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

Chi Leung Hui, Sau Fun ng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

41 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1649-1657
Number of pages9
JournalTextile Research Journal
Issue number18
Publication statusPublished - 1 Jan 2009


  • artificial neural networks
  • commercial woven fabrics
  • multiple logarithm regression
  • seam performance

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Polymers and Plastics

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