Abstract
In recent years, the automotive industries have been driven by the ever-growing demands for weight reduction of automobiles to meet the environmental concerns regarding carbon dioxide emissions and fuel consumption. The need for weight reduction has led to the use of a wide range of high strength steel and aluminum alloy grades or even a mix of them. However, the use of the non-ferrous alloys constrains the automotive industries to reconsider their design approach as well as their joining techniques. Self-piercing riveting (SPR) has been indicated as a reliable technique to join similar or dissimilar material combinations, where the use of resistance spot welding may be difficult or even impossible [1]. Durability assessment of automotive structures joined by SPR and subjected to fatigue loading is an important part of automotive development. As a result, an accurate fatigue lifetime analysis of SPR joints is asked for by automotive engineers, especially as part of the computer-aided engineering calculations in the early stage of model development. In the present work, an approach based on a structural stress method, widely used for spot welded joints, was adapted to generate master curves for fatigue-lifetime prediction of SPR joints. For this purpose, experimental fatigue tests under specific tensile orientations were first carried out on single-cross-joints to obtain structural stress–fatigue lifetime (S-N) curves (raw data) using Lock’in thermography technique for local analyses [2]. These specimens were then simulated by using the finite element method (FEM) coupled with fastener approach and the local structural stresses were next theoretically calculated for an applied load of unity. The S-N curve corresponding to the applied fatigue test loads was finally obtained for these SPR joints. The results showed that all data points condensed into two master curves and the predicted fatigue lifetime was well correlated with the experimental one. In the light of the obtained results, the proposed approach can be used to predict the fatigue lifetime of the SPR joints. Session
