Predicting residual bagging bend height of knitted fabric using fuzzy modelling and neural networks

• Autorzy: Jaouachi B , Louati H. , Hellali H.
• Języki publikacji: EN

Abstrakty

EN

In this research, fuzzy modelling and neural network methods were used and compared to predict the residual bagging bend height of knitting fabric samples. Studies undertaken to minimize the bagging phenomenon vary significantly with the test conditions including the experimental field of interest, the input parameters and the applied method. Hence, we attempt to formulate a theoretical model of predicting bagging behaviour in our experimental design of interest. By analysing the bend height of overall bagging samples, this paper provides an effective neural network model to evaluate and predict the residual bagging bend height of the knitting specimens after test. It also provides the impact of each input parameter in our experimental field of interest to simulate this phenomenon after use. Moreover, the contribution of these influential input parameters was analysed and discussed. Nevertheless, our results show that residual bagging height decreases when yarn contains elastane filament, Spandex©. This finding is in agreement with Mirostawa et al. [11] that with an increase of the elastane content in fabric, permanent bagging decreases, whereas elastic bagging increases. According to the analytical results obtained, the neural network model gives a more accurate prediction than the fuzzy one.

Słowa kluczowe

EN

fuzzy logic analysis; neural network; bagging bend height; knitting fabric; elastane filament; residual deformation

PL

analiza logiki rozmytej; sieć neuronowa; dzianina

• Wydawca: Lodz University of Technology, Faculty of Material Technologies and Textile Design
• Czasopismo: Autex Research Journal
• Rocznik: 2010
• Tom: Vol. 10, no. 4
• Strony: 110--115
• Opis fizyczny: Bibliogr. 25 poz., wykr.

Twórcy

autor

Jaouachi B

• Textile Research Unit of I.S.E.T Ksar Hellal, B.P. 68 Ksar Hellal 5070, Tunisia, National School of Engineers of Monastir- Tunisia

autor

Louati H.

• Textile Research Unit of I.S.E.T Ksar Hellal, B.P. 68 Ksar Hellal 5070, Tunisia, National School of Engineers of Monastir- Tunisia

autor

Hellali H.

• Textile Research Unit of I.S.E.T Ksar Hellal, B.P. 68 Ksar Hellal 5070, Tunisia, National School of Engineers of Monastir- Tunisia

Bibliografia

• 1. Araújo, M., Fangueiro, R., and Hong, H. (2004). Modelling and simulation of the mechanical behaviour of weft-knitted fabrics for technical applications, Part IV: 3D FEA model with a mesh of tetrahedric elements, Autex Res. J., 4 (2), 72-80.
• 2. Spencer, D., (2004). Knitting Technology – A Comprehensive Handbook and Practical Guide (3rd Edition), Woodhead Publishing, 60-69.
• 3. Altinoz, C., and Winchester, S. (2001). A Fuzzy Approch to Supplier Selection, J. Text. Inst., 92 (2), 155- 167.
• 4. Youkura, H., Nagae, S. and Niwa, M. (1986). Contributions of in-plane fabric tensile properties in woven fabric bagging behaviour using a new developed test method, Textile Res. J., 56, 748-754.
• 5. Uçar, N., Realff, M., Radhakrishnaiah, P., Ucar, M. (2002), Objective and subjective analysis of knitted fabric bagging, Textile Res. J., 72, (11), 977-982.
• 6. Jaouachi, B., Mohamed Ben Hassen, M. Sahnoun, Faouzi Sakli, (2010), Evaluation of wet pneumatically spliced elastic denim yarns with fuzzy theory, J. Tex. Inst., 101 (2), 111-119.
• 7. Cox, E. (1995), Fuzzy logic for business and industry, MA: Charles River Media, 39-59.
• 8. Hyung, T., Sung, H., Sook, R., Jae, Y., & Seong, H. (2001), Detecting fabric defects with computer vision and fuzzy rule generation part II: defect identification by a fuzzy expert system. Textile Res. J., 71(7), 563-573.
• 9. Majumdar, A., Majumdar, P., and Sarkar, B. (2005), Application of an adaptive neuro-fuzzy system for the prediction of cotton yarn strength from fibre properties, J.Tex. Inst., 96, 55-60.
• 10. Kisiliak, D. (1999), A new method of evaluating spherical fabric deformation, Textile Res. J., 69, 908-913.
• 11. Kocik, M., Zyrek, W., Gersak, I., Jakubczyk, J. (2005), Fibers & Textile in Eastern Europe, 13 (2), 50, 31-34.
• 12. Kumar, P., and Majumdar, A. (2004), Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models, Textile Res. J., 74 (7), 652-655.
• 13. Fan, J., Newton, E., Au, R., and Chan, S. (2001), Predicting Garment Drape with a Fuzzy-Neural Network, Textile Res. J., 71 (7), 605-608.
• 14. Mori, T., and Komiyama, J., (2002), Evaluating Wrinkled Fabrics with Image Analysis and Neural Networks, Textile Res. J., 72 (5), 417-422.
• 15. Cheng, K., and Lam, H., (2003), Evaluating and Comparing the Physical Properties of Spliced Yarns by Regression and Neural Network Techniques, Textile Res. J., 73(2), 161-164.
• 16. Park, C., and Kang, T. (1997), Objective Rating of Seam Pucker Using Neural Networks, Textile Res. J., 67 (7), 494-502.
• 17. Yeung, K., Li, Y., Zhang, X., and Yao, M. (2002), Evaluating and Predicting Fabric Bagging with Image Processing, Textile Res. J., 72, 693-700.
• 18. Zhang X., Li, Y., Yeung, K., and Yao, M., (1999), Fabric Bagging. Part II: Objective Evaluation and Physical Mechanism, Textile Res. J.; 69(8), 598-606.
• 19. Zhang, X., Li, Y., Yeung, K., Miao M., and Yao, M. (2000), Fabric- bagging: Stress distribution in isotropic and anisotropic fabrics, J. Text. Inst., 91(4), 563-576.
• 20. Zhang, X., Li, Y., Yeung, K., Miao, M., and Yao, M., (2000), Mathematical simulation of fabric bagging, Textile Res. J., 70 (1), 18-28.
• 21. Association Française de Normalisation, (1985), Détermination de l’autodefroissabilité: Méthode au cylinder creux, recueil de norme française: Textile Etoffe, tome 2, 256-265.
• 22. AFNOR (1985), Textile fibres et fils analyse chimique des textiles, 212-230.
• 23. Grunewald K., and Zoll, W. (1973), Inst. Textile Bull., 3, 273-275.
• 24. Zhang, X., Li, Y., Yeung, K., and Yao, M. (2000), Textile Res. J., 69(7), 511-518.
• 25. Zhang, X., Yeung, K., Yao M., Li Y., (1997), Factors influencing bagging behaviour of woven fabrics, Proc. The 4th Asian Conference, 24-26, 512-517.