Review of Artificial Intelligence Applications in Garment Manufacturing

  • Radhia Abd Jelil
Part of the Springer Series in Fashion Business book series (SSFB)


Nowadays, apparel manufacturing enterprises are confronted with ever-increasing global competition and unpredictable demand fluctuations. These pressures compel manufacturers to continuously improve the performance of their production process in order to deliver the finished product within the most approximate period of time and the lowest production cost. However, consistent and optimal solutions are difficult to obtain under a fuzzy and dynamic manufacturing environment. Therefore, in response to the need for new approaches, a large (and continually increasing) number of efforts have sought to investigate and exploit the use of AI techniques in a variety of industrial applications. This chapter provides a systematic review of contemporary research articles related to the application of AI techniques in garment manufacturing. The research issues are classified into three categories, including production planning, control, and scheduling; garment quality control and inspection; and garment quality evaluation. The challenges facing adoption of AI technologies in garment industry are discussed.


Artificial intelligence Garment manufacturing Decision making Survey 


  1. Abeysooriya RP, Fernando TGI (2012a) Canonical genetic algorithm to optimize cut order plan solutions in apparel manufacturing. J Emerg Trends Comput Inf Sci 3(2):150–154Google Scholar
  2. Abeysooriya RP, Fernando TGI (2012b) Hybrid approach to optimize cut order plan solutions in apparel manufacturing. Int J Inf Commun Technol Res 2(4):348–353Google Scholar
  3. Adeli H (2003) Expert systems in construction and structural engineering. CRC Press. ISBN 9780203401101Google Scholar
  4. Al-Rashidi K, Alazmi R, Alazmi M (2015) Artificial neural network estimation of thermal insulation value of children’s school wear in Kuwait classroom. Adv Artif Neural Syst Article ID 421215, 9 pp.
  5. Bahlmann C, Heidemann G, Ritter H (1999) Artificial neural networks for automated quality control of textile seams. Patt Recogn 32(6):1049–1060 CrossRefGoogle Scholar
  6. Barrett GR, Clapp TG, Titus KJ (1996) An on-line fabric classification technique using a wavelet-based neural network approach. Text Res J 66(8):521–528CrossRefGoogle Scholar
  7. Bouziri A, M’hallah R (2007) A hybrid genetic algorithm for the cut order planning problem. In: New trends in applied artificial intelligence. Lecture notes in computer science, vol 4570, pp 454–463Google Scholar
  8. Brown EC, Sumichrast RT (2005) Evaluating performance advantages of grouping genetic algorithms. Eng Appl Artif Intell 18:1–12CrossRefGoogle Scholar
  9. Carvalho H, Silva LF, Soares F, Guhr F (2010) Adaptive control of an electromagnetically presser-foot for industrial sewing. In: 2010 IEEE 15th conference on emerging technologies and factory automation (ETFA 2010), 13–16 September, Bilbao, SpainGoogle Scholar
  10. Chan CC, Hui CL, Yeung KW, Ng SF (1998) Handling the assembly line balancing problem in the clothing industry using a genetic algorithm. Int J Cloth Sci Technol 10(1):21–37CrossRefGoogle Scholar
  11. Chen R-S, Lu K-Y, Yu S-C (2002) A hybrid genetic algorithm approach on multi-objective of assembly planning problem. Eng Appl Artif Intell 15(5):447–457CrossRefGoogle Scholar
  12. Chen JC, Hsaio MH, Chen CC, Sun CJ (2009) A grouping genetic algorithm for the assembly line balancing problem of sewing lines in garment industry. In: 2009 international conference on machine learning and cybernetics, Hebei, ChinaGoogle Scholar
