Prediction of turned surface roughness based on GADF of multi-channel signal fusion and MA-ResNet
Release time:2025-02-14
Hits:
- Journal:
- Journal of Measurement Science and Instrumentation
- Key Words:
- Signal fusion；Gramian angular difference field；Dilated convolution；Residual network； Roughness prediction
- Abstract:
- In order to achieve high precision online prediction of surface roughness during turning process and improve cutting quality,a prediction method of turned surface roughness based on Gramian angular difference field (GADF) of multi-channel signal fusion and Multi-scale Attention Residual Network (MA-ResNet) is proposed. Firstly, the multi-channel vibration signals are subdivided into various frequency bands using wavelet packet decomposition, and the sensitive channels are selected for signal fusion by doing correlation analysis between the signals of various frequency bands and the surface roughness; then the fused signals are converted into pictures using GADF image encoding; finally, the pictures are inputted into the residual network model combining the parallel dilation convolution and attention module for training and verifying the effectiveness of the model performance.The results show that the proposed method has a root mean square error (RMSE) of 0.0187, a mean absolute error (MAE) of 0.0143, and a coefficient of determination (R2) of 0.8694 in predicting the surface roughness, which is close to the actual value. Therefore, the method proposed in this paper has good engineering significance for high-precision prediction and is conducive to on-line monitoring of surface quality during workpiece processing.
- First Author:
- wanghaitao,刘腾飞
- Indexed by:
- Journal paper
- Correspondence Author:
- Lichen Shi
- Discipline:
- Engineering
- Document Type:
- J
- Translation or Not:
- no
- Date of Publication:
- 2024-01-01
- Links to published journals:
- https://link.cnki.net/urlid/14.1357.TH.20240513.0809.002