Hybrid Neuro-Genetic Machine Learning Models for the Engineering of Ring-spun Cotton Yarns

Abstract

ABSTRACT Yarn engineering is a long-standing problem for the cotton spinning industry as the functional relationship between fiber and yarn properties is quite complex. The objective of this research is to develop a hybrid machine learning-based prescriptive yarn engineering system that can foretell the properties of cotton fiber for achieving desired yarn properties. Artificial neural network (ANN) and genetic algorithm (GA) were used to develop the predictive model for cotton yarn properties and optimization of cotton fiber properties, respectively. Two separate ANN models were developed for predicting yarn tenacity and yarn unevenness. The functional relationships approximated by the ANN models were used to formulate the fitness function for GA. The validation of the ANN-GA system demonstrated good accuracy as cotton fiber strength, length and length uniformity were predicted with very good accuracy (mean error < 5%). The developed machine learning system can supplant the intuition-based decision making in textile spinning industry and pave the way for yarn engineering.