Modeling of textile manufacturing processes using intelligent techniques: a review


As the need for quickly exploring a textile manufacturing process is increasingly costly along with the complexity in the process. The development of manufacturing process modeling has attracted growing attention from the textile industry. More and more researchers shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of this paper is in line with the procedure of textile processes from yarn to fabrics, and then to garments. The analysis and discussion of the previous studies are conducted on different applications in different processes. The factors and performance properties considered in process modeling are collected in comparison. In terms of inputs’ relative importance, feature selection, modeling techniques, data distribution, and performance estimations, the considerations of the previous studies are analyzed and summarized. It is also concluded the limitations, challenges, and future perspectives in this issue on the basis of the summaries of more than 130 related articles from the point of views of textile engineering and artificial intelligence.

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  1. 1.

    Almetwally, Ahmed A, Idrees HMF, Hebeish AA (2014) Predicting the tensile properties of cotton / spandex core-spun yarns using artificial neural network and linear regression models. J Text Instit 105(11):1221–1229.

    Article  Google Scholar 

  2. 2.

    Azam, Shahriar N, Rezuanul Alam ASM, Roy A, Hossain MJ, Tusar IH, Rahman MA (2020) Optimization of sewing line production rate and cost using taguchi signal-to-noise method. J Prod Syst Manuf Sci 1(1):19–28

    Google Scholar 

  3. 3.

    Azimi B, Tehran MA, Reza M, Mojtahedi M (2013) Prediction of false twist textured yarn properties by artificial neural network methodology. 8(3):97–101.

  4. 4.

    Balci O, Tuǧrul Oǧulata R (2009) Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models. Fibers Polymers 10(3):384–393.

    Article  Google Scholar 

  5. 5.

    Balci O, Noyan Oǧulata S, Sçahin C, Tuǧrul Oǧulata R (2008) An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics. Fibers Polymers 9(5):604–614.

    Article  Google Scholar 

  6. 6.

    Bald O, Noyan Ogulata S, Sahin C, Tuǧrul Oǧulata R (2008) Prediction of CIELab data and wash fastness of nylon 6,6 using artificial neural network and linear regression model. Fibers Polymers 9(2):217–224.

    Article  Google Scholar 

  7. 7.

    Banjar H, Adelson D, Brown F, Chaudhri N (2017) “Intelligent techniques using molecular data analysis in leukaemia: an opportunity for personalized medicine support system.” Edited by Junya Kuroda. Biomed Res Int 2017:3587309.

    Article  Google Scholar 

  8. 8.

    Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inform Proc-Lett Rev 11(10):203–224

    Google Scholar 

  9. 9.

    Beltran R, Wang L, Wang X (2004) Predicting worsted spinning performance with an artificial neural network model. Text Res J 74(9):757–763

    Article  Google Scholar 

  10. 10.

    Beltran R, Wang L, Wang X (2006) Mill specific prediction of worsted yarn performance mill specific prediction of worsted yarn. J Text Instit 97(1):11–16.

    Article  Google Scholar 

  11. 11.

    Beşdok E (2004) A new method for impulsive noise suppression from highly distorted images by using anfis. Eng Appl Artif Intell 17(5):519–527.

    Article  Google Scholar 

  12. 12.

    Boullart L 1997. “Network / genetic algorithm approach” 67 (2): 84–92.

  13. 13.

    Breiman L (2001) Random forests. Mach Learn 45:5–32

    MATH  Article  Google Scholar 

  14. 14.

    Chattopadhyay R, and Guha A. 2004. “Performance of neural networks for predicting yarn properties using principal component analysis” 91: 1746–51.

  15. 15.

    Chen T, Li L, Huang X (2005a) Modelling and simulation in materials science and engineering predicting the fibre diameter of melt blown nonwovens : comparison of physical , statistical and artificial neural network models predicting the fibre diameter of melt blown. Model Simul Mater Sci Eng 13(2005):575–584.

    Article  Google Scholar 

  16. 16.

    Chen T, Wang J, Huang X (2005b) Artificial neural network modeling for predicting melt blowing processing. J Appl Polym Sci 99(2006):424–429.

    Article  Google Scholar 

  17. 17.

    Chen T, Li L, Koehl L, Vroman P, Zeng X, Zeng X (2006) A soft computing approach to model the structure – property relations of nonwoven fabrics. J Appl Polym Sci 103(2007):442–450.

    Article  Google Scholar 

  18. 18.

    Chen T, Zhang C, Li L, Chen X, Chen T, Zhang C, Li L, Chen X (2008) Simulating the drawing of spunbonding nonwoven process using an artificial neural network technique. J Text Instit 99(December 2014):37–41.

    Article  Google Scholar 

  19. 19.

