SpringerLink
Log in

Machine learning-based marker length estimation for garment mass customization

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The quick development of mass customization in the apparel industry leads to an exponential increase of garment size combinations for markers, which induces a heavy and complex workload of marker making. In this context, due to the complexity of the problem, the classical marker making methods using the existing commercialized software are less performant in terms of efficiency and accuracy. Therefore, machine learning techniques, usually taken as efficient tools for extracting relevant information from data measured in uncertain and complex scenarios, are considered much simpler and faster. In this study, we apply the methods of multiple linear regression (MLR) and radial basis function neural network (RBF NN) to estimate marker lengths that are used in various garment production modes by considering various sets of garment sizes and different marker types. The experimental results show that the proposed approach leads to a good performance in estimating marker lengths of different types of markers (mixed marker and group marker) with diverse size combinations taken from various sets of garment sizes in both mass production and mass customization conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

Data associated with this study is recorded.

References

  1. Nayak R & Padhye R (Eds.). (2015). Garment manufacturing technology. Elsevier.

  2. Naveed T, Hussain A, Zhong Y (2018) Reducing fabric wastage through image projected virtual marker (IPVM). Text Res J 88(14):1571–1580

    Article  Google Scholar 

  3. Thomassey S, Zeng X (eds) (2018) Artificial intelligence for fashion industry in the big data era. Springer, Singapore

    Google Scholar 

  4. Fowler RJ, Paterson MS, Tanimoto SL (1981) Optimal packing and covering in the plane are NP-complete. Inf Process Lett 12(3):133–137

    Article  MathSciNet  Google Scholar 

  5. Heckmann R, Lengauer T (1995) A simulated annealing approach to the nesting problem in the textile manufacturing industry. Ann Oper Res 57(1):103–133

    Article  Google Scholar 

  6. Jacobs-Blecha C, Ammons JC, Schutte A, Smith T (1997) Cut order planning for apparel manufacturing. IIE Trans 30(1):79–90

    Article  Google Scholar 

  7. Xu Y, Thomassey S, & Zeng X (2020) Optimization of garment sizing and cutting order planning in the context of mass customization. Int J Adv Manuf Technol, 1-19.

  8. Jaidormrong, J., Chaiyaratana, N., & Hassamontr, J. (2003). Software tool development for marker making operations in textile industry. In Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No. 03EX694) (Vol. 2, pp. 503-508). IEEE.

  9. Amaral C, Bernardo J, Jorge J (1990) Marker-making using automatic placement of irregular shapes for the garment industry. Comput Graph 14(1):41–46

    Article  Google Scholar 

  10. Hwan Sul I, ** Kang T (2002) Optimal marking of garment patterns using rectilinear polygon approximation. Int J Cloth Sci Technol 14(5):334–346

    Article  Google Scholar 

  11. Yeung LHW, Tang WKS (2003) A hybrid genetic approach for garment cutting in the clothing industry. IEEE Trans Ind Electron 50(3):449–455

    Article  Google Scholar 

  12. Wong WK, Leung SS (2009) A hybrid planning process for improving fabric utilization. Text Res J 79(18):1680–1695

    Article  Google Scholar 

  13. Wong WK, Wang XX, Mok PY, Leung SYS, Kwong CK (2009) Solving the two-dimensional irregular objects allocation problems by using a two-stage packing approach. Expert Syst Appl 36(2):3489–3496

    Article  Google Scholar 

  14. Wong WK, Guo ZX (2010) A hybrid approach for packing irregular patterns using evolutionary strategies and neural network. Int J Prod Res 48(20):6061–6084

    Article  Google Scholar 

  15. Ko E, Kim S (2013) Garment pattern nesting using image analysis and three-dimensional simulation. Fibers Polymers 14(5):860–865. https://doi.org/10.1007/s12221-013-0860-6

    Article  Google Scholar 

  16. Heckmann R, Lengauer T (1998) Computing closely matching upper and lower bounds on textile nesting problems. Eur J Oper Res 108(3):473–489

    Article  Google Scholar 

  17. Awais A, Naveed A (2015) Width-packing heuristic for grou** in two-dimensional irregular shapes cutting stock problem. Arab J Sci Eng 40(3):799–816

    Article  Google Scholar 

  18. Javanshir H, Rezaei S, Najar SS, Ganji SS (2010) Two dimensional cutting stock management in fabric industries and optimizing the large object’s length. Int J Res Rev Appl Sci 4(2)

  19. Bounsaythip, C., & Maouche, S. (1997, October). Irregular shape nesting and placing with evolutionary approach. In 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation (Vol. 4, pp. 3425-3430). IEEE.

