Predicting thermal resistance of cotton fabrics by artificial neural network model

Abstract

This paper presents the prediction of thermal resistance of handloom cotton fabrics by artificial neural network models using four primary fabric construction parameters, i.e. ends per inch (EPI), picks per inch (PPI), warp count and weft count as the inputs. ANN model with seven nodes in the single hidden layer exhibited the overall best performance with coefficient of determination of 0.90 and 0.86 and mean absolute error of only 5.13% and 4.23% during training and testing respectively. The importance of fabric construction parameters on the thermal resistance of fabrics was also analyzed by the developed ANN model. Weft count, EPI and warp count were found to be the first three most important fabric constructional parameters in descending order of importance in predicting thermal resistance of plain woven cotton fabrics.