
For the test order, the expected garment delivery lead time is 39.8 days, with a standard deviation of 2.1 days. The fabric weaving/processing activity and the garment manufacture activity account for about 36 per cent and 34 per cent, respectively, of the overall garment delivery lead time. This leaves about 30 per cent which will be accounted for by the remaining merchandising activities on the critical path. The implication from the critical path analysis is clear that, in order to further improve speed sourcing by reducing the delivery lead time in the merchandising process, three critical areas need detailed attention. First, since fabric weaving/processing is by far the longest activity, merchandisers of the buying office should work closely with fabric suppliers and weavers to reduce the time for fabric delivery. A genuine quick response programme should be set up between the parties concerned in order to make this work. Second, the garment manufacture activity should receive the same attention as the woven fabric supply because it is the second longest activity. The first and second critical areas warrant a separate thorough study. Third, another critical area for improvement mainly concerns the internal merchandising activities within the company itself, i.e. operational systems and procedures. These systems and procedures can be reviewed critically and then “re-engineered” so as to eliminate unnecessary activities or to shorten activity durations. Also bear in mind the interrelationship of this critical area and the other two, within the whole buying office’s operation. The point is to treat the three critical areas as subsystems within a grand system within which the clothing merchandising process plays a major role but does not act independently. Probability of late shipments Statistically, it is possible to make predictions about the chance of late shipments of garment delivery, given the information of time estimates for each
Quick response in clothing merchandising 51
Table II. Network analyses of merchandising activities: comparison of woven bulk and test order
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merchandising activity. Table II shows the delivery lead times for bulk orders and test orders when the garment shipments are assumed to be two standard deviations later than the expected or planned lead times. A discrepancy of two standard deviations is used only as a rule of thumb, since the probability of such an event is small – about two out of 100 times. The analysis shows that, for bulk orders, such delivery lead time for late shipments is 73.4 days, compared with the original expected lead time of 68.6 days – about five days behind schedule. For test orders, the delivery lead time for late shipments is 44 days, versus the original lead time of 39.8 days – about four days behind schedule. Such a probability analysis for possible late shipments will be helpful for merchandising managers to assess in advance the seriousness of shipments behind schedule. Contingency plans in their merchandising planning can then be well formulated. The PERT analysis results coincide very well with the normal lead time expected by the buying office. For the woven bulk order, 95 per cent confidence interval for the expected delivery lead time by PERT is 63.8-73.4 days. The interviewer was told during the interview that the normal delivery lead time was 65-75 days. Re-engineering the merchandising process During this study, an attempt was made to scrutinize the activities of the merchandising process in the buying office. The purpose was to eliminate unnecessary tasks so as to reduce the overall time to complete a garment order. Not only were activities on the critical path examined, but other merchandising activities or related activities were also looked into. As mentioned before, half of the total amount of work performed by merchandisers is carried out in the preproduction stage; therefore, it is natural to start looking in this area. First, if the onorder colours are to be simultaneously approved by the US buyer during the product development stage, the activities of lab-dipping and its approval can be eliminated from the PERT analysis. Lab-dipping can be proceeded far before the season starts. To reduce the sourcing time, merchandisers, at the beginning of each season, must get the lab-dip approval for the in-season colours which might be chosen for the forth-coming orders. Therefore, it can be considered to exclude activity F (lab-dipping) and activity G (lab-dip approval) from the PERT analysis. Second, the purpose of the pre-production sample is to control the fit of a particular style and ensure correct measurements before production starts. This sampling activity, to a certain extent, is good in assuring the quality of the garment delivery. However, it is a very time-consuming exercise since it normally needs about three days for factories to make the sample garments and half a day for merchandisers, together with quality assurance personnel, to approve the sample. Moreover, the pre-production sample cannot be made before the actual bulk fabric yardage, bulk trimmings and accessories have been delivered. If this activity can be eliminated, a lot of work will be reduced and the lead time shortened. However, this will require trust and a very close collaboration between
the fabric supplier, the buying office and the garment manufacturer. Thus, activity S (make pre-production sample) and activity T (pre-production sample approval) are excluded from the analysis. Third, the production of garments runs from the point when the fabric is cut to when the finished garment item is packed and awaiting shipment or collection by the forwarder. Activities in this production stage include garment manufacture, top-of-production sample review and quality inspection. This study shows that the activities of top-of-production review and quality inspection can be merged into one, thus shortening activity time. Fourth, order processing includes activity B (order confirmation), activity C (create cost sheet) and activity D (generate manufacturer purchase order), which are being done through the corporation’s worldwide computer network. In the past, it takes about four to five days for merchandisers in the buying office to complete these tasks. Even after receiving the order verbally from the marketing department in the US headquarters, the merchandisers have to wait for the order confirmation from the retail buyers. After this, they enter costing information regarding the order and send back to the marketing department, which will then generate the manufacturer production orders (MPO). The MPO will be generated electronically, printed and then passed to the manufacturer. The buying office has revised and upgraded the management information system. When excluding the above-mentioned four merchandising activities, followed by streamlining order processing and re-scheduling the remaining activities, a new activity network is created (Figure 2). Again, the new activity network is subjected to a PERT analysis. For the woven bulk order, the analysis results in a critical path with only five critical activities, which are E, N, R, U, and V. The expected garment delivery time for the new merchandising process is 63.5 days, about five days shorter than the original process of 68.6 days (see Table III).
