Reinforcement Learning-Based Cutting Parameter Dynamic Decision Method Considering Tool Wear for a Turning Machining Process
Author:
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40684-023-00582-9.pdf
Reference36 articles.
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2. Tesic, S., Cica, D., Borojevic, S., Sredanovic, B., Zeljkovic, M., Kramar, D., & Pusavec, F. (2022). Optimization and prediction of specific energy consumption in ball-end milling of Ti–6Al–4V alloy under MQL and cryogenic cooling/lubrication conditions. International Journal of Precision Engineering and Manufacturing-Green Technology, 9(6), 1427–1437.
3. International Energy Agency (IEA). (2019). Key energy statistics 2018. https://www.iea.org/countries/china. Retrieved 19 Sep 2018.
4. Binali, R., Patange, A. D., Kuntoğlu, M., Mikolajczyk, T., & Salur, E. (2022). Energy saving by parametric optimization and advanced lubri-cooling techniques in the machining of composites and superalloys: A systematic review. Energies, 15(21), 8313.
5. Xiao, Q., Li, C., Tang, Y., & Li, L. (2021). Meta-reinforcement learning of machining parameters for energy-efficient process control of flexible turning operations. IEEE Transaction on Automation Science and Engineering, 18(1), 5–18.
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