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Artificial intelligence enabled smart machining and machine tools

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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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Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr. NW, Calgary, Alberta, Canada

    Yu Sung Chuo, Sina Rezvani, Jihyun Lee & Simon S. Park

  2. Department of Mechanical Engineering, Graduate School, Sungkyunk-wan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, Korea

    Ji Woong Lee, In Woong Noh & Sang Won Lee

  3. Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan, 44919, Korea

    Chang Hyeon Mun & Dong Chan Kim

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  1. Yu Sung Chuo
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  2. Ji Woong Lee
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  3. Chang Hyeon Mun
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  5. Sina Rezvani
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  6. Dong Chan Kim
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  7. Jihyun Lee
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  8. Sang Won Lee
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  9. Simon S. Park
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Correspondence to Simon S. Park.

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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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