Sage Journals: Discover world-class research

Abstract

The objective of this paper is to investigate the predictability of the warp breakage rate from a sizing yarn quality index using a feed-forward back-propagation network in an artificial neural network system. In order to achieve the objective, a series of trials is conducted. An eight-quality index (size add-on, abrasion resistance, abrasion resistance irregularity, hairiness beyond 3 mm, breaking strength, breaking strength irregularity, breaking elongation, and breaking elongation irregularity) and warp breakage rates are rated in controlled conditions. A good correlation between predicted and actual warp breakage rates indicates that warp breakage rates can be predicted by neural networks. A model with a single sigmoid hidden layer with four neurons is able to produce better predictions than the other models of this particular data set in the study.

Get full access to this article

View all access options for this article.

References

Aggarwal, S. K. , and Hari, P. K. , Factors Influencing Warp Breakage Rate I , Ind. Textile J. 118-125 (1989).

Aggarwal, S. K. , and Hari, P. K. , Factors Influencing Warp Breakage Rate II , Ind. Textile J. 148-153 (1989).

Aggarwal, S. K. , and Subramanian, T. A. , Prediction of Warp Breakage Rate in Weaving , Textile Res. J. 58(1), 11-21 (1988).

Aggarwal, S. K. , Subramanian, T. A. , and Hari, P. K. , Weavability—Dependence on Yarn Quality and Sizing , Textile Asia 18(2), 16-24 (1987).

Faasen, N. J. , and van Harten, K. , The Effect of Sizing on the Weavability of Cotton Yarns , J. Textile Inst. 57(7), 269-285 (1966).

Friedman, H. L. , Miller, B. , Kepka, S. , et al. , Abrasion Studies of Sized Cotton Yarns Before and After Weaving , Textile Res. J. 59(10), 622-629 (1989).

Friedman, H. L. , Zhou, Y. Y. , and Miller, B. , Development of Hairiness of Sized Warp Yarns During Flex-Abrasive Wear , Textile Res. J. 59(9), 495-500 (1989).

Hagan, M. T. , Demuth, H. B. , and Beale, M. , “Neural Network Design,” China Machine Press, Beijing , 2000.

Heap, V. , Abrasion vs Weavability , Textile Asia. 18(3), 98-102 (1987).

10.

Hornik, K. , Approximation Capabilities of Multilayer Feedforward Networks , Neural Networks 4(2), 251-257 (1991).

11.

Rumelhart, D. E. , McClelland, J. L. , Eds., “Parallel Distributed Processing—Explorations in the Microstructure of Cognition,” vol. 1, Foundations, MIT Press, Cambridge, MA , 1986.

12.

Slauson, Sherilyn, D. , Miller, Bernard , and Rebenfeld, Ludwig , Physicochemical Properties of Sized Yarns. Part I: Initial Studies , Textile Res. J. 54(10), 655-663 (1984).

13.

Thomas, H. L. , and Hines, R. W. , New Yarn Testing Methods to Predict Warp Yarn Weaving Performance , Int. Textile Bull. Yarn Fabric Forming 41(2), 56-59 (1995).

14.

Vidyarthi, S. P. , Hari, P. K. , and Aggarwal, V. K. , Analysis of Size Droppings and Their Correlation with Warp Breakages , Textile Res. J. 53(6), 334-340 (1983).

15.

Walker, R. P. , and Perkins, W. S. , Techniques for Predicting How Sized Yarns Will Weave , Textile Chem. Color. 17(9), 185-188 (1985).

16.

Wasserman, P. D. , “Advanced Methods in Neural Computing”, Van Nostrand Reinhold, NY , 1993.

Predicting the Warp Breakage Rate in Weaving by Neural Network Techniques

Abstract

Get full access to this article

References