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

Viewed

Total article views: 341 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
278 46 17 341 16 28
  • HTML: 278
  • PDF: 46
  • XML: 17
  • Total: 341
  • BibTeX: 16
  • EndNote: 28
Views and downloads (calculated since 22 Oct 2025)
Cumulative views and downloads (calculated since 22 Oct 2025)

Viewed (geographical distribution)

Total article views: 337 (including HTML, PDF, and XML) Thereof 337 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 03 Dec 2025
Download
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.
Read more
Share