Applications of Machine Learning in Textile Industry

Abstract

It is a question to all of us that what is machine learning and its importance for business sectors. Smart textile when using AI and computer vision try to act on knitting machines and to reduce production rate of defective types with real-time defects along with irregularities to identify with the objective to bring defective production at zero level. When ML and SC technique, artificial neural network, genetic algorithm, and fuzzy logic have preponderance over all others like Bibliographic analysis revealed a drastic rise in number of studies since 2015. It is proposed that demand forecasting is quite volatile and sensitive to several factors. Data mining studies with classification and clustering techniques, ML algorithms were implemented in textile industry. In a study it was found that machine productivity is having negative effect on quality which was confirmed by the management and confirmed a huge important in quality processing in industrial sectors. Demand forecasting by ML driven techniques has proved minimization of forecasting errors by 50%. ML is being used by Adidas to forecast demand in products since 2016. Ridge Regression estimated the correlation between predictor and observation variables.