A visual measurement method of grinding surface roughness based on aliasing region index and neural network

Abstract

Most existing vision-based roughness measurements primarily rely on statistical information from grayscale images or intensity information from color images. However, the structural information of images has not been fully and effectively utilized. To more accurately measure the roughness of grinding surfaces, a visual measurement method of grinding surface roughness based on aliasing region index and neural network is proposed. Firstly, color images of grinding surface are obtained under red and green illumination. Secondly, aliasing regions of red and green images are extracted through fuzzy clustering segmentation and morphological processing. Then the aliasing width and the aliasing dispersion of aliasing region can be calculated as indices for roughness measurement. Thirdly, the relationship model between aliasing region index and grinding surface roughness is constructed using the back propagation (BP) neural network. The results demonstrate that the aliasing dispersion index has a better correlation with grinding surface roughness than the aliasing width index. The method based on the aliasing dispersion index and BP neural network is feasible and accurate for grinding surface roughness measurement.