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

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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.
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