Visual measurement of grinding surface roughness based on feature fusion

Abstract Due to the random surface texture and weak features of the grinding processing, most current visual measurement of grinding surface roughness is predicted by designing feature indicators, but its imaging environment is more demanding and the indicator design is more artificial. While deep learning can achieve feature self-extraction, the existing deep learning-based feature extraction uses a single convolutional model to extract features, which tends to make the extracted features noise, with low resolution and poor perception of details. To address the above problems, this paper proposes a feature fusion-based method for measuring grinding surface roughness. The method adopts different feature extraction ways and fuses the extracted features to obtain more features while also improving the generalization ability of the model, and verifies the adaptability of the model in different lighting environments. The experimental results show that deep learning self-extracted features based on feature fusion can effectively solve the problem of weak feature information on grinding surface roughness that is difficult to identify, and the model has high detection accuracy across different lighting environments, thus laying the foundation for the automated visual online measurement of grinding surface roughness.