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Comparison of artificial neural network and adaptive neuro-fuzzy inference system for predicting the wrinkle recovery of woven fabrics a




Tanveer Hussain , Zulfiqar Ali Malik , Zain Arshad & Ahsan Nazir a

Faculty of Engineering & Technology, National Textile University Faisalabad, Pakistan Published online: 01 Sep 2014.

To cite this article: Tanveer Hussain, Zulfiqar Ali Malik, Zain Arshad & Ahsan Nazir (2014): Comparison of artificial neural network and adaptive neuro-fuzzy inference system for predicting the wrinkle recovery of woven fabrics, The Journal of The Textile Institute, DOI: 10.1080/00405000.2014.953790 To link to this article:

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The Journal of The Textile Institute, 2014

Comparison of artificial neural network and adaptive neuro-fuzzy inference system for predicting the wrinkle recovery of woven fabrics Tanveer Hussain, Zulfiqar Ali Malik, Zain Arshad and Ahsan Nazir* Faculty of Engineering & Technology, National Textile University Faisalabad, Pakistan

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(Received 23 April 2014; accepted 7 August 2014) The aim of this study was to compare the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for predicting the wrinkle recovery of polyester/cotton woven fabrics. The prediction models were developed using experimental data-set of 115 fabric samples of different constructions. Warp and weft yarn linear densities, ends/25 mm and picks/25 mm, were used as input/predictor variables, and warp and weft crease recovery angles (CRA) as output/response variables. It was found that the prediction accuracy of the ANN models was slightly better as compared with that of ANFIS models developed in this study. However, the ANFIS models could characterize the relationships between the input and output variables through surface plots, which the ANN models could not. The developed models may be used to optimize the fabric construction parameters for maximizing the wrinkle recovery of polyester/cotton woven fabrics. Keywords: wrinkle recovery; woven fabric; prediction; ANN; ANFIS

Introduction Wrinkle recovery is the ability of a fabric to recover from any folding deformation (Farnfield & Alvey, 1978). It is one of the key properties desirable in fabrics used for apparel, to enhance their visual esthetics as well as easy-care properties. Different fiber, yarn, and fabric properties as well as the fabric finishing parameters are known to affect the wrinkle recovery of woven fabrics. Fiber type, its cross-section (Omeroglu, Karaca, & Becerir, 2010) and inter-fiber friction have been identified as important factors affecting the wrinkle recovery in fabrics (Daniels, 1960). Yarn twist level and inter-fiber cohesion within the yarn structure also affect the fabric wrinkle recovery properties (Gokarneshana, Subramaniam, & Anbumani, 2008; Steele, 1956). The effect of various fabric parameters on the wrinkle recovery properties of woven fabrics has also been reported in various studies (Jameson, Whittier, & Schiefer, 1952; Krasny, Mallory, Phillips, & Sookne, 1955; Nassif, 2012; Vasile, Ciesielska-Wrobel, & Langenhove, 2012). There have been a few attempts in the past to model the wrinkle recovery behavior of woven fabrics. A theoretical model comprising a frictional element in parallel with a generalized linear viscoelastic element was proposed by Chapman (1974). Some improvements in the Chapman’s model have also been proposed (Shi, Hu, & Yu, 2000; Shi & Wang, 2009). However, little work has been reported on the development of empirical models for predicting the wrinkle recovery of woven fabrics. *Corresponding author. Email: [email protected] © 2014 The Textile Institute

