Predicting tool wear size across multi-cutting conditions using advanced machine learning techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-020-01625-7.pdf
Reference28 articles.
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3. Geramifard, O., Xu, J. X., Zhou, J. H., & Li, X. (2011). Continuous health condition monitoring: A single hidden semi-Markov model approach. In IEEE Conference on Prognostics and Health Management (PHM) (pp. 1–10). IEEE.
4. Geramifard, O., Xu, J. X., Zhou, J. H., & Li, X. (2012). A physically segmented hidden Markov model approach for continuous tool condition monitoring: Diagnostics and prognostics. IEEE Transactions on Industrial Informatics, 8(4), 964–973.
5. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.
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