International Journal of Clothing Science and Technology Objective evaluation and prediction of properties of a fused panel Simona Jevsnik Jelka Gersak
Article information: To cite this document: Simona Jevsnik Jelka Gersak, (1998),"Objective evaluation and prediction of properties of a fused panel", International Journal of Clothing Science and Technology, Vol. 10 Iss 3/4 pp. 252 - 262 Permanent link to this document: http://dx.doi.org/10.1108/09556229810693645
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
Downloaded on: 14 April 2015, At: 03:40 (PT) References: this document contains references to 4 other documents. To copy this document:
[email protected] The fulltext of this document has been downloaded 402 times since 2006* Access to this document was granted through an Emerald subscription provided by 181692 []
For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. *Related content and download information correct at time of download.
IJCST 10,3/4
252
Objective evaluation and prediction of properties of a fused panel Simona Jevs˘nik and Jelka Ger˘sak
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia Introduction Evaluation and prediction of properties of a fused panel often present a difficult task because its properties depends on variety shell fabrics and fashion requirements, choice of fusible interlinings and fusing parameters. The expert’s selection of suitable fusible interlining and fusing parameters is based on experience, but it is not always reliable and accurate. How this problem can be avoided by using a system for automatic knowledge acquisition to make the right selection of fusible interlining and fusing parameters will be presented with this contribution. Machine learning as an alternative way to build the knowledge base helps experts in the decision-making process. At the same time the acquired knowledge is presented and the time for technical preparation of production is shortened.
International Journal of Clothing Science and Technology, Vol. 10 No. 3/4, 1998, pp. 252-262, MCB University Press, 0955-6222
Quality requirements of a fused panel During clothing manufacture processes it is very difficult to harmonise fashion and quality requirements of a produced garment. The most important questions are: will the produced garment fulfil aesthetic and functional requirements, will it be resistant to washing and dry cleaning as well as will the shape during wear be stable? The required properties of the textile surface as the assembling element of certain garments can be reached by stabilisation with the suitable adhesive interlining that can be fixed on the surface of a clothing part. A fused panel as a joined composite has specific properties with respect to the shell fabric and interlining. These properties take consequences in interactions, i.e. behaviour of the shell fabric and interlining in fused panel[1]. From this point of view it is important, for the selection of interlining, to know mechanical and physical properties of the fused panel and the built-in shell fabric and the interlining. A fused panel as a joined composite arises on the basis of joining the interlining with the shell fabric in the stabilisation process. To get the joint, the softened thermoplastic substance is, with the action of pressure, partly
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
impressed into the shell fabric and the interlining. The interlining is connected to the shell fabric. The strength of the connection depends on the adhesive forces between the thermoplastic substance and the fibre, or the shell fabric, respectively, and the cohesive forces in the thermoplastic substance as a polymer[2]. A fused panel that is a basic assembling element of sophisticated quality of the garments must influence the improvement of the appearance, the applicable properties and also mechanical properties. In this category there are: • hand value; • quality and strength of the connection; • flexibility; • shape stability; and • the duration of the joint. Influence of the type and quality of fusible interlining on the quality of produced garments The quality and the type of the shell fabric, as the main assembling element of a garment, are chosen already in the phase of garment model design and is considered as a constant[2]. This means that the required properties of the produced garment depend on known mechanical properties of the chosen shell fabric as well as on correct selection of fusible interlining. For this reason the fusible interlining has not just great influence on the hand value, but also on the aesthetic appearance, functionality, shape or model stability and on the final use of the garment. The selection of the appropriate interlining can be defined on the basis of adjusting to[1]: • base characteristics which determine the properties of the certain interlining in the process of stabilising and the final use; and • mechanical properties of the fusible interlining with respect to the used shell fabric. The basic properties, which determine the properties of the fusing interlining, are the type and the structure of the supporting material as a substrate and the type and deposit of the thermoplastic substance. Type and the structure of the supporting material, which is chosen with respect to the surface fabric weight and the thickness of this fabric, determine the mechanical properties of the interlining and influence the final applicable properties of the fused panels of garments. The type of thermoplastic substance that has the function of a joining connection element between the supporting material – interlining and the shell fabrics – dictates its use. The applicability depends on melting-point, melting-index, granulation, as well as on resistance against washing and drycleaning.