  13. Chen CJ, Chen C-C, Su L-H, Sun C-J (2012) Assembly line balancing in garment industry. Expert Syst Appl 39(11):10073–10081CrossRefGoogle Scholar
  14. Chen JC, Chen CC, Lin YJ, Lin CJ, Chen TY (2014) Assembly line balancing problem of sewing lines in garment industry. In: Proceedings of the 2014 international conference on industrial engineering and operations management, Bali, Indonesia, 7–9 January 2014, pp 1215–1225Google Scholar
  15. Cooklin G, Hayes SG, McLoughlin J (2006) Introduction to clothing manufacture. Blackwell Publishing Ltd., pp 85–99Google Scholar
  16. Du W, Tang Y, Yung S et al (2017) Robust order scheduling in the fashion industry: a multi-objective optimization approach. CoRR abs/1702.00159Google Scholar
  17. Dumishllari E, Guxho G (2016) Influence of lay plan solution in fabric efficiency and consume in cutting section. AUTEX Res J 16(4):222–227CrossRefGoogle Scholar
  18. Eryuruk SH, Kalaoglu F, Baskak M (2008) Assembly line balancing in a clothing company. Fibres Text East Eur 16(1):93–98Google Scholar
  19. Falkenauer E (1993) The grouping genetic algorithm: widening the scope of the Gas. JORBEL Belg. J Oper Res Stat Comput Sci 33:79–102Google Scholar
  20. Fan J, Newton E, Au R, Chan SCF (2001) Predicting garment drape with a fuzzy-neural network. Text Res J 71(7):605–608CrossRefGoogle Scholar
  21. Fung EHK, Wong YK, Zhang XZ, Cheng L, Yuen CWM, Wong WK (2011) Fuzzy logic control of a novel robotic hanger for garment inspection: modeling, simulation and experimental implementation. Expert Syst Appl 38:9924–9938CrossRefGoogle Scholar
  22. Gong RH, Chen Y (1999) Predicting the performance of fabrics in garment manufacturing with artificial neural networks. Text Res J 69(7):477–482CrossRefGoogle Scholar
  23. Guhr F, Silva L, Soares F and Carvalho H (2004) Fuzzy logic based control strategies for an electromagnetic actuated sewing machine presser foot. In: Proceedings of the 2004 IEEE international conference on industrial technology, pp 985–990Google Scholar
  24. Guo ZX, Wong WK, Leung SYS, Fan JT, Chan SF (2006) Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multiproduct assembly environment: a case study based on the apparel industry. Comput Ind Eng 50:202–219CrossRefGoogle Scholar
  25. Guo ZX, Wong WK, Leung SYS, Li M (2011) Application of artificial Intelligence in the apparel industry: a review. Text Res J 5(12):1871–1889CrossRefGoogle Scholar
  26. Guo Z, Wong WK, Leung S (2013) A hybrid intelligent model for order allocation planning in make-to-order manufacturing. Appl Soft Comput 13(3):1376–1390CrossRefGoogle Scholar
  27. Hopper E, Turton BCH (2001) An empirical investigation of meta-heuristic and heuristic algorithms for a 2D packing problem. Eur J Oper Res 128(1):34–57CrossRefGoogle Scholar
  28. Huang G (2013) Application of optimized particle swarm algorithm on apparel intelligent layout. Appl Mech Mater 380–384:1668–1672CrossRefGoogle Scholar
  29. Hui CL, Ng SF (2005) A new approach for prediction of sewing performance of fabrics in apparel manufacturing using artificial neural networks. J Text Instit 96(6):401–405CrossRefGoogle Scholar
  30. Hui CL, Ng SF (2009) Predicting seam performance of commercial woven fabrics using multiple logarithm regression and artificial neural networks. Text Res J 79(18):1649–1657CrossRefGoogle Scholar
  31. Islam MM, Mohiuddin HM, Mehidi SH, Sakib N (2014) An optimal layout design in an apparel industry by appropriate line balancing: a case study. Glob J Res Eng G Ind Eng 14(5):35–43Google Scholar
  32. Kalkanci M, Kurumer G, Ozturk H, Sinecen M, Kayacan O (2017) Artificial neural network system for prediction of dimensional properties of cloth in garment manufacturing: case study on a T-shirt. Fibres Text East Eur 25(4):135–140Google Scholar
