An approach for surface roughness measurement of helical gears based on image segmentation of region of interest

Abstract

Existing roughness measurement approaches based on machine vision cannot accurately measure irregular components with complex shapes, such as helical gears. Owing to the occlusion of relative positions between teeth, it is not possible to directly obtain an image that only contains the target surface, which decreases the accuracy and efficiency of the measurement model. This paper proposes a novel visual approach for the roughness measurement of helical gears. First, a region of interest (ROI) extraction method is designed to filter the interference information in the original image and extract the effective region. Then, a convolutional neural network (CNN) is applied to evaluate the roughness with the ROI processed image as input. The machine vision-based roughness values calculated before and after ROI extraction are compared with the stylus device-based roughness values. The accuracy and generality of the proposed approach are proved by two cases of helical gear and leadscrew roughness measurements.