Artificial intelligence enabled smart machining and machine tools

• Published in: Journal of mechanical science and technology Vol. 36; no. 1; pp. 1 - 23
• Main Authors: Chuo, Yu Sung, Lee, Ji Woong, Mun, Chang Hyeon, Noh, In Woong, Rezvani, Sina, Kim, Dong Chan, Lee, Jihyun, Lee, Sang Won, Park, Simon S.
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Mechanical Engineers 01.01.2022; Springer Nature B.V; 대한기계학회
• Subjects: Artificial intelligence; Control; Control stability; Dynamical Systems; Energy conservation; Engineering; Industrial and Production Engineering; Invited Review Article; Machine tools; Machining; Mechanical Engineering; Optimization; Prediction models; Tool wear; Vibration; 기계공학; Industry 4.0; Machine tools; Artificial intelligence; Machine learning; Machining
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-021-1201-0

Artificial intelligence (AI) in machine tools offers diverse advantages, including learning and optimizing machining processes, compensating errors, saving energy, and preventing failures. Various AI techniques have been proposed and applied; however, many challenges still exist that inhibit the use of AI for machining tasks. This paper deals with different types and usage of AI technologies in machining operations such as predictive modelling, parameter optimization and control, chatter stability, tool wear, and energy conservation. We discuss the challenges of AI technologies, such as data quality, transferability, explainability, and suggest future directions to overcome them.