  33. Kaulkanci M, Kurumar G (2015) Investigation of dimensional changes during garment production and suggestions for solutions. Fibers Text East Eur 23(3):8–13Google Scholar
  34. Kaur A, Roy K (2016) Prediction of shrinkage and fabric weight (g/m2) of cotton single jersey knitted fabric using artificial neural network and comparison with general linear model. Int J Inf Res Rev 2541–2544Google Scholar
  35. Kim I, Fok S, Fregene K, Lee D, Oh T, Wang D (2004) Neural network-based system identification and controller synthesis for an industrial sewing machine. Int J Control Autom 2:83–91Google Scholar
  36. Koustoumpardis P, Aspragathos N (2003) Fuzzy logic decision mechanism combined with a neuro-controller for fabric tension in robotized sewing process. J Intell Robot Syst 36:65–88CrossRefGoogle Scholar
  37. Koustoumpardis P, Aspragathos N (2007) Neural network force control for robotized handling of fabrics. In: Proceedings of the 2007 international conference on control, automation and systems, Seoul, South Korea. IEEE, pp 2845–2850Google Scholar
  38. Kulkarni AH, Patil SB (2012) Automated garment identification and defect detection model based on texture features and PNN. Accessed 29 Sept 2017
  39. Lin MT (2009) The single-row machine layout problem in apparel manufacturing by hierarchical order-based genetic algorithm. Int J Cloth Sci Tech 20(5):258–270CrossRefGoogle Scholar
  40. Luo X, Hou W, Li Y, Wang Z (2007) A fuzzy neural network model for predicting clothing thermal comfort. Comput Math Appl 53:1840–1846CrossRefGoogle Scholar
  41. M’hallah R, Bouziri A (2016) Heuristics for the combined cut order planning two-dimensional layout problem in the apparel industry. Int Trans Oper Res 23:321–353CrossRefGoogle Scholar
  42. Mahajan PM, Kolhe SR, Patil PM (2009) A review of automatic fabric defect detection techniques. Adv Comput Res 1(2):18–29Google Scholar
  43. Martens J (2004) Two genetic algorithms to solve a layout problem in the fashion industry. Eur J Oper Res 154(1):304–322CrossRefGoogle Scholar
  44. Mok PY, Kwong CK, Wong WK (2007) Optimization of fault-tolerant fabric cutting schedules using genetic algorithms and fuzzy set theory. Eur J Oper Res 177:1876–1893CrossRefGoogle Scholar
  45. Mok PY, Cheung TY, Wong WK et al (2013) Intelligent production planning for complex garment manufacturing. 24(1):133–145CrossRefGoogle Scholar
  46. Mousazadegan F, Ezazshahabi N, Latifi M, Saharkhiz S (2013) Formability analysis of worsted woven fabrics considering fabric direction. Fibers Polym 14(11):1933–1942CrossRefGoogle Scholar
  47. Naik SB, Kallurkar S (2016) A literature review on efficient plant layout design. Int J Ind Eng Res Dev (IJIERD) 7(2):43–51Google Scholar
  48. Nascimento DB, Figueiredo JN, Mayerle SF, Nascimento PR, Casali RM (2010) A state-space solution search method for apparel industry spreading. Int J Prod Econ 128(1):379–392CrossRefGoogle Scholar
  49. Nawaz N, Troynikov O, Watson C (2011) Evaluation of surface characteristics of fabrics suitable for skin layer of firefighters’ protective clothing. Phys Procedia 22:478–486CrossRefGoogle Scholar
  50. Ngan HYT, Pang GKH, Yung NHC (2011) Automated fabric defect detection—a review. Image Vis Comput 29(7):442–458CrossRefGoogle Scholar
  51. 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. Text Res J 79(5):468–478CrossRefGoogle Scholar
  52. Ozel Y, Kayar H (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, Cantabria, Spain, 23–25 September 2008, pp 224–218Google Scholar
  53. Pamuk O (2008) Clothing comfort propeties in textile industry. e-J New World Sci Acad 3(1):69–74Google Scholar
  54. Park CK, Kang TJ (1997) Objective rating of seam pucker using neural networks. Text Res J 67(7):494–502CrossRefGoogle Scholar
