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Journal Article

PSO-Based Multidisciplinary Design Optimization of Automotive Assemblies

Abstract Widely used in automotive industry, lightweight metallic structures are a key contributor to fuel efficiency and reduced emissions of vehicles. Lightweight structures are traditionally designed through employing the material distribution techniques sequentially. This approach often leads to non-optimal designs due to constricting the design space in each step of the design procedure. The current study presents a novel Multidisciplinary Design Optimization (MDO) framework developed to address this issue. Topology, topography, and gauge optimization techniques are employed in the development of design modules and Particle Swarm Optimization (PSO) algorithm is linked to the MDO framework to ensure efficient searching in large design spaces often encountered in automotive applications. The developed framework is then further tailored to the design of an automotive Cross-Car Beam (CCB) assembly.
Technical Paper

A Novel Approach for Design and Optimization of Automotive Aluminum Cross-Car Beam Assemblies

Nowadays, moving toward more lightweight designs is the key goal of all major automotive industries, and they are always looking for more mass saving replacements. In this study, a new methodology for the design and optimization of cross-car beam (CCB) assemblies is proposed to obtain a more lightweight aluminum design as a substitution for the steel counterpart considering targeted performances. For this purpose, first, topology optimization on a solid aluminum geometry encompassing the entire design space should be carried out to obtain the element density distribution within the model. Reinforcing locations with high element density and eliminating those with density lower than the threshold value result in the conceptual design of the CCB. To attain the final conceptual design, the process of topology optimization and removal of unnecessary elements should be addressed in several steps.