  20. Vorasitchai S, Madarasmi S (2003) Improvements on layout of garment patterns for efficient fabric consumption. In Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS'03. (Vol. 4, pp. IV-IV). IEEE. https://doi.org/10.1109/iscas.2003.1206139

  21. Vilumsone-Nemes I (2018) Industrial cutting of textile materials. Woodhead Publishing.

  22. Haque MN (2016) Impact of different sorts of market efficiency in fabric consumption. Int J Text Sci 5(5):96–109

    Google Scholar 

  23. Abd Jelil R (2018) Review of artificial intelligence applications in garment manufacturing. In Artificial Intelligence for Fashion Industry in the Big Data Era (pp. 97-123). Springer, Singapore

  24. Thomassey S, Happiette M (2007) A neural clustering and classification system for sales forecasting of new apparel items. Appl Soft Comput 7(4):1177–1187

    Article  Google Scholar 

  25. Ferreira KJ, Lee BHA, Simchi-Levi D (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manuf Serv Oper Manag 18(1):69–88

    Article  Google Scholar 

  26. Huang H, Liu Q (2017) Intelligent retail forecasting system for new clothing products considering stock-out. Fibres Text East Eur 25:10–16

    Article  Google Scholar 

  27. Lin TH (2004) Construction of Predictive Model on Fabric and Sewing Thread Optimization. J Text Eng 50(1):6–11

    Article  Google Scholar 

  28. Jaouadi M, Msahli S, Babay A, Zitouni B (2006) Analysis of the modeling methodologies for predicting the sewing thread consumption. Int J Cloth Sci Technol 18(1):7–18

    Article  Google 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 Inst 96(6):401–405

    Article  Google 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):1649–1657

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Pavlinic DZ, Gersak J (2009) Predicting garment appearance quality. Open Text J 2(1):29–38

    Article  Google Scholar 

  33. 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 

  34. Zhang X, & Wong LY (2014) Virtual fitting: real-time garment simulation for online shop**. In SIGGRAPH Posters (pp. 41-1).

  35. Liu K, Zeng X, Bruniaux P, Wang J, Kamalha E, Tao X (2017) Fit evaluation of virtual garment try-on by learning from digital pressure data. Knowl-Based Syst 133:174–182

    Article  Google Scholar 

  36. Foysal KH, Chang HJ, Bruess F, Chong JW (2021) SmartFit: smartphone application for garment fit detection. Electronics 10(1):97

    Article  Google Scholar 

  37. 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 2015:1–9

    Article  Google Scholar 

  38. Salamone F, Belussi L, Currò C, Danza L, Ghellere M, Guazzi G, Lenzi B, Megale V, Meroni I (2018) Integrated method for personal thermal comfort assessment and optimization through users’ feedback, IoT and machine learning: a case study. Sensors 18(5):1602

    Article  Google Scholar 

  39. Koustoumpardis PN, Aspragathos NA (2014) Intelligent hierarchical robot control for sewing fabrics. Robot Comput Integr Manuf 30(1):34–46

    Article  Google Scholar 

  40. Ramisa A, Alenya G, Moreno-Noguer F, Torras C (2014) Learning RGB-D descriptors of garment parts for informed robot gras**. Eng Appl Artif Intell 35:246–258

    Article  Google Scholar 

  41. Degraeve Z, Gochet W, Jans R (2002) Alternative formulations for a layout problem in the fashion industry. Eur J Oper Res 143(1):80–93

    Article  Google Scholar 

  42. Murray AF (ed) (1995) Applications of neural networks (pp. 157-189). Kluwer Academic Publishers, The Netherlands

    Google Scholar 

  43. Mujtaba IM (2001) Application of neural networks and other learning technologies in process engineering. World Scientific

  44. Cheng CB, Lee ES (2001) Fuzzy regression with radial basis function network. Fuzzy Sets Syst 119(2):291–301

    Article  MathSciNet  Google Scholar 

  45. Diamantidis NA, Karlis D, Giakoumakis EA (2000) Unsupervised stratification of cross-validation for accuracy estimation. Artif Intell 116(1-2):1–16 Abd Jelil, R. (2018)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

Thanks to Dr **ang YAN for valuable editorial comments and suggestions.

Funding

Thanks to the Chinese Scholarship Council (CSC) and Zhejiang Sci-Tech University for the financial support of this research.

Author information

Authors and Affiliations

  1. School of International Education, Zhejiang Sci-Tech University, Hangzhou, 310018, China

    Yanni Xu

  2. Laboratoire Génie et Matériaux Textile (GEMTEX), the ENSAIT Textile Institute, Centrale Lille, F-59000, Lille, France

    Yanni Xu, Sébastien Thomassey & **anyi Zeng

Authors
  1. Yanni Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sébastien Thomassey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. **anyi Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, Yanni Xu and Sébastien Thomassey; investigation, Yanni Xu; data collection and curation, Yanni Xu; writing—original draft preparation, Yanni Xu; writing—review and editing, Sébastien Thomassey and **anyi Zeng; supervision, Sébastien Thomassey and **anyi Zeng.

Corresponding author

Correspondence to Yanni Xu.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

The authors confirm that the manuscript has been read and approved by all named authors.

Consent for publication

All the authors have reached agreement for publication in The International Journal of Advanced Manufacturing Technology.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, Y., Thomassey, S. & Zeng, X. Machine learning-based marker length estimation for garment mass customization. Int J Adv Manuf Technol 113, 3361–3376 (2021). https://doi.org/10.1007/s00170-021-06833-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-06833-w

Keywords

Search

Navigation