Although wrinkle recovery is a major concern for 100% cotton fabrics, but the polyester/cotton blended fabrics with substantial amount of cotton also have poor wrinkle recovery. This study is based on 52:48 blend ratio of polyester/cotton fabrics which have poor wrinkle recovery as compared with 65:35 polyester/cotton blended fabrics. Artificial neural networks (ANN) have been applied extensively in modeling and predicting textile behavior (Chattopadhyay & Guha, 2004; Vassiliadis, Rangoussi, Cay, & Provatidis, 2010). Some recent studies on the application of ANN in predicting textile behavior include: prediction of tensile properties of cotton/spandex core-spun yarns (Almetwally, Idrees, & Hebeish, 2014), prediction of tensile strength of polyester/cotton-blended woven fabrics (Malik, Haleem, Malik, & Tanwari, 2012), prediction of thermal resistance of woven fabrics (Mitra, Majumdar, Majumdar, & Bannerjee, 2013), prediction of UV radiation protection of polyester/cotton-blended fabrics (Hatua, Majumdar, & Das, 2013), and prediction of drape profile of woven fabrics (Pattanayak, Luximon, & Khandual, 2011). Adaptive neuro-fuzzy inference systems (ANFIS) have also been used quite successfully in recent years for predicting different properties of fabrics including: bursting strength of knitted fabrics (Jamshaid, Hussain, & Malik, 2013), load-extension behavior of woven fabrics (Hadizadeh & Jeddi, 2010), and bending rigidity of woven fabrics (Behera & Guruprasad, 2012). The aim of this study was to develop and compare the ANN and


T. Hussain et al.

ANFIS models for predicting the wrinkle recovery of polyester/cotton woven fabrics.

Table 2.

Variables and range of values used.

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Materials and methods One hundred and twenty-eight (128) fabric samples were woven on Sulzer weaving machine (P 7150) in 3/1 twill weave design using polyester/cotton (52/48) blended yarns in both warp and weft. Yarn specifications are given in Table 1. All the fabric samples were desized on jigger machine using 5 g/L amylases enzyme at 5.5 pH and 60°C temperature for 30 min, followed by rinsing and drying. After desizing, the fabric specimens were placed in hot air oven for preconditioning at a temperature of 47 °C and relative humidity of 10–25% for 4 h, and then for conditioning for 24 h in standard atmospheric conditions according to ASTM D 1776. After conditioning the samples, actual fabric constructions including warp yarn linear density, weft yarn linear density, ends per 25 mm and picks per 25 mm, were determined again, because these may have changed after any possible shrinkage during desizing. Total number of warp Yarns and weft yarns in 25 mm length were counted according to ASTM D 3775. Linear density of the warp and weft yarns was determined according to ASTM D 1059-01. The crease recovery angle (CRA) of the fabric specimens was tested according to AATCC Test Method 66. Out of the total 128 fabric samples, the data of 115 samples (Table 2) were used for developing the prediction models, while the data of remaining 13 samples were selected for validation of the models as hidden data-set. In order to get higher productivity and a balanced fabric structure, number of picks in woven fabrics is always kept lower or equal to the number of ends in the fabric. If a statistical experimental design such as full factorial is used, then there are some fabric samples which have more number of picks than the ends, which are practically not feasible to produce in Table 1.

Yarn specifications. Linear density (tex)

S. no


1 2 3 4 5 6 7 8 9 10 11

Actual linear density (tex) Linear density CV % CVm % CVb % Hairiness Imperfection Breaking force (CN) CV for breaking force % Breaking tenacity (CN/tex) Breaking elongation % CV for elongation %




14.66 1.23 15.74 1.70 5.79 956 272.47 9.45 18.59 7.25 9.85

19.77 1.54 17.24 1.97 6.16 785 391.82 8.90 19.82 7.96 9.90

24.86 1.25 15.19 2.75 6.72 731 537.93 8.50 18.59 8.19 10.10

Predictor variables Warp count, tex Weft count, tex Ends/25 mm Picks/25 mm

Range of values (on-loom fabric)

Range of values (fabric after desizing)





