Research on Crack Detection Method of Self-Piercing Riveting

Compared with traditional welding, self-piercing riveting technology has unique advantages and is widely used in automobile lightweight technology. The riveting quality of self-piercing riveting is closely related to the safety and durability of automobiles. The detection of riveting quality has gradually become an important part of the automobile manufacturing process. The generation of surface cracks under self-piercing riveting will affect the riveting strength, which in turn affects the riveting quality. Therefore, the detection of riveting external quality is transformed into the detection of riveting surface cracks. The existing artificial vision-based riveting lower surface crack recognition technology is inefficient, subjective and cannot be applied on a large scale. Therefore, this paper will propose a local-overall strategy based on image processing and computer vision. Firstly, three sub-image crack recognition networks based on extreme learning machine and feature extraction are constructed. Considering that the crack recognition network based on feature extraction has a large room for improvement in accuracy and the limitation of feature description operator on image expression, two sub-image crack recognition networks based on convolutional neural network are constructed. Then based on the traversal search algorithm, four representative full-size images are used to show the detection effect of different crack recognition models. The final results show that the crack recognition network based on convolutional neural network has the best detection effect.