Objective evaluation and prediction 253
IJCST 10,3/4
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
254
Figure 1. The basis TDIDT algorithm
Expert systems and machine learning An expert system can be defined as computer program, which can present knowledge and perform decisions in the area where exact algorithm solutions do not exist. Here, an important role in problem solving has experience. The base components of an expert system are: knowledge base, which contains expert knowledge in defined area, inference engine, which uses the knowledge from knowledge base and user interface for communication between user and the system. To design a knowledge base of an expert system is very specialised and responsible work because it must include entire expert knowledge from the specific area[3]. In general two ways exist to build a knowledge base: (1) assembling of knowledge using human experience; and (2) assembling of knowledge with a system for automatic knowledge acquisition from a given set of examples. The essence of machine learning is construction of algorithms that are able to build the database which reflects the principles of a certain scientific field. Machine learning from a given set of examples with tree structured regression method is used for building of regression trees algorithm which belongs to the family of TDIDT algorithms (top-down induction of decision trees), The basis TDIDT algorithm could be presented in the following way, shown in Figure 1[4]. The tree structured regression is a technique which serves to find the functional dependence between dependent variables y and independent variable xi. This dependence is presented in the form of a regression tree. RETIS is a system for automatic knowledge acquisition from a given set of examples. The induced knowledge is represented by regression trees with a different degree of pruning. When learning regression trees with RETIS, examples have to be described with a set of attributes. Each attribute has its possible set of values. There are If all learning examples belong to a single class then terminate with a leaf labelled with that class else begin on the basis of the learning set choose the most informative attribute (using the entropy measure) for the root of the tree and partition the learning set into subsets according to the values of the selected attribute; for each value do recursively construct a subtree with the corresponding subset of examples end
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
two kinds of attributes: continuous attributes (their value can be any real number) and discrete attributes which can have a value from some predefined set of values. Each example has also associated class value, which is continuous and represents the quantity we want to learn. Therefore, the program actually learns a function y(x1 … xn), which approximates the relationship between the values of the attributes and the value of the class. Each internal node of a regression tree contains a test on a value of an attribute. According to the result of the test, interpretation of the tree proceeds to the left or to the right subtree of the node. A leaf prescribes a value to a function, approximated by the regression tree. The quality of the constructed tree is measured by the mean squared error R of a tree T, defined with; (1) where: N = number of testing examples; = the actual value of the class of the ith example; yi → x = the value of the ith example; → y( xi) = a value of the class estimated by a regression tree. To enable comparison of the quality of several trees, possibly from different domains, one uses the relative mean squared error, defined as: (2) The mean squared error of the tree is normalised by the mean squared error of the predictor, which always predicts the mean value of the training example set. Methodology On the basis of analyses of achieved results of shell fabrics, fusible interlining, fusing parameters and bond strength of fused panels a set of examples was constructed with the purpose of predicting the bond strength of a fused panel. To construct the learning and testing set of examples 60 woollen shell fabrics were used. Those fabrics were stabilised with three different fusible interlinings. The shell fabrics had different surface fabric weight, weft and warp density, colour and weave; 21 shell fabrics were in plain weave, P 1⁄1 , 28 were in twill weave, K 1⁄2 and 11 were in twill weave, K 2⁄2. Fusible interlinings differed in raw material, weave and type of adhesive (Table I). Experimental work The learning set of examples was constructed to give a rule for prediction of bond strength of fused panel.
