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Multisensor-based tool wear diagnosis using 1D-CNN and DGCCA

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Abstract

Bad conditions during machining cause tool chatter, wear, or breakage, which affect the tool life and consequently the surface quality and dimensional accuracy of the machined workpiece. Therefore, for the purposes of production efficiency and economics, monitoring and diagnostics of the tool’s condition are important. This study presents a one-dimensional convolutional neural network (1D-CNN) and deep generalized canonical correlation analysis (DGCCA) for multiple sensors-based tool wear diagnosis. In particular, 1D-CNN is used to extract features from 1D raw data, such as force, vibration, and sound, whereas DGCCA with attention mechanism is used to fuse the feature output from each 1D-CNN by removing irrelevant or redundant information. Experiments are performed using PHM2010 and NASA data sets. The experimental results show that our proposed approach can achieve satisfactory accuracy of 95.6% and near real-time performance. Results of our study can be implemented in real tool wear diagnosis, and thus identify novel opportunities toward realizing Industry 4.0.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 51875429), the General Program of Shenzhen Natural Science Foundation (Grant No. JCYJ20190809142805521) and Wenzhou Major Program of Scientific and Technological Innovation (Grant No. ZG2021021).

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Authors and Affiliations

  1. School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Yong Yin & Jian Zhou

  2. Shenzhen Research Institute of Wuhan University of Technology, Shenzhen, 518000, China

    Yong Yin

  3. Wenzhou University of Technology, Wenzhou, 325035, China

    Shuxin Wang

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  1. Yong Yin
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  2. Shuxin Wang
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  3. Jian Zhou
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Correspondence to Shuxin Wang.

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Yin, Y., Wang, S. & Zhou, J. Multisensor-based tool wear diagnosis using 1D-CNN and DGCCA. Appl Intell 53, 4448–4461 (2023). https://doi.org/10.1007/s10489-022-03773-0

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