Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30837
Title: A new approach for prediction of sewing performance of fabrics in apparel manufacturing using artificial neural networks
Authors: Hui, CL 
Ng, SF 
Keywords: Apparel manufacturing
Artificial neural networks
Sewing performance
Issue Date: 2005
Publisher: Routledge, Taylor & Francis Group
Source: Journal of the Textile Institute, 2005, v. 96, no. 6, 186, p. 401-405 How to cite?
Journal: Journal of the Textile Institute 
Abstract: This paper investigates the use of extended normalized radial basis function (ENRBF) neural networks to predict the sewing performance of fabrics in apparel manufacturing. In order to evaluate the performance of the ENRBF neural networks that could be emulated as human decision in the prediction of sewing performance of fabrics more effectively, it could be compared with the traditional back-propagation (BP) neural networks in terms of prediction errors. There are 109 data sets cover fabric properties measured by using a computerized measuring system, and the sewing performance of each fabric's specimen assessed by the domain experts. Of these 109 input-output data pairs, 94 were used to train the proposed ENRBF and BP neural networks for the prediction of the unknown sewing performance of a given fabric, and 15 were used to test the proposed ENRBF and BP neural networks, respectively. After 10,000 iterations of training of the ENRBF and BP neural networks, both of them converged to the minimum error level. A comparison was made between actual fabric performances during sewing, the experts' advices, and the results of predicting fabric performances during sewing for both networks. It was found that the ENRBF and BP neural networks indicate similar error levels, but the prediction made by the ENRBF neural network is better than the prediction made by the BP neural network in some areas. Both the systems provided better advice than the experts in some areas, when compared to actual sewing performance.
URI: http://hdl.handle.net/10397/30837
ISSN: 0040-5000
EISSN: 1754-2340
DOI: 10.1533/joti.2005.0101
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