    Cheng L, Adams DL (1995) Yarn strength prediction using neural networks part I: fiber properties and yarn strength relationship. Text Res J 65(9):495–500

    Article  Google Scholar 

  20. 20.

    Cheng KPS, Lam HLI (2003) Evaluating and comparing the physical properties of spliced yarns by regression and neural network techniques. Text Res J 73(2):161–164

    Article  Google Scholar 

  21. 21.

    Das S, Ghosh A, Majumdar A, Banerjee D (2013) Yarn engineering using hybrid artificial neural network-genetic algorithm model. Fibers Polymers 14(7):1220–1226.

    Article  Google Scholar 

  22. 22.

    Dayik M (2009) Prediction of yarn properties using evaluation programing. Text Res J 79(11):963–972.

    Article  Google Scholar 

  23. 23.

    Dayik M, and Colak O. 2004. “Velocity control of weft insertion on air jet looms by fuzzy logic” 12 (3): 29–33.

  24. 24.

    Debnath S, Madhusoothanan M (2008) Modeling of compression properties of needle-punched nonwoven fabrics using artificial neural network. Indian JFibreText Res 33(December):392–399

    Google Scholar 

  25. 25.

    Debnath S, Madhusoothanan M, Srinivasamoorthy VR (2000a) Prediction of air permeability of needle-punched nonwoven fabrics using artificial neural network and empirical models & V R Srinivasamoorth L. Indian J FibreText Res 25(December):251–255

    Google Scholar 

  26. 26.

    Debnath, S, M Madhusoothanan, and V R Srinivasmoorthl. 2000b. “Modelling of tensile properties of needle-punched nonwovens using artificial neural networks” 25 (March).

  27. 27.

    Demiryurek O, Koc E (2009) Predicting the unevenness of polyester / viscose blended open-end rotor spun yarns using artificial neural network and statistical models. Fibers Polymers 10(2):237–245.

    Article  Google Scholar 

  28. 28.

    Demiryürek O, Koç E (2009) The mechanism and / or prediction of the breaking elongation of polyester / viscose blended open-end rotor spun yarns. Fibers Polymers 10(5):694–702.

    Article  Google Scholar 

  29. 29.

    Desai JV, Kane CD, Bandyopadhayay B (2004) Neural networks : an alternative solution for statistically based parameter prediction. Text Res J 74(3):227–230

    Article  Google Scholar 

  30. 30.

    Dey, Raj P, Mahamud S, Haque I, Chowdhury AMS, Das JR (2020) Six Sigma DMAIC approach with uncertainty quantification and propagation in garments industry. J Prod Syst Manuf Sci 2(1):70–83

    Google Scholar 

  31. 31.

    Doran, Enver Can, and Cenk Sahin. 2019. “The prediction of quality characteristics of cotton / elastane core yarn using artificial neural networks and support vector machines.”

  32. 32.

    Dorrity, J L, G Vachtsevanos, G Daves, S Rim, and A Kumar. 1994. “Advanced application of statistical and fuzzy control to textile processes.” National Textile Center Annual Report 1–9.

  33. 33.

    Etters JN (1993) Indigo dyeing of cotton denim yarn: correlating theory with practice. J Soc Dye Colour 109(7–8):251–255.

    Article  Google Scholar 

  34. 34.

    Etters JN (1995) Advances in indigo dyeing: implications for the dyer, apparel manufacturer and environment. Text Chem Color 27(2):17–22.

    Article  Google Scholar 

  35. 35.

    Fan J, Hunter L (1998) A worsted fabric expert system: part II: an artificial neural network model for predicting the properties of worsted fabrics. Text Res J 68(10):763–771

    Article  Google Scholar 

  36. 36.

    Farooq A, Cherif C (2008) Use of artificial neural networks for determining the leveling action point at the auto-leveling draw frame. Text Res J 78(6):502–509.

    Article  Google Scholar 

  37. 37.

    Farooq, Assad, and Chokri Cherif. 2012. “Development of prediction system using artificial neural networks for the optimization of spinning process” 13 (2): 253–57.

  38. 38.

    Fattahi S, Taheri SM, Ravandi HAH (2012) Cotton yarn engineering via fuzzy least squares regression. Fibers Polymers 13(3):390–396.

    Article  Google Scholar 

  39. 39.

    Feki, Imed, Faouzi Msahli, Xianyi Zeng, and Ludovic Koehl. 2016. “modeling fabric hand of a textile process using a multilayer perceptron pruning algorithm.” In Proceedings of the 12th International FLINS Conference, 1015–21. World Scientific.

  40. 40.

    Ferreira, Candida. 2001. “Gene expression programming: a new adaptive algorithm for solving problems.” ArXiv Preprint Cs/0102027.

