Security wire mesh weaving process modelling with artificial neural network

Abstract

This paper investigates weaving process in the production of security woven wire mesh. Weaving is a critical process of the entire production as the quality of the final product depends very much on this process. High defect rate and low production yield is now a major concern in the production. There has been no prior study of the relationship among variables such as inspection data and machine setting on production yield. Conducting experiments to investigate this relationship is not reasonable in this case, as the product targeted at premium market and scrap cost is very high. In order to investigate the effect of these parameters, artificial neural network (ANN) was applied to model the process with data from the company databases. The type of ANN used in this research was the multi-layer neural network trained with back-propagation algorithm. The results suggested that ANN can effectively be used to predict weaving process production yield. The use of ANN proposed in this research is not limit to only weaving process, but can be applied to other manufacturing process.