Tool state prediction model of Tent-ASO-BP neural network based on multi-feature fusion
Author:
Affiliation:
1. College of Mechanical and Power Engineering, China Three Gorges University
2. College of Innovation and Entrepreneurship, China Three Gorges University
Publisher
Japan Society of Mechanical Engineers
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Link
https://www.jstage.jst.go.jp/article/jamdsm/17/6/17_23jamdsm0082/_pdf
Reference28 articles.
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3. Cai, Y., Starly, B., Cohen, P. and Lee, Y-S., Sensor Data and Information Fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing, Procedia Manufacturing, Vol. 10 (2017), pp. 1031-1042.
4. Cheng, C., Li, J. Y., Xu, W. S. and Nie, M., Tool wear state recognition based on support vector machine and particle filter, Vibration and shock, Vol. 37, No. 17 (2018), pp. 48-55 (in Chinese).
5. Daisuke, K., Yasuhiro, K. and Seiichiro, K., Cutting force control applying sensorless cutting force monitoring method, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol. 4, No. 5 (2010), pp. 955-965.
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