A new machine vision–based intelligent detection method for gear grinding burn

As a severe thermal damage, grinding burns reduce the surface quality and service life of high-performance gears. In this study, to realize accurate localization, classification, and grading of burn defects on gear grinding surfaces, a machine vision–based intelligent detection method was proposed. Different from image processing methods based on hand-designed features, the proposed method can minimize inspection conditions. To recognize individual gear functional surfaces and burn regions from random backgrounds, an image segmentation model was developed by using deep convolutional neural networks. For the scarcity of defect samples in industrial surface testing, a data-augmentation method combining deep learning methods with geometric and color-space transformations was proposed. The experimental results of the gear surface image dataset demonstrated that the proposed burn segmentation model has a precision and recall rate of 84.44% and 82.79%, respectively. This work provides fresh insights for realizing efficient and accurate detection of gear grinding burn defects.