Optimizing Textile Manufacturing With Neural Network Decision Support: An Ornstein-Uhlenbeck Reinforcement Learning Approach

Abstract

The textile sector makes a substantial contribution to the global economy. During the Industry 4.0 era, the textile production process is anticipated to become more adaptable and versatile. The complex interplay between the macroscopic factors in different textile processes may make decision-making challenging. To address these challenges and provide a viable answer to textile manufacturing companies, machine learning methods are suggested to develop decision support systems. It is possible for a model to correctly reflect the intricate relationships that exist between the parameters and performances of a textile manufacturing process. This representation serves as the foundation for a decision support system. Especially in this work, quality control can be improved by predicting the defects in fabrics using an effective Ornstein-Uhlenbeck reinforcement learning-based decision neural network (OU_RL_DNN), which can detect both local and global defects. The framework's approach effectively reduces the expense of manually annotating the dataset. It takes a few faulty samples mixed with reference samples to learn fault characteristics and properly locate them. The experiment, which comprised a data set that was available to the public and three private fabric data sets, demonstrated that the proposed method surpasses a number of cutting-edge methods in terms of detection and detection time.