  41. 41.

    Ferreira, Cândida. 2006. Gene expression programming: mathematical modeling by an artificial intelligence. Vol. 21. Springer.

  42. 42.

    Furferi, Rocco, and Maurizio Gelli. 2010. “Yarn strength prediction : a practical model based on artificial neural networks” 2010.

  43. 43.

    Furferi, Rocco, Lapo Governi, and Yary Volpe. 2012. “Modelling and simulation of an innovative fabric coating process using artificial neural networks.”

  44. 44.

    Ghanmi H, Ghith A, Benameur T (2015) Ring yarn quality prediction using hybrid artificial neural network. Int J Cloth Sci Technol 27(6):940–956

    Article  Google Scholar 

  45. 45.

    Ghanmi, Hanen, Adel Ghith, and Tarek Benameur. 2019. “Prediction of rotor-spun yarn quality using hybrid artificial neural network-fuzzy expert system model” 44 (March): 31–38.

  46. 46.

    Gharehaghaji AA, Shanbeh M, Palhang M (2007) Analysis of two modeling methodologies for predicting the tensile properties of cotton-covered nylon core yarns. Text Res J 77(8):565–571.

    Article  Google Scholar 

  47. 47.

    Ghorbani V, Vadood M, and Johari MS. 2016. “Prediction of polyester / cotton blended rotor-spun yarns hairiness based on the machine parameters” 41 (March): 19–25.

  48. 48.

    Ghosh A. 2014. “Forecasting of cotton yarn properties using intelligent machines forecasting of cotton yarn properties using intelligent machines,” no. May.

  49. 49.

    Ghosh A, Chatterjee P (2010) Prediction of cotton yarn properties using support vector machine. Fibers Polymers 11(1):84–88.

    Article  Google Scholar 

  50. 50.

    Gong RH, Chen Y (1999) Predicting the performance of fabrics in garment manufacturing with artificial neural networks. Text Res J 69(7):477–482

    Article  Google Scholar 

  51. 51.

    Grosberg P, and Iype C. 1999. Yarn production: theoretical aspects. Textile Institute.

  52. 52.

    Guo ZX, Wong WK, Leung SYS, Li M (2011) Applications of artificial intelligence in the apparel industry: a review. Text Res J 81(18):1871–1892.

    Article  Google Scholar 

  53. 53.

    Haghighat E, Johari MS, Etrati SM, Tehran MA (2012a) Study of the hairiness of polyester-viscose blended yarns. Part III - Predicting yarn hairiness using an artificial neural network. Fib Text Eastern Eur 20(1(90)):33–38

    Google Scholar 

  54. 54.

    Haghighat E, Johari MS, Etrati SM, Tehran MA (2012b) Study of the hairiness of polyester-viscose blended yarns. Part IV - predicting yarn hairiness using fuzzy logic. Fib Text Eastern Eur 20(3(92)):39–42

    Google Scholar 

  55. 55.

    Haghighat E, Etrati SM, Najar SS (2013) Modeling of needle penetration force in denim fabric. Int J Cloth Sci Technol 25(5):361–379.

    Article  Google Scholar 

  56. 56.

    Haghighat E, Najar S, Etrati SM (2014) The prediction of needle penetration force in woven denim fabrics using soft computing models. J Eng Fibers Fabrics 9(4):45–55

    Google Scholar 

  57. 57.

    He Z, Li M, Zuo D, Yi C (2018) The effect of denim color fading ozonation on yarns. Ozone Sci Eng 40(5):377–384.

    Article  Google Scholar 

  58. 58.

    He Z, Li M, Zuo D, Xu J, Yi C (2019a) Effects of color fading ozonation on the color yield of reactive-dyed cotton. Dyes Pigments 164:417–427.

    Article  Google Scholar 

  59. 59.

    He ZL, Li M, Zuo DY, Yi CH (2019b) Color fading of reactive-dyed cotton using UV-assisted ozonation. Ozone Sci Eng 41(1):60–68.

    Article  Google Scholar 

  60. 60.

    He Z, Tran KP, Zeng X, Xu J, Yi C (2020) Modeling color fading ozonation of reactive-dyed cotton using the extreme learning machine , support vector regression and random forest. Text Res J 90(7–8):896–908.

    Article  Google Scholar 

  61. 61.

    He Z, Tran KP, Thomassey S, Zeng X, Xu J, Yi C (2021a) Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning. ArXiv, no. March.

  62. 62.

    He Z, Tran KP, Thomassey S, Zeng X, Yi C (2021b) A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. Comput Ind 125:103373

    Article  Google Scholar 

  63. 63.

