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Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1033)

Abstract

The relationship between visual appearance and structure and technical properties of a knitted fabric is subtle and complex. This is an area that has been traditionally problematic within the knitting sector, understanding between technologists and designers is hindered which limits the possibility of dialogues from which design innovation can emerge. Recently there has been interest from the Human-Computer Interaction (HCI) community to narrow the gap between product design and knitwear. The goal of this article is to show the potential of predictive software design tools for fashion designers who are developing personalized advanced functionalities in textile products. The main research question explored in this article is: “How can designers benefit from intelligent design software for the manufacturing of personalized advanced functionalities in textile products?”. In particular we explored how to design interactions and interfaces that use intelligent predictive algorithms through the analysis of a case study, in which several predictive algorithms were compared in the practice of textile designers.

Keywords

User Interface Machine learning Knitwear 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC, Grant No. 51750110497). Many thanks for the generous support of Santoni Shanghai, Studio Eva x Carola, and the support from Yuanjin Liu and Yifan Zhang.

References

  1. 1.
    ten Bhömer, M., Canova, R., de Laat, E.: Body inspired design for knitted body-protection wearables. In: Proceedings of the 2018 ACM Conference Companion Publication on Designing Interactive Systems, pp. 135–139. ACM, New York (2018)Google Scholar
  2. 2.
    ten Bhömer, M., Jeon, E., Kuusk, K.: Vibe-ing: designing a smart textile care tool for the treatment of osteoporosis. In: Chen, L.L., et al. (eds.) Proceedings of the 8th International Conference on Design and Semantics of Form and Movement, pp. 192–195. Koninklijke Philips Design (2013)Google Scholar
  3. 3.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  4. 4.
    Black, S.: Innovative knitwear design utilising seamless and unconventional construction, London, UK (2002)Google Scholar
  5. 5.
    Eckert, C.: The communication bottleneck in knitwear design: analysis and computing solutions. Comput. Support. Coop. Work 10(1), 29–74 (2001)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hsu, C.H., Wang, M.J.J.: Using decision tree-based data mining to establish a sizing system for the manufacture of garments. Int. J. Adv. Manuf. Technol. 26(5), 669–674 (2005)CrossRefGoogle Scholar
  7. 7.
    Jaouachi, B., Khedher, F.: Evaluation of sewed thread consumption of jean trousers using neural network and regression methods. Fibres Text. East. Eur. 23, 91–96 (2015)CrossRefGoogle Scholar
  8. 8.
    Karmon, A., Sterman, Y., Shaked, T., Sheffer, E., Nir, S.: KNITIT: a computational tool for design, simulation, and fabrication of multiple structured knits. In: Proceedings of the 2nd ACM Symposium on Computational Fabrication, pp. 4:1–4:10. ACM, New York (2018)Google Scholar
  9. 9.
    Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer, New York (2013).  https://doi.org/10.1007/978-1-4614-6849-3CrossRefzbMATHGoogle Scholar
  10. 10.
    Lau, F., Yu, W.: Seamless knitting of intimate apparel. In: Yu, W. (ed.) Advances in Women’s Intimate Apparel Technology, pp. 55–68. Woodhead Publishing (2016)Google Scholar
  11. 11.
    Matković, V.M.P.: The power of fashion: the influence of knitting design on the development of knitting technology. Textile 8(2), 122–146 (2010)CrossRefGoogle Scholar
  12. 12.
    Matusiak, M.: Application of artificial neural networks to predict the air permeability of woven fabrics. Fibres Text. East. Eur. 23, 41–48 (2015)CrossRefGoogle Scholar
  13. 13.
    McCann, J., et al.: A compiler for 3D machine knitting. ACM Trans. Graph. 35(4), 49:1–49:11 (2016)CrossRefGoogle Scholar
  14. 14.
    Narayanan, V., Albaugh, L., Hodgins, J., Coros, S., Mccann, J.: Automatic machine knitting of 3D meshes. ACM Trans. Graph. 37(3), 35:1–35:15 (2018)CrossRefGoogle Scholar
  15. 15.
    Ogulata, S.N., Sahin, C., Ogulata, R.T., Balci, O.: The prediction of elongation and recovery of woven bi-stretch fabric using artificial neural network and linear regression models. Fibres Text. East. Eur. 14(2), 9–46 (2006)Google Scholar
  16. 16.
    Power, E.J.: Advanced knitting technologies for high-performance apparel. In: McLoughlin, J., Sabir, T. (eds.) High-Performance Apparel, pp. 113–127. Woodhead Publishing (2018)Google Scholar
  17. 17.
    Sayer, K., Wilson, J., Challis, S.: Seamless knitwear - the design skills gap. Des. J. 9(2), 39–51 (2006)Google Scholar
  18. 18.
    Spencer, D.J.: Knitting Technology: A Comprehensive Handbook and Practical Guide, 3rd edn. Woodhead Publishing, Sawston (2001)CrossRefGoogle Scholar
  19. 19.
    Yildirim, P., Birant, D., Alpyildiz, T.: Data mining and machine learning in textile industry. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(1), e1228 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Xi’an Jiaotong-Liverpool UniversitySuzhouPeople’s Republic of China

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