Performance of Error Back Propagation vis-à-vis Radial Basis Function Neural Network: Part II: Reverse Engineering of Woven Fabrics

Abstract

This paper describes the application of an artificial neural network approach to engineer the design of woven wool and wool blended suiting fabric, to be used by the weavers. Two neural network models based on error back propagation and radial basis function algorithms are used for ascertaining fabric constructional parameters such as fibre composition, yarn density, yarn tex, weave, yarn crimp and yarn twist to obtain desired low stress mechanical properties and other properties such as breaking strength and extension, bending rigidity, shear rigidity and tear strength of the suiting fabric. Of the two networks, radial basis function network is again found to be fast to train and easier to design than back propagation network. Evaluation of both the models for each fabric property specification shows good agreement between predicted and generally accepted fabric, yarn structure–property relationships.