    Hossain I, Altab H, Choudhury IA, Bakar A, Uddin H, Shahid A (2014) Color fastness modeling of viscose dyed fabrics using fuzzy expert system. Proc Eng 00(000):1–6

    Google Scholar 

  64. 64.

    Hossain I, Hossain A, Choudhury IA (2015) Color strength modeling of viscose/lycra blended fabrics using a fuzzy logic approach. J Eng Fibers Fabrics 10(1):158–168.

    Article  Google Scholar 

  65. 65.

    Hossain, Ismail, Imtiaz Ahmed Choudhury, Azuddin Bin Mamat, Abdus Shahid, Ayub Nabi Khan, and Altab Hossain. 2016a. “Predicting the mechanical properties of viscose / lycra knitted fabrics using fuzzy technique” 2016.

  66. 66.

    Hossain I, Hossain A, Choudhury IA, Al Mamun A (2016b) Fuzzy knowledge based expert system for prediction of color strength of cotton knitted fabrics. J Eng Fibers Fabrics 11(3):33–44.

    Article  Google Scholar 

  67. 67.

    Hossain I, Choudhury IA, Mamat AB (2017) Predicting the colour properties of viscose knitted fabrics using soft computing approaches. J Text Instit 5000(January):1689–1699.

    Article  Google Scholar 

  68. 68.

    Hui C-l, Ng S-f (2005) A new approach for prediction of sewing performance of fabrics in apparel manufacturing using artificial neural networks. J Text Instit 96(6):401–405.

    Article  Google Scholar 

  69. 69.

    Hui CL, Ng SF (2009) Predicting seam performance of commercial woven fabrics using multiple logarithm regression and artificial neural networks( ANNs ). Text Res J 79(18):1649–1657.

    Article  Google Scholar 

  70. 70.

    Hui PCL, Chan KCC, Yeung KW, Ng FSF (2007) Application of artificial neural networks to the prediction of sewing performance of fabrics. Int J Cloth Sci Technol 19(5):291–318.

    Article  Google Scholar 

  71. 71.

    Hung ON, Song LJ, Chan CK, Kan CW, Yuen CWM (2011) Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric. Fibers Polymers 12(8):1069–1076.

    Article  Google Scholar 

  72. 72.

    Hung ON, Song LJ, Chan CK, Kan CW, Yuen CWM, and Kong H. 2012. Laser-engraved color properties on cotton-spandex fabric predicted by artificial neural network, no. June: 57–64.

  73. 73.

    Hung ON, Chan CK, Kan CW, Yuen CWM, Song LJ (2014) Artificial neural network approach for predicting colour properties of laser-treated denim fabrics. Fibers Polymers 15(6):1330–1336.

    Article  Google Scholar 

  74. 74.

    Hussain T, Jabbar A, Ahmed S (2014) Comparison of regression and adaptive neuro-fuzzy models for predicting the compressed air consumption in air-jet weaving. Fibers Polymers 15(2):390–395.

    Article  Google Scholar 

  75. 75.

    Jamshaid H, Hussain T, Malik ZA (2013) Comparison of regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics comparison of regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics. Fibers Polymers 14(7):1203–1207.

  76. 76.

    Jang (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Transact Syst, Man, Cybernet 23(3):665–685.

    Article  Google Scholar 

  77. 77.

    Jang JSR, Sun C-T (1993) Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans Neural Netw 4(1):156–159.

    Article  Google Scholar 

  78. 78.

    Jaouachi B, Louati H, Hellali H (2010) Predicting residual bagging bend height of knitted fabric using. Autex Res J 10(December):110–115

    Google Scholar 

  79. 79.

    Jeffrey, C-F, Kun-iuan Hsiao, and Yi-shiuan Wu. 2015. “Using fuzzy theory to predict the properties spinning system,”Textile Res J 74(3) 231–235.

  80. 80.

    Jelil A, Radhia XZ, Koehl L, Perwuelz A (2013a) Modeling plasma surface modification of textile fabrics using artificial neural networks. Eng Appl Artif Intell 26(8):1854–1864.

    Article  Google Scholar 

  81. 81.

    Jelil RA, Zeng X, Koehl L, Perwuelz A (2013b) Modeling plasma fabric surface treatment using fuzzy logic and artificial neural networks. J Inform Comp Sci 8(2):141–152

    Google Scholar 

  82. 82.

    Kan CW, Song LJ (2016) An artificial neural network model for prediction of colour properties of knitted fabrics induced by laser engraving. Neural Process Lett 44(3):639–650.

    Article  Google Scholar 

  83. 83.

    Kan CW, Wong WY, Song LJ, Law MC (2013) Prediction of color properties of cellulase-treated 100% cotton denim fabric. J Text 2013:1–10.

    Article  Google Scholar 

  84. 84.

