Predicting the Warp Breakage Rate in Weaving by Neural Network Techniques

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.

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