An in-process tool wear assessment using Bayesian optimized machine learning algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Modeling and Simulation
Link
https://link.springer.com/content/pdf/10.1007/s12008-023-01270-3.pdf
Reference46 articles.
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2. Jaffery, S. H., Khan, M., Sheikh, N. A., Mativenga, P.: Wear mechanism analysis in milling of Ti-6Al-4V alloy. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 227(8), 1148–1156 (2013).
3. Chen, B., Chen, X., Li, B., He, Z., Cao, H., Cai, G.: Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mech. Syst. Signal Process. 25(7), 2526–2537 (2011)
4. Diaz-Rozo, J., Bielza, C., Larrañaga, P.: Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering. Eng. Appl. Artif. Intell. 89(December 2019), 103434 (2020).
5. Mikolajczyk, T., Paczkowski, T., Kuntoglu, M., Patange, A. D., Binali, R.: Research on Using an unconventional tool for increasing tool life by selective exchange of Worn Cutting Edge. Appl. Sci. 13(1), 460 (2023). https://doi.org/10.3390/app13010460.
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Configuration of tool wear and its mechanism in sustainable machining of titanium alloys with energy signals;The International Journal of Advanced Manufacturing Technology;2024-09-05
2. RETRACTED: An acoustic imaging recognition based cutting tools wear state prediction method;Journal of Intelligent & Fuzzy Systems;2024-04-08
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