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

• Published in: International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4) Vol. 5; no. 4; pp. 555 - 568
• Main 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
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society for Precision Engineering 01.08.2018; Springer Nature B.V; 한국정밀공학회
• Subjects: Algorithms; Artificial intelligence; Biotechnology; Cyber-physical systems; Design optimization; Design parameters; Energy Efficiency; Engineering; Industrial and Production Engineering; Industry 4.0; Internet of Things; Learning algorithms; Machine learning; Machine tools; Manufacturing; Nanotechnology; Neural networks; Process parameters; Quantum computing; Review Paper; Robotics; Sustainable Development; Three dimensional printing; Turning (machining); 기계공학; Machining process; 4th industrial revolution; Machining industry; Artificial intelligence; Machine learning
• ISSN: 2288-6206; 2198-0810
• DOI: 10.1007/s40684-018-0057-y

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.