Estimation of Ease Allowance of a Garment using Fuzzy Logic

  • Y. Chen
  • X. zeng
  • M. Happiette
  • P. Bruniaux
  • R. Ng
  • W. Yu
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 201)

Abstract

The ease allowance is an important criterion in garment sales. It is often taken into account in the process of construction of garment patterns. However, the existing pattern generation methods can not provide a suitable estimation of ease allowance, which is strongly related to wearer’s body shapes and movements and used fabrics. They can only produce 2D patterns for a fixed standard value of ease allowance. In this chapter, we propose a new method of estimating ease allowance of a garment using fuzzy logic and sensory evaluation. Based on these values of ease allowance, we develop a new method of automatic pattern generation, permitting to improve the wearer’s fitting perception of a garment. The effectiveness of our method has been validated in the design of trousers of jean type. It can also be applied for designing other types of garment.

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References

  1. 1.
    C.A. Crawford, The Art of Fashion Draping, 2nd Edition, Fairchild Publications, New York (1996).Google Scholar
  2. 2.
    W. Aldrich, Metric Pattern Cutting for Men’s Wear, 3rd Edition, Blackwell Science Ltd, Cambridge (1997).Google Scholar
  3. 3.
    R. Ng, Computer Modeling for Garment Pattern Design, Ph.D. Thesis, The Hong Kong Polytechnic University (1998).Google Scholar
  4. 4.
    K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. San Diego, CA: Academic (1990).MATHGoogle Scholar
  5. 5.
    E.H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. of Man-Machine Studies, 7, 1–13 (1975).MATHCrossRefGoogle Scholar
  6. 6.
    J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press (1981).Google Scholar
  7. 7.
    P.T. Chan and A.B. Rad, Antecedent validity adaptation principle for fuzzy systems tuning, Fuzzy Sets and Systems, 131, pp.153–163 (2002).MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    L.X. Wang and J.M. Mendel, Generating fuzzy rules by learning from examples, IEEE Trans. on SMC, vol.22, no.6, pp.1414–1427 (1992).MathSciNetGoogle Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Y. Chen
    • 1
  • X. zeng
    • 1
  • M. Happiette
    • 1
  • P. Bruniaux
    • 1
  • R. Ng
    • 2
  • W. Yu
    • 2
  1. 1.Ecole Nationale Supérieure des Arts & Industries TextilesRoubaixFrance
  2. 2.The Hong Kong Polytechnic UniversityHong KongChina

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