A reduced-order machine-learning-based method for fault recognition in tool condition monitoring-Reference-Cited by-同舟云学术

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A reduced-order machine-learning-based method for fault recognition in tool condition monitoring

Published:2024-01 Issue: Volume:224 Page:113906
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Isavand Javad^ORCID,Kasaei Afshar^ORCID,Peplow Andrew^ORCID,Wang Xiaofeng,Yan Jihong

Funder

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference51 articles.

1. Tran Minh-Quang, Doan Hoang-Phuong, Q. Viet, T. Lien, Machine learning and IoT-based approach for tool condition monitoring: A review and future prospects, measurement, 2023.

2. Danil Yu. Pimenov, Munish Kumar Gupta, Leonardo R.R. da Silva, Maitri Kiran, Navneet Khanna, Grzegorz M. Krolczyk, Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach, Measurement, Vol. 199, 2022, 111503, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2022.111503.

3. D. Chuchala, M. Dobrzynski, D.Y. Pimenov, K.A. Orlowski, G. Krolczyk, K. Giasin, Surface roughness evaluation in thin en aw-6086-t6 alloy plates after face milling process with different strategies, Materials 14 (2021) 3036, https://doi.org/ 10.3390/ma14113036.

4. M. Kuntoglu, Measurement and analysis of sustainable indicators in machining of Armox 500T armor steel, in: Proceedings of the Institution of Mechanical Engineers, Part C: J. Mech. Eng. Sci. (2022) 095440622210797. https://doi.org/10.1177/09544062221079775.

5. N. Ambhore, D. Kamble, S. Chinchanikar, V. Wayal, Tool condition monitoring system: a review, Mater. Today:. Proc. 2 (4) (2015) 3419–3428, https://doi.org/10.1016/j.matpr.2015.07.317.

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3. An In-Depth Study of Vibration Sensors for Condition Monitoring;Sensors;2024-01-23

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