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Smart machining process using machine learning: A review and perspective on machining industry

Cited 171 time in Web of Science Cited 220 time in Scopus
Authors

Kim, Dong-Hyeon; Kim, Thomas J. Y.; Wang, Xinlin; Kim, Mincheol; Quan, Ying-Jun; Oh, Jin Woo; Min, Soo-Hong; Kim, Hyungjung; Bhandari, Binayak; Yang, Insoon; Ahn, Sung-Hoon

Issue Date
2018-08
Publisher
한국정밀공학회
Citation
International Journal of Precision Engineering and Manufacturing-Green Technology, Vol.5 No.4, pp.555-568
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.
ISSN
2288-6206
Language
English
URI
https://hdl.handle.net/10371/149810
DOI
https://doi.org/10.1007/s40684-018-0057-y
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