15 15 40 40

25 25 80 80

15 15 41 41

26 26 85 85

weaving. The fabric constructions selected in this study are those which are not only practically feasible in weaving, but also cover a broad range of areal density for different end uses. Development of ANN models The ANN Toolbox of MATLAB R2008b was used for developing the ANN models. Two ANN models were designed separately for warp-way and weft-way CRA prediction. ANNwp was developed for the prediction of warp-way CRA, while ANNwt for weft-way CRA. The most important component of ANN modeling is the network architecture, including number of neurons, hidden layers, and training functions, which is optimized through trial and error. In this study, networks developed for both the warp and weft-way wrinkle recovery were singlelayered feed-forward back propagation networks, which are one of the most commonly used ANN. The number of hidden neurons for both the models was four. Both the networks were trained with ‘trainlm’ function which uses Levenberg-Marquardt algorithm. However, the network architecture, including number of neurons, hidden layers and training functions, was optimized through trial and error. In this study, 115 total input–output patterns each for warp- and weft-way wrinkle recovery were divided into training, testing and validation sets as 70, 15, and 15%, respectively. The network performance function was set to ‘mse’ which validates the performance on basis of mean squared error. Backpropagation is a gradient descent algorithm and supervised form of learning from the input and output pattern of data given to the network. Backpropagation feed-forward networks work on the principle of adjustment of initially set network weights according to the given output by each observation presented during training session. The weight adjustment process continues until the training error attains the minimum possible value. The change in the weights of the network input variables is governed by the rule given in the following equation: DW ji ¼ Kdpj ipi ;

The Journal of The Textile Institute where Δp Wji represents the change to be carried out in the weight of the link that is connecting the ith and jth units when the p pattern is given to the network for training. The constant K represents the learning rate of the neural network; δpj is the error between the target and actual output, while ipi is the value of the ith element of the input pattern (Wang & Fu, 2008).

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Development of ANFIS models The same data-set that was used for developing the ANN model was used for developing the ANFIS model. The ANFIS models were also developed using MATLAB R2008b. Figure 1 shows the structure of the developed ANFIS. The ANFIS structure consists of four input variables, viz. warp tex, weft tex, ends/25 mm, picks/25 mm, each with three triangular membership functions (MF). There is one output variable i.e. CRA, each for warp and weft fabric directions. Both the ANFIS models for warp and weft are based on 81 if-then rules of the form: If w is A1, x is B1, y is C1, and D1 is z, then output = k1w + k2x + k3y + k4z + k5 where w, x, y, and z are inputs; A, B C, and D are fuzzy MF for corresponding inputs; and k1, k2, k3, k4, and k5 are constants determined by training the model. The Sugenotype fuzzy inference systemwas generated using grid partition method, and the training of the system was accomplished using hybrid learning algorithm which applies a combination of least-squares method and the backpropagation gradient descent method. The number and type of MFs for different inputs were determined through trial and error to result in a model with good fit and prediction accuracy of unknown input values.

determined by the ANFIS. Figures 2 and 3 show that the warp CRA increases with increase in ends/25 mm, while the weft CRA improves with increase in picks/25 mm. The results are in agreement with a previous study (Mori & Matsudaira, 2007). As the number of yarns in a fabric direction increases, their collective resistance to wrinkling and ability to recover from wrinkling in that direction also increases. The interpolations of the surface plots in Figures 2 and 3 need to be carefully interpreted. Valid conclusions can only be drawn by considering those parts of the surface plots where the number of ends is equal or greater than the number of picks, since there were no fabric samples in the database which had more number of picks than the ends. Such fabrics with more number of picks than the ends are not commercially manufactured in textile mills, because of low productivity in weaving as the weaving productivity depends on the number of picks inserted per unit time. Therefore, such samples were not manufactured and included in the study.

Results and discussion The relationships between the inputs and the output variables were characterized through surface plots as

Figure 2.

Effect of ends and picks on warp CRA.

Figure 1.

Figure 3.

Effect of ends and picks on weft CRA.

ANFIS structure.


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T. Hussain et al.

Figure 3 depicts that at 50 ends/25 mm, increase in picks/25 mm from 50 to 80 does not result in any increase in CRA. This is not a valid conclusion since in actual there were no samples in which picks were greater than the ends. The surface plot in Figure 3 corresponding only to 80 ends/25 mm should be considered to make valid conclusions, since in the sample database, all the fabric samples either had less number of picks than the ends or equal. Considering this part of the surface plot in Figure 3, it can be concluded that with increase in number of picks, the wrinkle resistance of the fabric in weft direction increases Figures 4 and 5 show that increase in warp yarn tex improves the warp CRA and increase in weft yarn tex results in increase in weft CRA. As the yarn becomes coarser with increase in tex, its bending rigidity increases leading to increase in its resistance to

Figure 4. Table 3.