Objective evaluation and prediction 255
IJCST 10,3/4
Research parameters Raw material
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
256
Table I. List of properties of used interlinings
Code of fusible interlining FI-2
FI-1
FI-3
28% PA 72% CV
100% Co
100% Co
twill
plain
plain
Adhesive type
PA
VTPE
PA
Warp density (yarns/cm)
11
24
24
Weft density (yarns/cm)
13
13
10.5
Warp linear density (Tt/tex)
4.4
20
17
Weft linear density (Tt/tex)
36
20
17
Surface fabric weight (m/gm–2)
75
85
65
Weave
For a constructed learning set of examples the following were carried out: • analyses of mechanical and physical properties of shell fabrics and fusible interlinings. The measurements have been performed on FAST system for objective evaluation of properties of shell fabrics; and • analyses of quality and bond strength of fused panel by DIN 54 310. Fusing of shell fabrics and fusible interlinings was performed on a continuous press machine. Fusing parameters i.e. temperature, time and pressure of fusing, have been determined on the basis of previous testing and achieved quality of fused panel (Table II). Recommendations of manufactures of interlining have been also considered. All measurements were carried out at standard testing conditions (20±2°C and 65±2 per cent RH).
Fusing parameters Fusing temperature T/°C Table II. List of selected fusing parameters
Fusing pressure p/Ncm–2 Fusing time t/s
FI-1
Code of fusible interlining FI-2
FI-3
135
125
130
3
2
4
15
8
10
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
The learning set contained 165 examples. The value of the class was described with 21 attributes, out of which 16 were continuous and five discrete. A list of attributes used is given in Table III. Regression tree for prediction of bond strength of fused panel was constructed using the program RETIS and learning set of examples. Comparison between predicted values and measured values of bond strength was made on the basis of testing a set of examples which contained 15 examples.
Objective evaluation and prediction 257
Results The results of research for prediction of bond strength of analysed shell fabrics regarding the applied fusible interlining and fusing parameters are given in a form of: • results of measured values of bond strength of fused panels; • graphical presentation of regression tree for prediction of bond strength; and • analyses of comparison between predicted and measured values of bond strength. The achieved values of measured bond strengths are shown in Table IV.
Name of attribute
Type of attribute
Fusing_temp C Fusing_press Ncm2 Fusing_time_t Weave_SF Warp_density_SF-1_yarns/cm Weft_density_SF-2_yarns/cm Linear density_SF-1_TEX Linear density_SF-2_TEX Thickness_SF_MM E100-1_SF_% E100-2_SF_% RS-1_SF_% RS-2_SF_% Type_FI Type_of_resin Forms_of_adhesives Raw_material_FI Warp_density_FI-1_yarns/cm Weft_density_FI-2_yarns/cm Linear density_FI-1_TEX Linear density_FI-2_TEX
