Machine Learning and Artificial Intelligence Supported Machining: A Review and Insights for Future Research

Industry 4.0 and 5.0 have led to the extensive implementation of Artificial Intelligence (AI) and Machine Learning (ML). AI and ML signify a significant breakthrough in numerous fields by enabling more efficient data processing, offering enhancements across various services, and automation to replicate the learning process of machines, thereby enhancing system accuracy. In machining processes, AI and ML play crucial roles in predicting cutting forces, tool wear, and optimizing machining parameters. By employing advanced ML systems, machining operations can achieve longer cutting tool lifespan and increased efficiency. Additionally, these systems enable the prediction and enhancement of surface quality in machined components, contributing to overall part quality improvement. Furthermore, ML techniques are instrumental in analyzing and reducing power consumption during machining operations by predicting the energy consumption patterns of machine tools. This paper reviews the applications of AI and ML in machining operations and suggests future research directions. By examining recent achievements in the available literature, it aims to advance the research field by offering innovative concepts and approaches for integrating AI and ML into machining industries.