    Khan Z, Lim AEK, Wang L, Wang X, Beltran R (2009) An artificial neural network-based hairiness prediction model for worsted wool yarns. Text Res J 79(8):714–720.

    Article  Google Scholar 

  85. 85.

    Kim S, Vachtsevanos GJ (2000) Intelligent approach to integration and control of textile processes. Inf Sci 123(3):181–199.

    Article  Google Scholar 

  86. 86.

    Kuo C-f J (2006) Optimization of the Processing conditions and prediction of the quality for dyeing nylon and lycra blended fabrics. Fibers Polymers 7(4):344–351

    Article  Google Scholar 

  87. 87.

    Kuo C-f J, Hsiao K-i, Yi-shiuan W (2004) Using neural network theory to predict the properties of melt spun fibers. Text Res J 74(9):840–843

    Article  Google Scholar 

  88. 88.

    Lawrence CA (2003). Fundamentals of spun yarn technology. Crc Press.

  89. 89.

    Li JW, Zhang WJ, Yang GS, Tu SD, Chen XB (2008) Thermal-error modeling for complex physical systems: the-state-of-arts review. Int J Adv Manuf Technol 42(1):168–179.

    Article  Google Scholar 

  90. 90.

    Li M, He Z, Jie X (2020) A comparative study of ozonation on aqueous reactive dyes and reactive-dyed cotton. Color Technol 2021:1–13.

    Article  Google Scholar 

  91. 91.

    Liaw A, Wiener M (2002a) Classification and regression by randomforest. R News 2(December):18–22.

    Article  Google Scholar 

  92. 92.

    Majumdar A (2010) Modeling of cotton yarn hairiness using adaptive neuro-fuzzy inference system. Indian J FibreText Res 35(June):121–127

    Google Scholar 

  93. 93.

    Majumdar PK, Majumdar A (2004) Predicting the breaking elongation of ring spun cotton yarns using mathematic. Text Res J 74(7):652–655

    Article  Google Scholar 

  94. 94.

    Majumdar A, Majumdar PK, Sarkar B (2005a) Application of an adaptive neuro-fuzzy system for the prediction of cotton yarn strength from HVI fibre properties. J Text Inst 96(1):55–60.

    Article  Google Scholar 

  95. 95.

    Majumdar A, Majumdar PK, Sarkar B (2005b) Application of linear regression, artificial neural network and neuro-fuzzy algorithms to predict the breaking elongation of rotor-spun yarns. Indian JFibreText Res 30(1):19–25

    Google Scholar 

  96. 96.

    Majumdar A, Ciocoiu M, Blaga M (2008a) Modelling of ring yarn unevenness by soft computing approach. Fibers Polymers 9(2):210–216

    Article  Google Scholar 

  97. 97.

    Majumdar A, Ph D, Ghosh A, Ph D (2008b) Yarn strength modelling using fuzzy expert system. J Eng Fibers Fabrics 3(4):61–68

    Google Scholar 

  98. 98.

    Malik ZA, Malik MH (2010) Predicting strength transfer efficiency of warp and weft yarns in woven fabrics using adaptive neuro-fuzzy inference system. Indian JFibreText Res 35(December):310–316

    Google Scholar 

  99. 99.

    Malik SA, Farooq A, Gereke T, Cherif C, Performance H, Technology M (2016) Prediction of blended yarn evenness and tensile properties by using artificial. AUTEX Res J 16(2):8–15.

    Article  Google Scholar 

  100. 100.

    Malik SA, Kocaman RT, Kaynak HK, Gereke T, Aibibu D, Babaarslan O, Cherif C (2017) Analysis and prediction of air permeability of woven barrier fabrics with respect to material, fabric construction and process parameters. Fibers Polymers 18(10):2005–2017.

    Article  Google Scholar 

  101. 101.

    McNeill, F Martin, and Ellen Thro. 2014. Fuzzy logic: a practical approach. Academic Press.

  102. 102.

    Midha VK, Kothari VK, Chattopadhyay R, Mukhopadhyay A (2010) A neural network model for prediction of strength loss in threads during high speed industrial sewing. Fibers Polymers 11(4):661–668.

    Article  Google Scholar 

  103. 103.

    Moghassem A, Fallahpour A (2011) Processing parameters optimization of draw frame for rotor spun yarn strength using gene expression programming ( GEP ). Fibers Polymers 12(7):970–975.

    Article  Google Scholar 

  104. 104.

    Moghassem A, Fallahpour A, Shanbeh M (2012) An intelligent model to predict breaking strength of rotor spun yarns using gene expression programming. J Eng Fibers Fabrics 7(2):155892501200700.

    Article  Google Scholar 

  105. 105.