continuous continuous continuous discrete continuous continuous continuous continuous continuous continuous continuous continuous continuous discrete discrete discrete discrete continuous continuous continuous continuous
Table III. List of used attributes of predicted class
IJCST 10,3/4
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
258
Table IV. Average values of bond strength of fused panel
Code SF-01_FI-1 SF-02_FI-1 SF-03_FI-1 SF-04_FI-1 SF-05_FI-1 SF-06_FI-1 SF-07_FI-1 SF-08_FI-1 SF-09_FI-1 SF-10_FI-1 SF-11_FI-1 SF-12_FI-1 SF-13_FI-1 SF-14_FI-1 SF-15_FI-1 SF-16_FI-1 SF-17_FI-1 SF-18_FI-1 SF-19_FI-1 SF-20_FI-1 SF-21_FI-1 SF-22_FI-1 SF-23_FI-1 SF-24_FI-1 SF-25_FI-1 SF-26_FI-1 SF-27_FI-1 SF-28_FI-1 SF-29_FI-1 SF-30_FI-1 SF-31_FI-1 SF-32_FI-1 SF-33_FI-1 SF-34_FI-1 SF-35_FI-1 SF-36_FI-1 SF-37_FI-1 SF-38_FI-1 SF-39_FI-1 SF-40_FI-1 SF-41_FI-1 SF-42_FI-1 SF-43_FI-1 SF-44_FI-1 SF-45_FI-1 SF-46_FI-1 SF-47_FI-1 SF-48_FI-1
Bond strength F/N/5cm2
Code
Bond strength F/N/5cm2
Code
10.84 10.08 10.78 10.02 7.12 9.8 8.32 10.77 10.78 9.32 9.64 10.76 9.71 9.77 8.03 8.87 8.8 8.65 8.61 8.91 10.73 10.4 10.05 9.99 9.25 8.67 7.47 9.24 7.76 7.27 8.46 7.1 8.67 8.73 8.14 11.21 10.77 10.42 12.49 10.94 12.07 12.42 12.84 12.6 12.76 10.62 12.04 13.82
SF-01_FI-2 SF-02_FI-2 SF-03_FI-2 SF-04_FI-2 SF-05_FI-2 SF-06_FI-2 SF-07_FI-2 SF-08_FI-2 SF-09_FI-2 SF-10_FI-2 SF-11_FI-2 SF-12_FI-2 SF-13_FI-2 SF-14_FI-2 SF-15_FI-2 SF-16_FI-2 SF-17_FI-2 SF-18_FI-2 SF-19_FI-2 SF-20_FI-2 SF-21_FI-2 SF-22_FI-2 SF-23_FI-2 SF-24_FI-2 SF-25_FI-2 SF-26_FI-2 SF-27_FI-2 SF-28_FI-2 SF-29_FI-2 SF-30_FI-2 SF-31_FI-2 SF-32_FI-2 SF-33_FI-2 SF-34_FI-2 SF-35_FI-2 SF-36_FI-2 SF-37_FI-2 SF-38_FI-2 SF-39_FI-2 SF-40_FI-2 SF-41_FI-2 SF-42_FI-2 SF-43_FI-2 SF-44_FI-2 SF-45_FI-2 SF-46_FI-2 SF-47_FI-2 SF-48_FI-2
7.11 7.43 7.08 7.81 7.23 6.81 7.06 6.95 7.02 6.21 7.37 6.96 6.82 6.89 6.03 7.56 7.26 6.32 5.98 6.31 6.96 7.75 7.23 7.21 7.65 7.18 6.53 6.22 6.61 6.47 6.08 6.31 6.28 6.24 6.11 9.19 11.89 8.76 10.89 10.84 11.04 11.75 9.84 10.53 12.47 10.68 11.27 13.38
SF-01_FI-3 SF-02_FI-3 SF-03_FI-3 SF-04_FI-3 SF-05_FI-3 SF-06_FI-3 SF-07_FI-3 SF-08_FI-3 SF-09_FI-3 SF-10_FI-3 SF-11_FI-3 SF-12_FI-3 SF-13_FI-3 SF-14_FI-3 SF-15_FI-3 SF-16_FI-3 SF-17_FI-3 SF-18_FI-3 SF-19_FI-3 SF-20_FI-3 SF-21_FI-3 SF-22_FI-3 SF-23_FI-3 SF-24_FI-3 SF-25_FI-3 SF-26_FI-3 SF-27_FI-3 SF-28_FI-3 SF-29_FI-3 SF-30_FI-3 SF-31_FI-3 SF-32_FI-3 SF-33_FI-3 SF-34_FI-3 SF-35_FI-3 SF-36_FI-3 SF-37_FI-3 SF-38_FI-3 SF-39_FI-3 SF-40_FI-3 SF-41_FI-3 SF-42_FI-3 SF-43_FI-3 SF-44_FI-3 SF-45_FI-3 SF-46_FI-3 SF-47_FI-3 SF-48_FI-3
Bond Strength F/N/5cm2 8.94 8.81 9.07 7.08 6.77 7.42 7.99 8.80 7.88 9.03 7.39 7.68 7.50 7.22 8.52 8.14 9.70 9.17 8.83 7.87 9.05 8.18 8.74 8.96 8.66 8.96 9.21 9.28 9.54 9.53 9.56 9.65 9.35 8.59 7.66 8.44 8.38 7.32 9.61 9.33 8.83 8.97 8.91 9.05 8.49 9.18 10.21 12.31 (Continued)
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
