Tool wear monitoring based on an improved convolutional neural network-Reference-Cited by-同舟云学术

Tool wear monitoring based on an improved convolutional neural network

Published:2023-04 Issue:4 Volume:37 Page:1949-1958
ISSN:1738-494X
Container-title:Journal of Mechanical Science and Technology
language:en
Short-container-title:J Mech Sci Technol

Author:

Zhao Jia-Wei,Guo Shi-Jie,Ma Lin,Kong Hao-Qiang,Zhang Nan

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Link

https://link.springer.com/content/pdf/10.1007/s12206-023-0332-x.pdf

Reference30 articles.

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2. Y. Cheng, J. Yang, D. Zuo, X. Song and X. Feng, Tool design and cutting parameters optimization for plunge milling blisk, International Journal of Manufacturing Research, 15 (3) (2020) 266–284.

3. R. A. Patil and S. L. Gombi, Experimental study of cutting force on a cutting tool during machining using inverse problem analysis, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40 (10) (2018) 494.

4. T. S. Liu, Y. Y. Liu and K. D. Zhang, An improved cutting force model in micro-milling considering the comprehensive effect of tool runout, size effect, and tool wear, The International Journal of Advanced Manufacturing Technology (2022) 659–668.

5. K. Gomathi and A. Balaji, Tool condition monitoring of PCB milling machine based on vibration analysis, Materials Today: Proceedings, 45 (P2) (2021) 3386–3397.

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