    Mozafary V, Payvandy P (2014) Application of data mining technique in predicting worsted spun yarn quality. J Text Inst 105(1):100–108.

    Article  Google Scholar 

  106. 106.

    Murrells CM, Tao XM, Xu BG, Cheng KPS (2009) An artificial neural network model for the prediction of spirality of fully relaxed single jersey fabrics. Text Res J 79(3):227–234.

    Article  Google Scholar 

  107. 107.

    Mwasiagi JI, Huang XB, Wang XH (2008a) Performance of neural network algorithms during the prediction of yarn breaking elongation.Pdf. Fibers Polymers 9(1):80–86

    Article  Google Scholar 

  108. 108.

    Mwasiagi JI, Wang XH, Huang XB (2008b) Use of input selection techniques to improve the performance of an artificial neural network during the prediction of yarn quality properties. J Appl Polym Sci 108:320–327.

    Article  Google Scholar 

  109. 109.

    Mwasiagi JI, Huang X, Wang X (2012) The use of hybrid algorithms to improve the performance of yarn parameters prediction models. Fibers Polymers 13(9):1201–1208.

    Article  Google Scholar 

  110. 110.

    Nasiri M, Berlik S (2009) Modeling of polyester dyeing using an evolutionary fuzzy system. In IFSA-EUSFLAT 2009:1246–1251

    Google Scholar 

  111. 111.

    Nasiri M, Shanbeh M, Tavanai H (2005) “Comparison of statistical regression, fuzzy regression and artificial neural network modeling methodologies in polyester dyeing.” Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 205 and International Conference on Intelligent Agents. Web Technol Internet 1:505–510.

    Article  Google Scholar 

  112. 112.

    Nurwaha D, Wang XH (2008) Comparison of the new methodologies for predicting the CSP strength of rotor yarn. Fibers Polymers 9(6):782–784.

    Article  Google Scholar 

  113. 113.

    Nurwaha D, Wang XH (2010) Prediction of rotor spun yarn strength from cotton fiber properties using adaptive neuro-fuzzy inference system method. Fibers Polymers 11(1):97–100.

    Article  Google Scholar 

  114. 114.

    Nurwaha D, Wang X (2011) Prediction of rotor spun yarn strength using support vector machines method. Fibers Polymers 12(4):546–549.

    Article  Google Scholar 

  115. 115.

    Nurwaha D, Wang XH (2012) Using intelligent control systems to predict textile yarn quality. Fibres Text Eastern Eur 1(90):23–27

    Google Scholar 

  116. 116.

    Onal L, Zeydan M, Korkmaz M, Meeran S (2009) Predicting the seam strength of notched webbings for parachute assemblies using the Taguchi ’ s design of experiment and artificial neural networks abstract. Text Res J 79(5):468–478.

    Article  Google Scholar 

  117. 117.

    Ozel Y, Kayar M (2008) An application of neural network solution in the apparel industry for cutting time forecasting. In: 8th WSEAS International Conference on Simulation. Modelling and Optimization (SMO ‘08), Santander, pp 23–25

    Google Scholar 

  118. 118.

    Özkan İ, Kuvvetli Y, Baykal D, Erol R (2014) Comparison of the neural network model and linear regression model for predicting the intermingled yarn breaking strength and elongation. J Text Instit 105(11):37–41.

    Article  Google Scholar 

  119. 119.

    Patterson, Dan W. 1998. Artificial neural networks: theory and applications. Prentice Hall PTR.

  120. 120.

    Pavlinic DZ, Gersak J, Demsar J, Bratko I (2006) Predicting seam appearance quality. Text Res J 76(3):235–242.

    Article  Google Scholar 

  121. 121.

    Pei Z, and Chongwen Y. 2011. “Prediction of the vortex yarn tenacity from some process and nozzle parameters based on numerical simulation and artificial neural network.”

  122. 122.

    Pynckels F, Kiekens P, Sette S, Langenhove L, Impe K (1995) Use of neural nets for determining the spinnability of fibres. J Text Instit 86(3):425–440.

    Article  Google Scholar 

  123. 123.

    Pynckels F, Kiekens P, Sette S, Van Langenhove L, Impe K, Pynckels F, Kiekenst P, Settet S, Van Langenhovet L, Tmpe K (1997) The use of neural nets to simulate the spinning process. J Text Instit 88(4):440–448.

    Article  Google Scholar 

  124. 124.

    Rajamanickam R, Hansen S, Jayaraman S (1997) Analysis of the modeling methodologies for predicting the strength of air-jet spun yarns. Text Res J 67(1):39–44

    Article  Google Scholar 

  125. 125.