  55. Park CK, Kang TJ (1999a) Objective evaluation of seam pucker using artificial intelligence, Part I: Geometric modeling of seam pucker. Text Res J 69(10):735–742CrossRefGoogle Scholar
  56. Park CK, Kang TJ (1999b) Objective evaluation of seam pucker using artificial intelligence, Part II: Method of evaluating seam pucker. Text Res J 69(11):835–845CrossRefGoogle Scholar
  57. Park CK, Kang TJ (1999c) Objective evaluation of seam pucker using artificial intelligence, Part III: Using the objective evaluation method to analyze the effects of sewing parameters on seam pucker. Text Res J 69(12):919–924CrossRefGoogle Scholar
  58. Patrick CLH, Frency SFN, Keith CCC (2000) A study of the roll planning of fabric spreading using genetic algorithms. Int J Cloth Sci Technol 12(1):50–62CrossRefGoogle Scholar
  59. Patrick CLH, Keith CCC, Yeung KW, Frency SFN (2007) Application of artificial neural networks to the prediction of sewing performance of fabrics. Int J Cloth Sci Technol 19(5):291–318CrossRefGoogle Scholar
  60. Pavlinic DZ, Gersak J (2009) Predicting garment appearance quality. Open Text J 2:29–38CrossRefGoogle Scholar
  61. Pavlinic DZ, Gersak J, Demsar J, Bratko I (2006) Predicting seam appearance quality. Text Res J 76(3):235–242CrossRefGoogle Scholar
  62. Rose DM, Shier DR (2007) Cut scheduling in the apparel industry. Comput Oper Res 34(11):3209–3228CrossRefGoogle Scholar
  63. Silva LF, Carvalho H, Soares F (2004) Improving feeding efficiency of a sewing machine by on-line control of the presser-foot. In: Proceedings of the 4th international conference on advanced engineering design—AED’2004 (CD-ROM), Glasgow, Scotland, UK, 5–8 September 2004Google Scholar
  64. Ukponmwan JO, Mukhopadhyay A et al (2000) Sewing thread. Text Inst (Manchester) 30:1–91Google Scholar
  65. Ultutas B, Islier AA (2015) Dynamic facility layout problem in footwear industry. J Manuf Syst 36:55–61CrossRefGoogle Scholar
  66. Unal C, Tunali S, Guner M (2009) Evaluation of alternative line configurations in apparel industry using simulation. Text Res J 79(10):908–916CrossRefGoogle Scholar
  67. Vorasitchai S, Madarasmi S (2003) Improvements on layout of garment patterns for efficient fabric consumption, circuits and systems. In: ISCAS ‘03, Proceedings of the 2003 IEEE international symposium on circuits and systems, 25–28 May 2003, Bangkok, ThailandGoogle Scholar
  68. Wang Z, Li Y, Wong A (2005) Simulation of clothing thermal comfort with fuzzy logic. Elsevier Ergon Book Ser 3:467–471CrossRefGoogle Scholar
  69. Wang L, Chen Y and Wang Y (2008) Formalization of fashion sensory data based on fuzzy set theory. In: Guo M, Zhao L and Wang L (eds) Proceedings of the 4th international conference on natural computation, Jinan, China. IEEE Computer Society, pp 80–84Google Scholar
  70. Wong WK (2003) Optimisation of apparel manufacturing resource allocation using a generic optimised table-planning model. Int J Adv Manuf Technol 21(12):935–944CrossRefGoogle Scholar
  71. Wong WK, Chan CK (2001) An artificial intelligence method for planning the clothing manufacturing process. J Text Inst 92(2):168–178CrossRefGoogle Scholar
  72. Wong WK, Leung SYS (2008) Genetic optimization of fabric utilization in apparel manufacturing. Int J Prod Econ 114(1):376–387CrossRefGoogle Scholar
  73. Wong WK, Leung SYS (2009) A hybrid planning process for improving fabric utilization. Text Res J 79(18):1680–1695CrossRefGoogle Scholar
  74. Wong ASW, Li Y (2004) Prediction of clothing comfort perceptions using artificial intelligence hybrid models. Text Res J 74(1):13–19CrossRefGoogle Scholar
