CAE Driven Light Weighting of Automotive Hood Using Multiple Loadcase Optimization

In the automotive industry the requirement for low emissions has led to the demand for lightweight vehicle structures. Light weighting can be achieved through different iterative approaches but is usually time consuming. Current paper highlights deployment of the multi-loadcase optimization approach for light weighting. This work involves developing a process for multiple loadcase optimization for automotive hood. The main goal is to minimize the weight of a hood assembly by meeting strength and stiffness targets. The design variables considered in this study are thickness of the panels. Design constraints were set for stress and stiffness based on DVP (Design Verification Plan) requirement. Optimization workflow is setup in mode-frontier with design objective of minimizing weight of hood.

Based on the DOE (Design of Experiments) data response surfaces are generated using different algorithms for prediction of the structural performance parameters such as displacement, modal frequency and stress. Virtual optimization was performed using hybrid algorithm which combines a steady-state genetic algorithm with a sequential quadratic programming optimizer. Interaction effects were studied between input and output variables and critical panels which are driving the performances were identified. Trade off study was performed on output parameters and final optimized design was identified with weight saving of 1 kg.