Prediction of shrinkage and fabric weight(g/m2) of cotton single jersey knitted fabric using artificial neural network and comparison with general linear model

This paper presents a study which predicts the knitted fabric dimension scientifically in order to eliminate the unpredictable dimensional behavior of cotton knitted fabrics after washing when used by the customer. In this study experimental investigation was conducted to assess the shrinkage and fabric weight of plain knitted fabrics. Following a Full Factorial design of experimental plan, plain knitted fabrics were manufactured with four different yarn counts, each with three levels of twist factors, three machine gauges, and four levels of stitch lengths, making the total number of samples 144. The prepared fabric was then bleached and dyed. The finished fabric samples were divided into two groups; one group was allowed to relaxed in dry condition whereas the other group was subjected to repeated washing and tumble drying to achieve reference state. The shrinkage (in width and length direction) and fabric weight in g/m2 were measured following the standard procedure as mentioned in ASTM standards. The results were analyzed for shrinkage and fabric weight (g/m2) of cotton knitted fabric using the General Linear Model (GLM) technique and Artificial Neural Network (ANN) models. The predicted values of shrinkage and fabric weight (g/m2) from GLM and ANN models were then compared.

Anupreet, Kaur and Prof. Kalyan Roy
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Int J Inf Res Rev
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