关键词: 磨削, 表面粗糙度, 卷积神经网络, 正交试验

Abstract: A grinding surface roughness measurement method was proposed based on SA-CNN for convenient and accurate prediction of roughness values on screw rotor surfaces after grinding. Through orthogonal experiments, the surface roughness values of screw rotors and corresponding surface images were obtained. After preprocessing including adaptive histogram equalization and unsharp masking, the images were used as training samples input into the SA-CNN model. The SA-CNN model was employed to predict the roughness values on the grinding surfaces of screw rotors and compared with the predictions of classical networks such as ResNet, AlexNet, VGG-16, basic CNN, and graph neural network (GNN). Experimental results show that the SA-CNN model achieves an average prediction accuracy of 95.24%, with an RMSE of 0.0706 μm and an MAPE of 7.4206%, outperforming the compared networks. Furthermore, the SA-CNN model exhibits fast convergence, high accuracy, and good robustness.

Key words: grinding, surface roughness, convolutional neural network(CNN), orthogonal experiment