Improvement in predictive capability of smalloverlap crash simulation with emphasis on GISSMO material model, weld rupture and detailed modeling

CAE tools are one of the best techniques in the auto industry to drive design and help product development with minimal physical tests. Physical tests are very time consuming and expensive which is driving the Auto industry towards virtual simulations to replace physical tests. CAE has become an integral part of product development to accurately predict physical testing and drive design direction. For CAE to accurately predict the physical test, it depends on details captured in the full vehicle model. In the small overlap load case it’s necessary to capture as much detail as possible for components engaged during the impact event. However, capturing too much detail leads to prohibitively large models with excessive computational time. So it is important to understand the load path to decide the critical vehicle components which play a vital role in the crash event. This includes the sheet steel/aluminum stamped parts, aluminum extrusion and also the fasteners and welds. In this paper an attempt is made to revisit the modeling of these critical vehicle components and later confirm the performance with respect to the physical test. The sheet steel/aluminum stamped parts and also the aluminum extrusions are finely meshed and GISSMO material models are implemented to define their rupture. The fasteners (bolts) are modeled using solid elements. Spot welds are modeled as solid nuggets with damage material model MAT_SPOTWELD_DIAMLERCHRYSLER and a simple elegant technique is used to define the aluminum MIG welds. The MIG welds are joining thick Aluminum parts in the cradle. MIG welds are represented by discrete beams with MAT119 material model. The stiffness, loads and rupture displacement parameters are adjusted to component tests and an envelope of rupture is created. This is carried on to the full vehicle as a predictive model and the designs are iterated. All of the above modeling methods and techniques helped to accurately predict velocities, intrusion, wheel kinematics and a good correlation to the physical test was achieved.