Figure 5.

Effect of warp and weft tex on weft CRA.

wrinkling and improved recovery from wrinkling. The yarns with higher tex are thicker and larger in diameter. Fabrics made from thicker yarns offer more resistance to the formation of wrinkles as compared with those made from the thinner yarns. When thicker yarns are bent under pressing load for wrinkling, they tend to recover back because of better resilience. Hence, the fabrics made from higher tex yarns give overall better wrinkle recovery results. Validation of the prediction models Out of 128 fabrics samples, 13 each were used to check the validity of the developed models for warp and weft CRA. A comparison of actual CRA values and those predicted by the developed ANN and ANFIS models is shown in Table 3. The Pearson correlations between the actual and the predicted warp CRA by the ANN and ANFIS models were found to be 0.974 (P-value 0.000)

Effect of warp and weft tex on warp CRA. Comparison of actual and predicted CRA values.

Warp CRA Predicted No. 1 2 3 4 5 6 7 8 9 10 11 12 13 Mean

Warp tex

Weft tex

15 15 15 15 21 21 21 21 21 26 26 26 26 20.6

15 21 26 26 15 15 21 21 26 15 15 15 21 19.3

Weft CRA Absolute error %


Absolute error %













75 84 72 81 75 81 61 83 73 63 76 83 74 75.5

44 83 43 65 53 43 42 64 54 42 63 63 53 54.8

91 117 87 105 97 105 85 115 96 96 120 130 109 104.1

93 111 86 104 96 108 84 112 92 96 119 134 115 103.8

98 126 87 104 95 106 78 110 94 105 117 126 110 104.3

2 5 1 1 1 3 1 3 4 0 1 3 6 2.4

8 8 0 1 2 1 8 4 2 9 3 3 1 3.8

64 139 72 108 76 68 71 101 91 66 90 87 82 85.8

63 134 78 111 80 66 73 100 88 70 87 87 77 85.7

69 142 78 114 80 65 76 93 88 71 85 79 74 85.7

1.6 3.6 8.3 2.8 5.3 2.9 2.8 1.0 3.3 6.1 3.3 0.0 6.1 3.6

7.8 2.2 8.3 5.6 5.3 4.4 7.0 7.9 3.3 7.6 5.6 9.2 9.8 6.5

The Journal of The Textile Institute and 0.931 (P-value 0.000), respectively. The Pearson correlations between the actual and the predicted weft CRA by the ANN and ANFIS models were found to be 0.986 (P-value 0.000) and 0.962 (P-value 0.000), respectively. Overall, ANN models were found to exhibit less absolute error (%) as compared with the ANFIS models.

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Conclusion ANN and ANFIS models were developed for predicting the warp and weft CRAs of polyester/cotton woven fabrics by taking warp and weft yarn linear densities, ends/25 mm and picks/25 mm, as predictor variables. It was found that the fabric wrinkle recovery in warp or weft direction increases with increase in yarn linear density and the fabric density in that direction. It was found that both the ANN and ANFIS models have the ability to predict warp and weft wrinkle recovery with very good accuracy, with ANN models being slightly better in performance. The developed models could be used for optimizing the woven fabric wrinkle recovery through appropriate selection of the fabric construction parameters. References Almetwally, A. A., Idrees, H. M., & Hebeish, A. A. (2014). Predicting the tensile properties of cotton/spandex corespun yarns using artificial neural network and linear regression models. The Journal of The Textile Institute, 105, 1–9. Behera, B., & Guruprasad, R. (2012). Predicting bending rigidity of woven fabrics using adaptive neuro-fuzzy inference system (ANFIS). The Journal of The Textile Institute, 103, 1205–1212. Chapman, B. (1974). A model for the crease recovery of fabrics. Textile Research Journal, 44, 531–538. Chattopadhyay, R., & Guha, A. (2004). Artificial neural networks: Applications to textiles. Textile Progress, 35, 1–46. Daniels, W. (1960). Relationship between fiber properties and fabric wrinkle recovery. Textile Research Journal, 30, 656–661. Farnfield, C., & Alvey, P. (1978). Textile terms and definitions (5th ed.). Manchester, NH: Redwood Burn. Gokarneshana, N., Subramaniam, V., & Anbumani, N. (2008). Influence of material and process parameters on the interfibre cohesion in ring-spun yarns. Indian Journal of Fibre & Textile Research, 33, 203–212. Hadizadeh, M., & Jeddi, A. A. (2010). Application of an adaptive neuro-fuzzy system for prediction of initial load – Extension behavior of plain-woven fabrics. Textile Research Journal, 80, 981–990. Hatua, P., Majumdar, A., & Das, A. (2013). Predicting the ultraviolet radiation protection by polyester–cotton blended