Number of activities
Original operation
Re-engineered operation
22
18
Number of critical activities
9
5
Number of slack activities
13
13
Critical path
F-G-N-O-P-S-T-U-V
E-N-R-U-V
Expected delivery lead time (days)
68.6
63.5
Standard deviation (days)
2.4
2.5
Delivery lead time two standard deviations later than expecteda
73.4
68.5
Ninety-five per cent confidence interval for expected delivery lead time (days)
63.8-73.4
58.5-68.5
Note: a
The probability of such happenings is only about 0.023, when assuming normal distribution
Quick response in clothing merchandising 53
Table III. Network analyses of merchandising activities: comparison of original and reengineered operation for woven bulk order
Receive order
Figure 2. Merchandising activities network after process reengineering – woven products (critical path in bold line)
Woven-yardage for fit sample
K
Source trimming
Hand-written order to factories
Prepare bulk fabric order
Order confirmation
L
I
Fabric weaving/ processing
Create cost sheet
Make fit sample
Trimming approval
N
C
M
J
Dye-lots approval
Fit approval
Trimming delivery
O
Generate manufacturer purchase order
D
Q
Fabric delivery
Duplicate pattern
P
R
Lab-testing
V
U
54
H
A
E
B
Garment delivery
Quality inspection
Garment manufacture
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Conclusions The emergence of the quick response management philosophy in the clothing supply chain since the early 1980s has presented a significant competitive threat to Hong Kong’s position as a world clothing exporter. In Hong Kong, as most of the manufacturing and merchandising activities are geared to the needs of overseas markets, time-based advantage might often be the single most effective competitive edge. Quick response is a partnership strategy wherein retailers, manufacturers and materials suppliers collaborate to work together to shorten the pipeline from raw materials to ultimate consumers. This study has demonstrated quantitatively, by means of a network analysis of PERT, that fabric weaving and garment manufacture are the two most significant critical activities which determine the woven garment delivery lead times for a resident buying office in Hong Kong. For bulk orders of woven garments, the time required for sourcing and manufacturing fabric is by far the longest in the merchandising process, accounting for about half the total delivery lead time (about 69 days) of the garment product. The other major activity is garment manufacture, which also accounts for about one-third of the delivery lead time. The technique can also help to identify critical activities which are redundant, thus shortening the lead time and reducing the work load of the merchandiser. Analyses have shown that, through process re-engineering, the number of critical activities for woven bulk orders can be reduced from eight to five. There is also a saving of five days in the garment delivery lead time. Also, the technique provides a probability analysis of late shipments and the degree of influence of such an event on the delivery lead time. Confidence intervals have been calculated and the figures coincide very well with the normal lead time expected by the buying office. References 1. Disher, M., “High technology in the clothing industry”, Textile Outlook International, No. 38, November 1991, pp. 44-67. 2. Kurt Salmon Associates, The Hong Kong Textile and Clothing Industry: Techno-economic and Market Research Study, Industry Department of Hong Kong Government, 1992, pp. 75-6. 3. Dewitt, J., “The ultimate consumer connection”, Apparel Industry Magazine, Vol. 53 No. 9, September 1992, pp. 56-62. 4. Kincade, D.H., Cassill, N. and Williamson, N., “The quick response management system: structure and components for the apparel industry”, Journal of the Textile Institute, Vol. 84 No. 2, 1993, pp.147-55. 5. Taha, A., Operations Research: An Introduction, 4th ed., Macmillan, New York, NY, 1987. 6. Bohlinger, M.S., Merchandise Buying, 4th ed., Allyn & Bacon, Boston, MA, 1993, pp. 354-8. 7. Krajewski, L.J. and Ritzman, L.P., Operations Management: Strategies and Analysis, Addison-Wesley, Reading MA, 1987, pp. 646-78.
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