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

Author:

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-HoonORCID

Publisher

Springer Science and Business Media LLC

Subject

Management of Technology and Innovation,Industrial and Manufacturing Engineering,Mechanical Engineering,General Materials Science,Renewable Energy, Sustainability and the Environment

Reference109 articles.

1. Shrouf, F., Ordieres, J., and Miragliotta, G., “Smart Factories in Industry 4.0: A Review of the Concept and of Energy Management Approached in Production Based on the Internet of Things Paradigm,” Proc. of IEEE International Conference on Industrial Engineering and Engineering Management, pp. 697–701, 2014.

2. Yan, J., Meng, Y., Lu, L., and Guo, C., “Big-Data-Driven Based Intelligent Prognostics Scheme in Industry 4.0 Environment,” Proc. of Prognostics and System Health Management Conference (PHMHarbin), pp. 1–5, 2017.

3. Coulter, R. and Pan, L., “Intelligent Agents Defending for an IoT World: A Review,” Computers & Security, Vol. 73, pp. 439–458, 2018.

4. TrendForce, “TrendForce Forecasts Size of Global Market for Smart Manufacturing Solutions to Top US$320 Billion by 2020; Product Development Favors Integrated Solutions,” https://doi.org/press.trendforce.com/press/20170731-2911.html (Accessed 8 AUG 2018)

5. Antony, P., Jnanesh, N., and Prajna, M., “Machine Learning Models for Material Selection: Framework for Predicting Flatwise Compressive Strength Using Ann,” Proc. of 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 424–427, 2016.

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