Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries

In smart manufacturing, data-driven artificial intelligence algorithms are becoming increasingly important in improving decision-making by monitoring the control, analysis, and prediction of manufacturing processes in a production system. In the textile industry, there is a strong need for smart manufacturing technologies because various parameters could affect the quality dynamically. This study aims to optimize the parameters of spinning processes by developing machine learning algorithms and models which can predict the toughness and elasticity of threads. At first, meaningful variables are extracted from the shop floor data, and then a defect classification learning model is developed to predict defects in advance. In addition, a regression model is implemented for the prediction of toughness and elasticity of the textile. By transitioning from the traditional trial and error method to the data-based method for the spinning process, production costs and time can be reduced through optimal settings of the production parameters for the spinning of the desired threads.