Application of artificial neural networks to the prediction of sewing performance of fabrics

Abstract

Purpose

This paper aims to investigate the use of artificial neural networks (ANN) to predict the sewing performance of fabrics. The purpose of this study is to verify the ANN techniques that could be emulated as human decision in the prediction of sewing performance of fabrics.

Design/methodology/approach

In order to verify the ANN techniques that could be emulated as human decision in the prediction of sewing performance of fabrics, 109 data sets of fabrics were tested by using fabric assurance by simple testing system and the sewing performance of each fabric's specimen was assessed by the domain experts. Of these 109 input‐output data pairs, 94 were used to train the proposed backpropagation (BP) neural network for the prediction of the unknown sewing performance of a given fabric, and 15 were used to test the proposed BP neural network.

Findings

After 10,000 iterations of training of BP neural network, the neural network converged to the minimum error level. The experimental results reveal the great potential of the proposed approach in predicting the sewing performance of fabrics for apparel production.

Originality/value

Generally, the fabric's performance in the manufacturing process is judged subjectively by the operators and/or their supervisors. Current methodologies of acquiring fabric property information and predicting fabric sewing performance are still incapable of providing a means for efficient planning and control for the sewing operation. Further, development of techniques to predict the sewing performance of fabric is essential for the current apparel production environment. In this paper, the use of ANN to predict the sewing performance of fabrics in garment manufacturing is investigated.

Citation

Hui, P.C.L., Chan, K.C.C., Yeung, K.W. and Ng, F.S.F. (2007), "Application of artificial neural networks to the prediction of sewing performance of fabrics", International Journal of Clothing Science and Technology, Vol. 19 No. 5, pp. 291-318. https://doi.org/10.1108/09556220710819500