    Ramesh MC, Rajamanickam R, Jayaraman S (1995) The prediction of yarn tensile properties by using artificial neural networks. J Text Inst 86(3):459–469.

    Article  Google Scholar 

  126. 126.

    Rawal A, Majumdar A, Anand S, Shah T (2009) Predicting the properties of needlepunched nonwovens using artificial neural network. J ApplPolymer Sci 112(2009):3575–3581.

    Article  Google Scholar 

  127. 127.

    Rumelhart DE, Hinton GE, and Williams RJ. 1985. “Learning internal representations by error propagation.” California Univ San Diego La Jolla Inst for Cognitive Science.

  128. 128.

    Schacher L, Adolphe D, Schacher L, and Adolphe. 2011. “Predicting compression and surfaces properties of knits using fuzzy logic and neural networks techniques.”

  129. 129.

    Sema E, Çoban S, Ünal PG (2011) “Prediction of various functional finishing treatments using artificial neural networks”Fibers and. Polymers 12(3):414–421.

    Article  Google Scholar 

  130. 130.

    Sentilkumar M, Selvakumar N (2006) Achieving expected depth of shade in reactive dye application using artificial neural network technique. Dyes Pigments 68(2–3):89–94.

    Article  Google Scholar 

  131. 131.

    Sette S, Van Langenhove L (2003) An overview of soft computing in textiles. J Text Inst 94(1–2):103–109.

    Article  Google Scholar 

  132. 132.

    Sette S, Boullart L, Van Langenhove L (2000) Building a rule set for the fiber-to-yarn production process by means of soft computing techniques. Text Res J 70(5):375–386

    Article  Google Scholar 

  133. 133.

    Shahid MA, Hossain MI (2015) Modeling the spirality of cotton knit fabric using fuzzy expert system. Turk J Fuzzy Syst 6(2):56–67

    Google Scholar 

  134. 134.

    Smola AJ, Scholkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222.

    MathSciNet  Article  Google Scholar 

  135. 135.

    Stylios G, Parson-Moore R (1993) Seam pucker prediction using neural computing. Int J Cloth Sci Technol 5(5):24–27

    Article  Google Scholar 

  136. 136.

    Stylios G, Sotomi JO (1995) A neuro-fuzzy control system for intelligent overlock sewing machines. Int J Cloth Sci Technol 7(2):49–55

    Article  Google Scholar 

  137. 137.

    Stylios G, Sotomi JO (1996) Thinking sewing machines for intelligent garment manufacture. 8(1):44–55

  138. 138.

    Subramanian SN, Venkatachalam A, Subramaniam V (2007) Prediction and optimization of yarn properties using genetic algorithm / artificial neural network. Indian JFibreText Res 32(December):409–413

    Google Scholar 

  139. 139.

    Tavanai H, Taheri SM, Nasiri M (2005) Modelling of colour yield in polyethylene terephthalate dyeing with statistical and fuzzy regression. Iran Polym J 14(11):954–967

    Google Scholar 

  140. 140.

    Tehran, M.A., and M. Maleki. 2011. “Artificial neural network prosperities in textile applications,” 35–64.

  141. 141.

    Thevenet L, Dupont D, Jolly-Desodt AM (2003) Modeling color change after spinning process using feedforward neural networks. Color Res Appl 28(1):50–58.

    Article  Google Scholar 

  142. 142.

    Turhan Y, Toprakci O (2013) Comparison of high-volume instrument and advanced fiber information systems based on prediction performance of yarn properties using a radial basis function neural network. Text Res J 83(2):130–147.

    Article  Google Scholar 

  143. 143.

    Uddin, Faheem Uddin ED1 - Faheem. 2019. “Introductory chapter: textile manufacturing processes.” In , Ch. 1. Rijeka: IntechOpen.

  144. 144.

    Unal PG, Arikan C, Ozdil N, Taskin C (2010a) The effect of fiber properties on the characteristics of spliced yarns : part II : prediction of retained spliced diameter. Text Res J 80(17):1751–1758.

    Article  Google Scholar 

  145. 145.

    Unal PG, Ozdil N, Taskin C (2010b) The effect of fiber properties on the characteristics of spliced yarns part I : prediction of spliced yarns tensile properties. Text Res J 80(5):429–438.

    Article  Google Scholar 

  146. 146.

    Üreyen ME, Gürkan P (2008a) Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties. Fibers Polymers 9(1):87–91.

    Article  Google Scholar 

  147. 147.

    Üreyen ME, Gürkan P (2008b) Comparison of Artificial neural network and linear regression models for prediction of ring spun yarn properties. II. Prediction of yarn hairiness and unevenness. Fibers Polymers 9(1):92–96.

    Article  Google Scholar 

  148. 148.