Code SF-49_FI-1 SF-50_FI-1 SF-51_FI-1 SF-52_FI-1 SF-53_FI-1 SF-54_FI-1 SF-55_FI-1 SF-56_FI-1 SF-57_FI-1 SF-58_FI-1 SF-59_FI-1 SF-60_FI-1
Bond strength F/N/5cm2 11.35 11.97 11.23 9.02 10.97 10.68 9.31 9.85 10.76 9.08 11.82 12.21
Code
Bond strength F/N/5cm2
Code
Bond Strength F/N/5cm2
SF-49_FI-2 SF-50_FI-2 SF-51_FI-2 SF-52_FI-2 SF-53_FI-2 SF-54_FI-2 SF-55_FI-2 SF-56_FI-2 SF-57_FI-2 SF-58_FI-2 SF-59_FI-2 SF-60_FI-2
10.75 14.03 12.64 6.71 7.58 7.51 7.39 6.48 7.17 7.4 10.75 8.93
SF-49_FI-3 SF-50_FI-3 SF-51_FI-3 SF-52_FI-3 SF-53_FI-3 SF-54_FI-3 SF-55_FI-3 SF-56_FI-3 SF-57_FI-3 SF-58_FI-3 SF-59_FI-3 SF-60_FI-3
10.39 11.95 11.50 8.26 9.14 8.18 8.08 7.34 8.06 7.27 9.30 9.08
Objective evaluation and prediction 259
Table IV.
The regression tree, written in file TRDN.TRT, was constructed from the file with learning examples TRDN.RDA and file with domain definition TRDN.RDO. The regression tree contained from 16 nodes and 17 leaves. A part of a regression tree for prediction of bond strength is shown in Figure 2. The testing of the quality of the regression tree was carried out with 15 randomly selected examples (Table V). Furthermore, the comparison between predicted and measured values of bond strength was elaborated with linear coefficient correlation. Linear correlation coefficient is shown in Figure 3.
RE-2_OT_% =0.6 Density_yarn_SF-2_yarn/cm
11.53±1.28 =17.75 9.16±1.09
8.96±0.40
Figure 2. Part of a regression tree for prediction of bond strength of fused panel
IJCST 10,3/4
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
260
Table V. Comparison between predicted and measured values of bond strength
Figure 3. Correlation between measured and predicted values of bond strength
Measured value of bond strength F/N/5cm
Predicted value of bond strength F/N/5cm
7.81 6.21 7.26 7.23 6.61 8.76 10.53 9.64 8.91 7.47 12.49 12.28 15.65 5.56 12.21
6.78 6.78 6.78 8.54 7.38 6.36 11.05 9.92 9.16 8.19 12.14 11.05 13.08 8.19 10.29
Predicted value of bond strength. F/N/5cm 16 14 12 10 8 6 4 4 6 8 10 12 14 16 Measured value of bond strength F/N/5cm
18
Discussion From achieved results of measurement of mechanical and physical properties of shell fabrics it can be seen that all the shell fabrics have a high relaxation shrinkage, which has a negative influence on dimension stability of garment parts. Relaxation shrinkage is higher in weft direction than in warp direction in all shell fabric; however, it stays within permitted borders. Results of research work indicated that the same fusing parameters do not ensure the same bond strength even if the same shell fabric fused with identical fusing interlining has been used. At the same time also weave, weft and warp density of shell fabric have influence on bond strength. The reason is different influence on the adhesive forces to the shell fabric and different heat conductibility of analysed shell fabrics.