  75. Wong ASW, Li Y, Yeung PKW, Lee PWH (2003) Neural network predictions of human psychological perceptions of clothing sensory comfort. Text Res J 73(1):31–37CrossRefGoogle Scholar
  76. Wong WK, Kwong CK, Mok PY, Ip WH, Chan CK (2005a) Optimization of manual fabric-cutting process in apparel manufacture using genetic algorithms. Int J Adv Manuf Technol 27(1–2):152–158CrossRefGoogle Scholar
  77. Wong WK, Leung SYS, Au KF (2005b) A real-time GA-based rescheduling approach for the pre-sewing stage of an apparel manufacturing process. Int J Adv Manuf Technol 25(1–2):180–188CrossRefGoogle Scholar
  78. Wong WK, Kwong CK, Mok PY, Ip WH (2006a) Genetic optimization of JIT operation schedules for fabric-cutting process in apparel manufacture. J Intell Manuf 17:341–354CrossRefGoogle Scholar
  79. Wong WK, Mok PY, Leung SYS (2006b) Developing a genetic optimisation approach to balance an apparel assembly line. Int J Adv Manuf Technol 28(3/4):387–394CrossRefGoogle Scholar
  80. Wong WK, Yuen CWM, Fan DD, Chan LK, Fung EHK (2009) Stitching defect detection and classification using wavelet transform and BP neural network. Expert Syst Appl 36:3845–3856CrossRefGoogle Scholar
  81. Wong WK, Wang XX, Guo ZX (2013a) Optimizing marker planning in apparel production using evolutionary strategies and neural networks. In: Optimizing decision making in the apparel supply chain using artificial intelligence (AI): form production to retail. Woodhead Publishing Series in Textiles, pp 106–131CrossRefGoogle Scholar
  82. Wong WK, Mok PY, Leung SYS (2013b) Optimizing apparel production systems using genetic algorithms. In: Optimizing decision making in the apparel supply chain using artificial intelligence (AI): form production to retail. Woodhead Publishing Series in Textiles, pp 153–169CrossRefGoogle Scholar
  83. Wong WK, Guo Z, Leung S (2014) Intelligent multi-objective decision-making model with RFID technology for production planning. Int J Product Econ 147(Part C):647–658CrossRefGoogle Scholar
  84. Xue X, Zeng X, Koehl L (2016) An intelligent method for the evaluation and prediction of fabric formability for men’s suits. Scholar
  85. Yeun CWM, Wong WK, Qian SQ, Fan DD, Chan LK, Fung EHK (2009a) Fabric stitching inspection using segmented window technique and BP neural network. Text Res J 79(1):24–35CrossRefGoogle Scholar
  86. Yeun CWM, Wong WK, Qian SQ, Chan LK, Fung EHK (2009b) A hybrid model using genetic algorithm and neural network for classifying garment defects. Expert Syst Appl 36(2):2037–2047CrossRefGoogle Scholar
  87. Yildiz Z, Dal V, Ünal M, Yildiz K (2013) Use of artificial neural networks for modelling of seam strength and elongation at break. Fibres Text East Eur 21(5):117–123Google Scholar
  88. Yolmeh A, Kianfar A (2012) An efficient hybrid genetic algorithm to solve assembly line balancing problem with sequence dependent setup times. Comput Ind Eng 62(4):936–945CrossRefGoogle Scholar
  89. Zacharia P (2012) Robot handling fabrics towards sewing using computational intelligence methods. In: Dutta A (ed) Robotic systems—applications, control and programming. ISBN 978-953-307-941-7.
  90. Zacharia P, Aspragathos NA, Mariolis I, Dermatas E (2009) Robotic system based on fuzzy visual servoing for handling flexible sheets lying on a table. Ind Robot Int J 36(5):489–496CrossRefGoogle Scholar
  91. Zhang YH, Yuen CWM, Wong WK, Chi-wai Kan (2011) An intelligent model for detecting and classifying color-textured fabric defects using genetic algorithms and the Elman neural network. Text Res J 81(17):1772–1787CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Textile Materials and Processes Research Unit MPTexHigher Institute of Fashion Crafts of MonastirMonastirTunisia

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