A grinding surface roughness class recognition combining red and green information

Abstract

The current machine vision-based surface roughness measurement mainly relies on the design of feature indicators associated with roughness to measure the surface roughness. However, the process is tedious and complicated. Moreover, most existing deep learning methods for workpiece surface roughness measurement use a monochromatic light source to acquire images. In the case of surface roughness in a grinding process with low roughness and random texture characteristics, the feature information obtained by monochromatic light source acquisition is relatively small. It is difficult to extract the workpiece surface roughness features, which can easily cause problems for subsequent measurement. Based on the problems above, this paper proposes a grinding surface roughness measurement method combining red-green information and a convolutional neural network. The technique uses a particular red-green block to highlight the grinding surface texture features. Finally, it classifies the grinding surface roughness measurement with a classification detection technique of the convolutional neural network. Experimental results show that the accuracy of the grinding surface roughness measurement method combining red-green information and the convolutional neural network is significantly improved compared with that of the grinding surface roughness measurement method without using the red-green data.