Strength modeling of Al-alloy sheet self-piercing riveting considering different failure modes

Abstract

Self-piercing riveting (SPR) has been widely utilized to connect metal components in industry, and the mechanical properties of final product depend on the strength of SPR joint which is experimentally measured through cross-tension and lap-shear tests. These tests are destructive and the tested strength of specimen is not directly related to the actual strength of the SPR product. In this study, the SPR process of aluminum alloy sheet was investigated and the general empirical model of SPR strength was established by comprehensively considering the factors including of the geometric dimensions of components and die, the material properties of rivet and sheet, and the load -stroke curve of punch. The calculated strength values of 4 group of SPR specimens were verified by the experimentally measured results. All 2 / 54 calculation errors are lower than 8%. An industrial internet of things (IIoT) was developed to automatically realize the data transmission and strength calculation of the SPR process.

Multi-indicator optimization of riveting joint forming quality of aluminum alloy sheets based on response surface test

“…Xu Fan [5] et al analyzed the factors affecting the strength of riveted joints and obtained the influence law of the number of joints, different tensile speeds and different combinations on the strength of the joints; Shen Yongfei [6] et al through the tensile shear experiments on joints of different riveted specimens, combined with the mechanical properties of the unriveted riveted joints, came to the conclusion that the interfacial breakage and the joints tensile shear strength depended on the thickness of the cracking of the neck of the upper plate; Benzegaou Ali [7] et al obtained the geometrical mechanical parameters affecting the quality of riveted joints during riveting and performed tensile tests to obtain the mechanical strength of riveted joints. Liu Yahui [8] et al investigated the metal deformation behavior during self-pierce riveting of thin aluminum alloy plates and the mechanical behavior of self-pierce riveting joint head in destructive testing, with a view to establishing a general strength model for self-pierce riveting joint head and realizing the data processing and strength prediction of riveted joints. Liu Yang [9] et al proposed a deep learning-based prediction method for riveting process, obtaining the original images of the model dataset according to the simulation results, and using image segmentation technology to classify the damage patterns.…”