A novel tool life model for varying process conditions and cutting volumes using cutting power consumption
Author:
Funder
National Research Foundation of Korea
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00170-024-13856-6.pdf
Reference27 articles.
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2. Dai W, Liang K, Huang T, Lu Z (2022) Tool condition monitoring in the milling process based on multisource pattern recognition model. Int J Adv Manuf Technol 119:2099–2114. https://doi.org/10.1007/s00170-021-08012-3
3. Sayyad S, Kumar S, Bongale A, Kotecha K, Selvachandran G, Suganthan PN (2022) Tool wear prediction using long short-term memory variants and hybrid feature selection techniques. Int J Adv Manuf Technol 121:6611–6633. https://doi.org/10.1007/s00170-022-09784-y
4. Zhang X, Gao Y, Guo Z, Zhang W, Yin J, Zhao W (2023) Physical model-based tool wear and breakage monitoring in milling process. Mech Syst Signal Process 184:109641. https://doi.org/10.1016/j.ymssp.2022.109641
5. Li Y, Zheng G, Zhang X, Cheng X, Yang X, Xu R (2019) Cutting force, tool wear and surface roughness in high-speed milling of high-strength steel with coated tools. J Mech Sci Technol 33:5393–5398. https://doi.org/10.1007/s12206-019-1033-3
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