Using Artificial Neural Network Algorithm to Predict Tensile Properties of Cotton-Covered Nylon Core Yarns

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Abstract

Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile properties of cotton-covered nylon core yarns. Multilayer Feedforward network with Back Propagation learning algorithm was used to study the relationship and mapping among the process parameters, i.e. count of sheath part, count of core part, applying pretension to the core part, inserted twist to the core spun-yarn as well as tensile properties, i.e. breaking strength and breaking elongation. The results show that ANN is an effective method for the prediction of the tensile properties of these yarns. This is due to the fact that in each case, standard deviation of prediction error for test and train data was less than that obtained from the expreiments.

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Journal of Advanced Materials in Engineering (Esteghlal)
Volume 24, Issue 2
January 2006
Pages 241-251

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