The quality of commutator surfaces in DC motors significantly affects the performance and longevity of the motors. Traditional methods of inspecting commutator surface defects, such as roundness and roughness meters, have limitations in detecting subtle and complex surface irregularities. This study proposes an image analysis technique combined with convolutional neural networks (CNNs) to enhance the detection of commutator surface defects. Our method improves the identification and classification of defects, correlating these findings with the assembly quality of DC motors. Although the experimental results are premilitary, it validates the effectiveness of the proposed approach, demonstrating improvements in defect detection accuracy. Future work will focus on expanding the image dataset and refining the CNN model to enhance its accuracy and real-time application capabilities.

defect detection, DC motor quality control, surface defects, correlation table, convolutional neural networks (CNNs)