A new tool wear monitoring method based on multi-scale PCA
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DOI: 10.1007/s10845-016-1235-9
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Cited by:
- Yanning Sun & Wei Qin & Zilong Zhuang & Hongwei Xu, 2021. "An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2007-2021, October.
- Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
- Joanna Kossakowska & Sebastian Bombiński & Krzysztof Ejsmont, 2021. "Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning," Energies, MDPI, vol. 14(20), pages 1-23, October.
- Wo Jae Lee & Gamini P. Mendis & Matthew J. Triebe & John W. Sutherland, 2020. "Monitoring of a machining process using kernel principal component analysis and kernel density estimation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1175-1189, June.
- Lucas Costa Brito & Márcio Bacci Silva & Marcus Antonio Viana Duarte, 2021. "Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 127-140, January.
- Yuqing Zhou & Bintao Sun & Weifang Sun & Zhi Lei, 2022. "Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 247-258, January.
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Keywords
Tool wear monitoring; Multi-scale principal component analysis; Milling process; Wavelet transformation;All these keywords.
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