Fault Classification of Face Milling Tool Using Vibration Signals and Histogram Features – A Machine Learning Approach
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SAE International
Reference16 articles.
1. Ambhore, N., Kamble, D., Chinchanikar, S., and Wayal, V. , “Tool Condition Monitoring System: A Review,” Mater. Today Proc. 2, no. 4–5 (2015): 3419-3428, doi:10.1016/j.matpr.2015.07.317.
2. Nath, C. , “Integrated Tool Condition Monitoring Systems and Their Applications: A Comprehensive Review,” Procedia Manuf. 48 (2020): 852-863, doi:10.1016/j.promfg.2020.05.123.
3. Madhusudana, C.K., Kumar, H., and Narendranath, S. , “Condition Monitoring of Face Milling Tool Using K-Star Algorithm and Histogram Features of Vibration Signal,” Eng. Sci. Technol. an Int. J. 19, no. 3 (2016): 1543-1551, doi:10.1016/j.jestch.2016.05.009.
4. Aghazadeh, F., Tahan, A., and Thomas, M. , “Tool Condition Monitoring Using Spectral Subtraction and Convolutional Neural Networks in Milling Process,” Int. J. Adv. Manuf. Technol. 98, no. 9–12 (2018): 3217-3227, doi:10.1007/s00170-018-2420-0.
5. Durairaj, P.K. and Vaithiyanathan, M. , “Tool Condition Monitoring in Face Milling Process Using Decision Tree and Statistical Features of Vibration Signal,” SAE Technical Paper 2019-28-0142 (2019), https://doi.org/10.4271/2019-28-0142.
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