Crack Detection and Section Quality Optimization of Self-Piercing Riveting

The use of lightweight materials is one of the important means to reduce the quality of the vehicle, which involves the connection of dissimilar materials, such as the combination of lightweight materials and traditional steel materials. The riveting quality of self-piercing riveting (SPR) technology will directly affect the safety and durability of automobiles. Therefore, in the initial joint development process, the quality of self-piercing riveting should be inspected and classified to meet safety standards. Based on this, this paper divides the self-piercing riveting quality into riveting appearance quality and riveting section quality. Aiming at the appearance quality of riveting, the generation of cracks on the lower surface of riveting will seriously affect the riveting strength. The existing method of identifying cracks on the lower surface of riveting based on artificial vision has strong subjectivity, low efficiency and cannot be applied on a large scale. Therefore, based on image processing and computer vision, this paper proposes an automatic identification method of surface cracks under self-piercing riveting based on convolutional neural network (CNN) and local-global strategy. Aiming at the quality of riveting section, the riveting process and section quality are analyzed by numerical simulation, and a multi-objective optimization method is proposed to assist in improving the quality of riveting section.