Articles | Volume 16, issue 1
https://doi.org/10.5194/ms-16-167-2025
https://doi.org/10.5194/ms-16-167-2025
Research article
 | 
25 Mar 2025
Research article |  | 25 Mar 2025

Research on online monitoring of chatter based on continuous wavelet transform and convolutional neural network–vision transformer (CNN-ViT)

Changfu Liu, Daohai Wang, Yuru Lin, and Shuyuan Song

Related subject area

Subject: Machining and Manufacturing Processes | Techniques and Approaches: Optimization
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Cited articles

Dun, Y. C., Zhu, L. D., Yan, B. L., and Wang, S. H.: A chatter detection method in milling of thin-walled TC4 alloy workpiece based on auto-encoding and hybrid clustering, Mech. Syst. Signal Pr., 158, https://doi.org/10.1016/j.ymssp.2021.107755, 2021. 
Fu, Y., Zhang, Y., Qiao, H., Li, D., Zhou, H., and Leopold, J.: Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief Networks, Proc. CIRP, 31, 29–34, https://doi.org/10.1016/j.procir.2015.03.016, 2015. 
Fu, Y., Zhang, Y., Gao, Y., Gao, H., Mao, T., Zhou, H. M., and Li, D. Q.: Machining vibration states monitoring based on image representation using convolutional neural networks, Eng. Appl. Artif. Intel., 65, 240–251, https://doi.org/10.1016/j.engappai.2017.07.024, 2017. 
Huang, P. L., Li, J. F., Sun, J., and Ge, M. J.: Milling force vibration analysis in high-speed-milling titanium alloy using variable pitch angle mill, Int. J. Adv. Manuf. Tech., 58, 153–160, https://doi.org/10.1007/s00170-011-3380-9, 2012. 
Ji, Y. J., Wang, X. B., Liu, Z. B., Wang, H. J., Jiao, L., Wang, D. Q., and Leng, S. Y.: Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation, J. Sound Vib., 433, 138–159, https://doi.org/10.1016/j.jsv.2018.07.019, 2018. 
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Short summary
To solve the flutter identification problem in the machining process of thin-walled parts, an online monitoring method based on continuous wavelet transform (CWT) and convolutional neural network–vision transformer (CNN-ViT) is proposed. First, the CNN model is utilized to extract local features from the image obtained after CWT. Then, the self-attention mechanism of the ViT model is employed to capture the global features of the feature map, and, finally, the classification results are output.
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