Fracture Characteristics and Analysis in Dissimilar Cu-Al Alloy Joints Formed via Electromagnetic Pulse Welding

The aim of this study was to identify and analyze the fatigue fracture characteristics of dissimilar Al 6061 to Cu (UNS C11000) lap joints made with ultrafast electromagnetic pulse welding (EMPW) via fractography, stress analysis and finite element simulation. It was observed that EMPW generated an annular (or ring-shaped) bonding area, with weld zones and a central non-weld zone when viewed from the cross section. Two types of failure modes occurred in relation to the cyclic loading levels: base metal fracture or transverse through-thickness (TTT) crack growth at a higher loading level, and joint interfacial failure at a lower loading level. In the interfacial failure, fatigue crack initiated from the outer edge of annular welding area, and propagated to form an approximate elliptical boundary. Fatigue crack propagation was characterized by fatigue striations existing in discrete areas on the fracture surface. This was attributed to a coupled role of shear and normal stresses present in a tensile lap shear sample due to the bending moment caused by the inherent misalignment. The final rapid fracture started from elliptical boundary with elongated shear dimples. Both theoretical stress analysis and finite element model revealed the maximum stress and stress concentration along the outer edge, where fatigue crack initiation occurred.

Obstacle Filtering Algorithm for Control of an Autonomous Road Vehicle in Public Highway Traffic

This paper presents an obstacle filtering algorithm that mimics human driver-like grouping of objects within a model predictive control scheme for an autonomous road vehicle. In the algorithm, a time to collision criteria is first used as risk assessment indicator to filter the potentially dangerous obstacle object vehicles in the proximity of the autonomously controlled vehicle. Then, the filtered object vehicles with overlapping elliptical collision areas put into groups. A hyper elliptical boundary is regenerated to define an extended collision area for the group. To minimize conservatism, the parameters for the tightest hyper ellipse are determined by solving an optimization problem. By excluding undesired local minimums for the planning problem, the grouping alleviates limitations that arise from the limited prediction horizons used in the model predictive control. The computational details of the proposed algorithm as well as its performance are illustrated using simulations of an autonomously controlled vehicle in public highway traffic scenarios involving multiple other vehicles.