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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. [ABSTRACT FROM AUTHOR]

Keywords: Spinning waste; cotton fibers; web; nonwoven; tear strength; burst strength; ; ; ; ; ; EN Spinning waste cotton fibers web nonwoven tear strength burst strength ZH 9064 9073 10 11/21/22 20221220 NES 221220 Introduction The raw material is considered as one of the most important factors affecting product costs in the textile industry based on its type, processing, and end-use. In this work, the webs of cotton fibers from spinning soft and hard wastes were formed after separating the short fibers from impurities by trash analyzer and carding fibers to form a web. Thus, textile material wastes can be classified as pre-consumer textile waste (soft and hard waste) and post-consumer textile waste. Following the procedures of staple length measurement mentioned above, the mean of fibers length from spinning waste is; I Mean fibers length (cm) = 1.34 ± 0.53 i The standard classification indicates that those fibers with length of 1 in. or less are categorized under short-staple fibers (Klein [11]). [Extracted from the article]