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Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry

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Abstract

The Fourth Industrial Revolution incorporates the digital revolution into the physical world, creating a new direction in a number of fields, including artificial intelligence, quantum computing, nanotechnology, biotechnology, robotics, 3D printing, autonomous vehicles, and the Internet of Things. The artificial intelligence field has encountered a turning point mainly due to advancements in machine learning, which allows machines to learn, improve, and perform a specific task through data without being explicitly programmed. Machine learning can be utilized with machining processes to improve product quality levels and productivity rates, to monitor the health of systems, and to optimize design and process parameters. This is known as smart machining, referring to a new machining paradigm in which machine tools are fully connected through a cyber-physical system. This paper reviews and summarizes machining processes using machine learning algorithms and suggests a perspective on the machining industry.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, 08826, Republic of Korea

    Dong-Hyeon Kim, Thomas J. Y. Kim, Xinlin Wang, Mincheol Kim, Ying-Jun Quan, Jin Woo Oh, Soo-Hong Min & Sung-Hoon Ahn

  2. BK21 Plus Transformative Training Program for Creative Mechanical and Aerospace Engineers, Seoul National University, Seoul, 08826, Republic of Korea

    Dong-Hyeon Kim

  3. Optical Instrumentation Research Center, Korea Basic Science Institute, Daejeon, 34133, Republic of Korea

    Mincheol Kim

  4. Institute of Advanced Machines and Design (IAMD), Seoul National University, Seoul, 08826, Republic of Korea

    Hyungjung Kim & Sung-Hoon Ahn

  5. Department of Railroad Integrated System, Woosong University, Daejeon, 34518, Republic of Korea

    Binayak Bhandari

  6. Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, 08826, Republic of Korea

    Insoon Yang

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  1. Dong-Hyeon Kim
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Correspondence to Sung-Hoon Ahn.

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Kim, DH., Kim, T.J.Y., Wang, X. et al. Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry. Int. J. of Precis. Eng. and Manuf.-Green Tech. 5, 555–568 (2018). https://doi.org/10.1007/s40684-018-0057-y

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  • DOI: https://doi.org/10.1007/s40684-018-0057-y

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