    Vadood M (2014) Predicting the color index of acrylic fiber using fuzzy-genetic approach. J Text Inst 105(7):779–788.

    Article  Google Scholar 

  149. 149.

    Vapnik,V. (2013). The nature of statistical learning theory. Springer science & business media.

  150. 150.

    Veit D (2012). “Fuzzy logic and its application to textile technology.” Simulation in Textile Technology: Theory and Applications, 112–41.

  151. 151.

    Wu ZF, Li J, Cai MY, LinY, and Zhang WJ. 2016. “On membership of black-box or white-box of artificial neural network models.” In 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), 1400–1404.

  152. 152.

    Xu L (1998) RBF nets, mixture experts, and Bayesian Ying–Yang learning. Neurocomputing 19(1–3):223–257

    MATH  Article  Google Scholar 

  153. 153.

    Xu Y, Thomassey S, Zeng X (2018) AI for apparel manufacturing in Big Data era: a focus on cutting and sewing. In: Artificial Intelligence for Fashion Industry in the Big Data Era. Springer, pp 125–151

  154. 154.

    Xu J, He Z, Li S, Ke W (2020) Production cost optimization of enzyme washing for indigo dyed cotton denim by combining kriging surrogate with differential evolution algorithm. Text Res J 90(15–16):1860–1871.

    Article  Google Scholar 

  155. 155.

    Yang J-g, Lu Z-j, Li B-z (2012) Quality prediction in complex industrial process with support vector machine and genetic algorithm optimization : a case study. Appl Mech Mater 232:603–608.

    Article  Google Scholar 

  156. 156.

    Yao G, Guo J, Zhou Y (2005) Predicting the warp breakage rate in weaving by neural network techniques. Text Res J 75(3):274–278

    Article  Google Scholar 

  157. 157.

    Yin X, Weidong Y (2007) The virtual manufacturing model of the worsted yarn based on artificial neural networks and grey theory. Appl Math Comput 185:322–332.

    Article  MATH  Google Scholar 

  158. 158.

    Yu Z, Sun J, Gupta MM, Moody W, Laverty WH, Zhang W (2017) Developing a mapping from affective words to design parameters for affective design of apparel products. Text Res J 87(18):2224–2232.

    Article  Google Scholar 

  159. 159.

    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Article  Google Scholar 

  160. 160.

    Zanaganeh M, Jamshid Mousavi S, Shahidi AFE (2009) A hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters. Eng Appl Artif Intell 22:1194–1202.

    Article  Google Scholar 

  161. 161.

    Zeng YC, Wang KF, Yu CW (2004) Predicting the tensile properties of air-jet spun yarns. Text Res J 74(8):689–694

    Article  Google Scholar 

  162. 162.

    Zeng X, Li Y, Ruan D, and Koehl L. (2007). Computational textile. Vol. 55. Springer Science & Business Media.

  163. 163.

    Zhang Y, Li S, Qian X, Wang J (2015) A fuzzy neural network based on non-Euclidean distance clustering for quality index model in slashing process. Math Probl Eng.

  164. 164.

    Zhao B (2012) Prediction of cotton ring yarn evenness properties from process parameters by using artificial neural network and multiple regression analysis. Adv Mater Res 366:103–107.

    Article  Google Scholar 

  165. 165.

    Zhao Y, Song J, Alireza M, Gupta MM, Lin Y, Wang C, Zhang WJ (2017) Mining affective words to capture customer’s affective response to apparel products. Text Res J 88(12):1426–1436.

    Article  Google Scholar 

  166. 166.

    Zheng L, Bing D, Xing J, Gao S (2010) Bio-degumming optimization parameters of kenaf based on a neural network model. J Text Inst 101(12):1075–1079.

    Article  Google Scholar 

  167. 167.

    Zhu R, Ethridge MD (1996) The prediction of cotton yarn irregularity based on the ‘ AFIS ’ measurement. J Text Instit 87(3):509–512.

    Article  Google Scholar 

  168. 168.

    Zhu R, Ethridge MD (1997) Predicting hairiness for ring and rotor spun yarns and analyzing the impact of fiber properties. Text Res J 67(9):694–698

    Article  Google Scholar 

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This research was supported by the funds from the National Key R&D Program of China (Project No: 2019YFB1706300), and Scientific Research Project of Hubei Provincial Department of Education, China (Project No: Q20191707). The first author would like to express his gratitude to China Scholarship Council for supporting this study (CSC, Project No. 201708420166).

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He, Z., Xu, J., Tran, K.P. et al. Modeling of textile manufacturing processes using intelligent techniques: a review. Int J Adv Manuf Technol 116, 39–67 (2021).

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  • Artificial intelligence
  • Manufacturing
  • Textile
  • Model
  • Process
  • Review