woven fabrics using nonlinear regression and artificial neural network models. Photodermatology, Photoimmunology and Photomedicine, 29, 182–189. Jameson, L. H., Whittier, B. L., & Schiefer, H. F. (1952). Factors affecting the properties of rayon fabrics. Textile Research Journal, 22, 599–608. Jamshaid, H., Hussain, T., & Malik, Z. A. (2013). Comparison of regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics. Fibers and Polymers, 14, 1203–1207. Krasny, J., Mallory, G., Phillips, J., & Sookne, A. (1955). Part II: Effect of construction on crease recovery of fortisan fabrics. Textile Research Journal, 25, 499–506. Malik, Z. A., Haleem, N., Malik, M. H., & Tanwari, A. (2012). Predicting the tensile strength of polyester/cotton blended woven fabrics using feed forward back propagation artificial neural networks. Fibers and Polymers, 13, 1094–1100. Mitra, A., Majumdar, A., Majumdar, P. K., & Bannerjee, D. (2013). Predicting thermal resistance of cotton fabrics by artificial neural network model. Experimental Thermal and Fluid Science, 50, 172–177. Mori, M., & Matsudaira, M. (2007, August). The effect of weave density on fabric handle and appearance of men’s suit fabrics. Research Journal of Textile and Apparel, 11, 71–80. Nassif, G. A. A. (2012). Effect of weave structure and weft density on the physical and mechanical properties of micro polyester woven fabrics. Life Science Journal, 9, 1326–1331. Omeroglu, S., Karaca, E., & Becerir, B. (2010). Comparison of bending, drapability and crease recovery behaviors of woven fabrics produced from polyester fibers having different cross-sectional shapes. Textile Research Journal, 80, 1180–1190. Pattanayak, A. K., Luximon, A., & Khandual, A. (2011). Prediction of drape profile of cotton woven fabrics using artificial neural network and multiple regression method. Textile Research Journal, 81, 559–566. Shi, F. J., Hu, J., & Yu, T. (2000). Modeling the creasing properties of woven fabrics. Textile Research Journal, 70, 247–255. Shi, F., & Wang, Y. (2009). Modelling crease recovery behaviour of woven fabrics. The Journal of The Textile Institute, 100, 218–222. Steele, R. (1956). The effect of yarn twist on fabric crease recovery. Textile Research Journal, 26, 739–744. Vasile, S., Ciesielska-Wrobel, I. L., & Langenhove, L. (2012). Wrinkle recovery of flax fabrics with embedded superelastic shape memory alloys wires. Fibres & Textiles in Eastern Europe, 20, 56–61. Vassiliadis, V., Rangoussi, M., Cay, A., & Provatidis, C. (2010). Artificial neural networks and their applications in the engineering of fabrics, woven fabric engineering, Polona Dobnik Dubrovski (1st ed.). Rijeka: Intechopen. ISBN: 978-953-307-194-7. Wang, L. & Fu, K. (2008). Artificial neural networks. Artificial neural networks, Wiley encyclopedia of computer science and engineering (pp. 181–188). Malden, MA: John Wiley & Sons.

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