Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications
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/s12008-022-01050-5.pdf
Reference131 articles.
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2. Mohanraj, T., Shankar, S., Rajasekar, R., Sakthivel, N.R., Pramanik, A.: Tool condition monitoring techniques in milling process-a review. J. Mater. Res. Technol. 9(1), 1032–1042 (2020). https://doi.org/10.1016/j.jmrt.2019.10.031
3. Lister, P.M., Barrow, G.: Tool Condition Monitoring Systems. In: Davies B.J. (eds) Proceedings of the Twenty-Sixth International Machine Tool Design and Research Conference. Palgrave, London. (1986). https://doi.org/10.1007/978-1-349-08114-1_36
4. Atli, A.V., Urhan, O.Ä.U., Z. H. A. N., Ertürk, S.A.R.P., Sönmez, M.: A computer vision-based fast approach to drilling tool condition monitoring. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(9), 1409–1415. (2006). https://doi.org/10.1243/09544054JEM412
5. Nitin Ambhore, D., Kamble, S., Chinchanikar, V., Wayal: Tool Condition Monitoring System: A Review, Materials Today: Proceedings, Volume 2, Issues 4–5, Pages 3419–3428, (2015). https://doi.org/10.1016/j.matpr.2015.07.317
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