A defect detection method for plastic gears based on deep learning and machine vision

For the detection of plastic gears, most factories still use manual method with measurement tools. Therefore, the efforts expended in their defect detection are tremendous in the production processes. This paper proposes a new method that detects defection for plastic gears during their production and recycling processes. An image dataset of different kind of plastic gears was created. Then, a defect detection DL model was proposed based on GoogLeNet; it detected whether the plastic gears have missing teeth (MT), edge fin (EF), or good quality (GQ). An independent dataset was created to test the DL model: the accuracy of this model reached 94.8%. Combined with MV and DL methods, this paper realizes the automatic detection of plastic gear defects. Based on the independent plastic gear data set, the effect of defect detection method is verified by experiments. The results have important theoretical value and practical significance for liberating manpower and promoting the automatic process of plastic gear defect detection.