Abstract
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.
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Acknowledgements
This research was supported by the MSIT (Ministry of Science, ICT), Korea, under the High-Potential Individuals Global Training Program (2020-0-01529) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A2A1A05079477).
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Simon S. Park is a Professor at the Schulich School of Engineering, Dept. of Mechanical and Manufacturing Engineering, University of Calgary, Canada. He was an AITF iCORE Chair in sensing and monitoring. He is a professional engineer in Alberta and is an associate member of CIRP (Int. Academy of Production Engineers) from Canada. Dr. Park received bachelor and master’s degrees from the University of Toronto, Canada. He then continued his Ph.D. at the University of British Columbia, Canada.
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Chuo, Y.S., Lee, J.W., Mun, C.H. et al. Artificial intelligence enabled smart machining and machine tools. J Mech Sci Technol 36, 1–23 (2022). https://doi.org/10.1007/s12206-021-1201-0
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DOI: https://doi.org/10.1007/s12206-021-1201-0