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
It can be seen that the bond strength of the fused panel has values within the range of 7 to 14 N/5cm for twill K1⁄2 and K 2⁄2 weave and that it is higher than for plain weave P 1⁄1 which values are within the range of 6.2 to 10.8 N/5cm. Also the fusible interlining structure influenced the bond strength. It can be seen from results that fused panels fused with interlining noted as FI-1 had in average for 1.3 N/5cm higher bond strength than fused panel fused noted as FI3 and for 1.9 N/5cm when compared with the fusible interlining noted as FI-2. Furthermore, the results of bond strength of fused panel showed that all the values were within allowed border. The fused panel of shell fabric in twill weave K 2⁄2 had an average for 3 N/5cm higher bond strength than the shell fabric in twill K1⁄2 and plain weave P 1⁄1. When predicting the bond strength of woollen shell fabrics and fusible interlinings using the regression tree we simply follow the values of attributes in nodes and read the class value in the leaf. For example, the predicted bond strength 10.36±0.93 N/5cm is achieved for a fused panel in weave twill with the following properties: • warp density: 30.19 yarns/cm; • weft density: 24 ˘st. yarns/cm; • warp linear density: 19.23 tex; • weft linear density: 14.29 tex; • thickness of shell fabric: 0.382 mm; • extensibility in warp, E100-1: 1.8 per cent; • extensibility in weft, E100-2: 3.6 per cent; • relaxation shrinkage in warp direction, RS-1: 3 per cent; • relaxation shrinkage in warp direction, RS-2:3.4 per cent; the fusible interlining had the following characteristics: • type of fusible interlining: twill • adhesive type : PA • form of adhesive: random • material of fusible interlining: 31% PA, 69% CV • warp density: 11 yarns/cm • weft density: 13 yarns/cm • warp linear density: 4.4 tex • weft linear density: 36 tex fused following fusing conditions under the: • fusing temperature: 135°C • Fusing pressure: 3 Ncm–2 • fusing time: 15s.
Objective evaluation and prediction 261
IJCST 10,3/4
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
262
Achieved results of bond strength predicted with regression tree showed that they are more reliable in the case of new fabrics than new interlining. The reason lies in the greater number of different shell fabrics included in the learning set of examples. Coefficient of correlation between measured values of bond strength and those predicted using the testing set was 0.87, which showed good agreement between them. Conclusions Owing to the interacting influence of shell fabric, fusible interlining and fusing parameters evaluation of properties of fused panel was based on investigation and subjective evaluation. Application of machine learning from a set of examples using program RETIS proved to be a successful and promising technique. It also indicated some important direction for future activities. References 1. Ger˘sak, J., “Proper evaluation and quality choice of fusible interlining DWI reports”, Deutschland Wollforschungsinstitut an der Technischen Hochschule Aachen, Aachen 1996, pp. 499-506. 2. Ger˘sak, J., “Objective evaluation of heat-set garment parts”, Tekstil, Vol. 46 No. 4, 1997, pp. 193-203. 3. Stjepanovi´c , Z., “Employment of information technologies in spun yarn production – a case study”, The 78th World Conference of the Textile Institute in association with The 5th Textile Symposium of SEVE and SEPVE, Vol. 1, 1997, pp. 271-85. 4. Karali˘c , A. and Cestnik, B., “The Bayesian approach to tree-structures regression”, Proceedings of ITI-91, Cavtat, 1991.
This article has been cited by:
Downloaded by ISTANBUL TECHNICAL UNIVERSITY At 03:40 14 April 2015 (PT)
1. C. Ball, D. Fairclough, J.E. Ruckman. 2001. Men’s tailored jackets: objective measurement and consumer perception. Journal of Fashion Marketing and Management: An International Journal 5:3, 189-198. [Abstract] [PDF]
