Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 16, issue 2
Articles | Volume 16, issue 2
https://doi.org/10.5194/ms-16-615-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-16-615-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Research article
 | 
22 Oct 2025
Research article |  | 22 Oct 2025

Depth-wise separable convolutional neural-network-based intelligent chatter monitoring for thin-walled polish grinding

Yuan Zhao, Chunxia Zhu, and Guofa Xu

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Short summary
To enhance chatter monitoring in polishing and grinding, we propose a model fusing deep separable convolutional neural networks (DCNN) and Gated Recurrent Unit. Signals are preprocessed via optimized variational mode decomposition (VMD) and wavelet denoising. The squeeze excitation deep separable convolutional neural networks gated recurrent units (SE-DCNN-GRU) model employs depth-wise convolution for multi-scale feature extraction. Experiments achieve 98.8 % accuracy for titanium alloy parts, offering a robust solution for complex conditions.
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