Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 15, issue 2
Articles | Volume 15, issue 2
https://doi.org/10.5194/ms-15-567-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-15-567-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 2
Research article
 | 
10 Oct 2024
Research article |  | 10 Oct 2024

Classification of drilling surface roughness on computer numerical control (CNC) machine tools based on Mobilenet_v3_small_improved

Gang Chen, Wang Peng, Jiajun Tu, Wenyu Wang, and Haijun Zhao

Viewed

Total article views: 576 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
295 40 241 576 17 17
  • HTML: 295
  • PDF: 40
  • XML: 241
  • Total: 576
  • BibTeX: 17
  • EndNote: 17
Views and downloads (calculated since 10 Oct 2024)
Cumulative views and downloads (calculated since 10 Oct 2024)

Viewed (geographical distribution)

Total article views: 564 (including HTML, PDF, and XML) Thereof 564 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Apr 2025
Download
Short summary
A prediction model for surface roughness classification of computer numerical control drills based on fusion of local time–frequency features and global time–frequency features of the Mobilenet_v3_small model is proposed. Correct rates of the training set, validation set, and test set are 85.2%, 84%, and 85.4%. Compared with industrial lightweight network models, this model improved the correctness rates on the training set, validation set, and test set by about 10%, 9%, and 13%, respectively.
Read more
Share