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Predicting Seam Performance of Commercial Woven Fabrics Using Multiple Logarithm Regression and Artificial Neural Networks

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Abstract In this study, the capability of artificial neural networks and multiple logarithm regression methods for modeling seam performance of commercial woven fabrics based on seam puckering, seam flotation and seam efficiency were investigated. The developed models were assessed by verifying Mean Square Error (MSE) and Correlation Coefficient (R-value) of test data prediction. The results indicated that the artificial neural network (ANN) model has better performance in comparison with the multiple logarithm regression model. The difference between the mean square error of predicting in these two models for predicting seam puckering, seam flotation, and seam efficiency was 0.0394, 0.0096, and 0.0049, respectively. Thus, the ANN model was found to be more accurate than MLR, and the prediction errors of ANNs was low despite the availability of only a small training data set. However, the difference in prediction errors made by both models was not significantly high. It was found that MLR models were quicker to construct, more transparent, and less likely to overfit the minimal amount of data available. Therefore, both models were effectively predicting the   seam performance of woven fabrics.

In cut and sewn apparel products, seams are formed when two or more pieces of fabric are held together by stitches. As the seam is one of the basic requirements in the construction of apparel, seam quality has great significance in apparel products. Consumers evaluate the seam quality of a particular fabric mainly based on the seam performance of a particular fabric sewn by a particular sewing thread. Many previous studies [1–۴] have shown that seam per
formance of a particular fabric depends on the interrelationship of fabrics, threads, stitch type, seam type, and sewing conditions. Sewing conditions include the needle size, stitch density, and the appropriate operation and maintenance of the sewing machines. The seam perform
Abstract