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In today’s cost-competitive automotive market, use of finite element simulations and optimization tools has become crucial to deliver durable and reliable products. Simulation driven design is the key to reduce number of physical prototypes, design iterations, cost and time to market. However, simulation driven design optimization tools have struggled to find global acceptance and are typically underutilized in many applications; especially in situations where the algorithms have to compete with existing know-how decision making processes. In this study, systematic multi-phase approach for optimization driven design is presented. Approach includes three optimization phases. In first phase, topology optimization is performed on concept BIW design volume to identify critical load paths. Architectural inputs from topology are used to design base CAD.

In today’s cost competitive environment, automotive companies are moving towards lightweight materials for reducing carbon footprint, increasing fuel economy and cost benefits. Fiber reinforced plastics (FRP) is one of the most attractive option considering its high strength to weight ratio. The advantage of continuous FRP composites is tailorability according to different performance requirements. This paper will focus on finite element analysis and optimization of automotive hood structure made up of continuous carbon fiber reinforced composite with epoxy resin based matrix. Composite hood structure is analyzed using detailed orthotropic composite laminate models and an appropriate composite material failure theory. Strength of FRPs is maneuvered by orientations of the fiber plies. Considering this, stack-up sequence optimization is performed considering bending, torsional stiffness and fundamental modes in dynamic analysis.

Stringent emission norms by government and higher fuel economy targets have urged automotive companies to look beyond conventional methods of optimization to achieve an optimal design with minimum mass, which also meets the desired level of performance targets at the system as well as at vehicle level. In conventional optimization method, experts from each domain work independently to improve the performance based on their domain knowledge which may not lead to optimum design considering the performance parameters of all domain. It is time consuming and tedious process as it is an iterative method. Also, it fails to highlight the conflicting design solutions. With an increase in computational power, automotive companies are now adopting Multi-Disciplinary Optimization (MDO) approach which is capable of handling heterogeneous domains in parallel. It facilitates to understand the limitations of performances of all domains to achieve good balance between them.

Designing a vehicle chassis involves meeting numerous performance requirements related to various domains such as Durability, Crashworthiness and Noise-Vibration-Harshness (NVH) as well as reducing the overall weight of chassis. In conventional Computer Aided Engineering (CAE) process, experts from each domain work independently to improve the design based on their own domain knowledge which may result in sub-optimal or even non-acceptable designs for other domains. In addition, this may lead to increase in weight of chassis and also result in stretching the overall product development time and cost. Use of Multi-Disciplinary Optimization (MDO) approach to tackle these kind of problems is well documented in industry. However, how to effectively formulate an MDO study and how different MDO formulations affect results has not been touched upon in depth.