Prediction of turned surface roughness based on GADF of multi-channel signal fusion and MA-ResNet

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) was proposed. Firstly, the multi-channel vibration signals were subdivided into various frequency bands using wavelet packet decomposition, and the sensitive channels were selected for signal fusion by doing correlation analysis between the signals of various frequency bands and the surface roughness. Then the fused signals were converted into pictures using GADF image encoding. Finally, the pictures were 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 proposed method has a root mean square error of 0.018 7, a mean absolute error of 0.014 3, and a coefficient of determination of 0.869 4 in predicting the surface roughness, which is close to the actual value. Therefore, the proposed method had good engineering significance for high-precision prediction and was conducive to on-line monitoring of surface quality during workpiece processing.

Keywords

signal fusion / Gramian angular difference field / dilated convolution / residual network / roughness prediction

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