Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM-Reference-Cited by-同舟云学术

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Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM

Published:2023-06-23 Issue: Volume: Page:
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Li Bohao,Lu Zhenghui,Jin Xiaoliang^ORCID,Zhao Liping

Funder

Natural Sciences and Engineering Research Council of Canada

Canada Research Chairs

Innovative Leading Talents Scholarship of Xi'an Jiaotong University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10845-023-02164-7.pdf

Reference39 articles.

1. Abhishek, K., Rakesh Kumar, V., Datta, S., & Mahapatra, S. S. (2017). Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm). Journal of Intelligent Manufacturing, 28(8), 1769–1785. https://doi.org/10.1007/s10845-015-1050-8

2. Altintas, Y. (2012). Manufacturing automation: Metal cutting mechanics, machine tool vibrations, and CNC design (2nd ed.). Cambridge University Press.

3. Ashworth, S., Fairclough, J. P. A., Meredith, J., Takikawa, Y., & Kerrigan, K. (2022). Effects of tool coating and tool wear on the surface quality and flexural strength of slotted CFRP. Wear, 498, 204340. https://doi.org/10.1016/j.wear.2022.204340

4. Azmi, A. I., Lin, R. J. T., & Bhattacharyya, D. (2013). Tool wear prediction models during end milling of glass fibre-reinforced polymer composites. The International Journal of Advanced Manufacturing Technology, 67(1), 701–718. https://doi.org/10.1007/s00170-012-4516-2

5. Bai, Y., Wang, F., Fu, R., Hao, J., Si, L., Zhang, B., Lei, W., & Davim, J. P. (2021). A semi-analytical model for predicting tool wear progression in drilling CFRP. Wear, 486, 204119. https://doi.org/10.1016/j.wear.2021.204119

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