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

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.

摘要

对于棉纺行业来说,纱线工程是一个长期存在的问题,因为纤维和纱线性能之间的函数关系非常复杂。本研究的目的是开发一个基于混合机器学习的规定性纱线工程系统,该系统可以预测棉纤维的性能,以实现所需的纱线性能。采用人工神经网络(ANN)和遗传算法(GA)分别建立了棉纱性能预测模型和棉纤维性能优化模型。开发了两个单独的ANN模型,用于预测纱线的强力和不匀率。利用人工神经网络模型逼近的函数关系,建立了遗传算法的适应度函数。ANN-GA系统的验证显示了良好的准确性,因为棉纤维强度、长度和长度均匀性的预测精度非常高(平均误差<5%)。所开发的机器学习系统可以取代纺织行业基于直觉的决策,为纱线工程铺平道路。

KEYWORDS:

• Artificial neural network
• genetic algorithm
• machine learning
• prescriptive analytics
• yarn engineering

关键词:

• 人工神经网络
• 遗传算法
• 机器学习
• 规定性分析
• 纱线工程

Highlights

• A machine learning-based cotton yarn engineering system has been proposed.

• Artificial neural network and Genetic algorithm have been hybridized for modeling and optimization.

• Cotton fibre properties are predicted for targeted values of yarn tenacity and yarn unevenness.

• The model is able to predict the cotton fibre properties with very good accuracy (error < 5%).

Disclosure statement

No potential conflict of interest was reported by the author(s).