Efficient stability prediction of milling process with arbitrary tool-holder combinations based on transfer learning-Reference-Cited by-同舟云学术

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Efficient stability prediction of milling process with arbitrary tool-holder combinations based on transfer learning

Published:2022-02-14 Issue:5 Volume:34 Page:2263-2279
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Deng Congying,Tang Jielin,Miao Jianguo,Zhao Yang,Chen Xiang,Lu Sheng^ORCID

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation Funded Project

Chongqing Special Postdoctoral Science Foundation

Chongqing Graduate Scientific Research Innovation Project

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10845-022-01912-5.pdf

Reference46 articles.

1. Albertelli, P., Goletti, M., Torta, M., Salehi, M., & Monno, M. (2016). Model-based broadband estimation of cutting forces and tool vibration in milling through in-process indirect multiple-sensors measurements. The International Journal of Advanced Manufacturing Technology, 82, 779–796. https://doi.org/10.1007/s00170-015-7402-x

2. Altintaş, Y., & Budak, E. (1995). analytical prediction of stability lobes in milling. CIRP Annals, 44, 357–362. https://doi.org/10.1016/S0007-8506(07)62342-7

3. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1798–1828. https://doi.org/10.1109/TPAMI.2013.50

4. Bernico, M., Li, Y., & Zhang, D. (2019). Investigating the impact of data volume and domain similarity on transfer learning applications. Future Technologies Conference (FTC), 881, 53–62. https://doi.org/10.1007/978-3-030-02683-7_5

5. Bonyadi, M. R. (2020). A Theoretical guideline for designing an effective adaptive particle Swarm. IEEE Transactions on Evolutionary Computation, 24, 57–68. https://doi.org/10.1109/TEVC.2019.2906894

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