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As conventional vehicle design is adjusted to suit the needs of all-electric, hybrid, and fuel-cell powered vehicles, designers are seeking new methods to improve system-level design and enhance structural efficiency; here, multi-material optimization is suggested as the leading method for developing these novel architectures. Currently, diverse materials such as composites, high strength steels, aluminum and magnesium are all considered candidates for advanced chassis and body structures. By utilizing various combinations and material arrangements, the application of multi-material design has helped designers achieve lightweighting targets while maintaining structural performance requirements. Unlike manual approaches, the multi-material topology optimization (MMTO) methodology and computational tool described in this paper demonstrates a practical approach to obtaining the optimum material selection and distribution of materials within a complex automotive structure.

Conventional vehicle design is continually being pushed by consumers and regulations to reach higher level of fuel efficiency and system performance. New methods such as use of alternative structural materials and structural optimization are being utilized heavily in the automotive industry. Currently, materials such as advanced composites, polymers, aluminum and magnesium are all being considered as candidates for alternatives to conventional steel parts to help meet lightweight performance targets. While topology optimization has proven to be a powerful in many case studies for automotive light weighting studies, it is currently constrained for use with one material in the optimization algorithm. Multi-material topology optimization (MMTO) methods presented in this paper demonstrate the tools capability to optimize material selection simultaneously alongside material layout for a given design space and desired weight target.

As automakers continue to develop new lightweight vehicles, the application of multi-material parts, assemblies and systems is needed to enhance overall performance and safety of new and emerging architectures. To achieve these goals conventional material selection and design strategies may be employed, such as standard material performance indices or full-combinatorial substitution studies. While these detailed processes exist, they often succeed at only suggesting one material per component, and cannot consider a clean-slate design; here, multi-material topology optimization (MMTO) is suggested as an effective computational tool for performing large-scale combined multi-material selection and design. Unlike previous manual methods, MMTO provides an efficient method for simultaneously determining material existence and distribution within a predefined design domain from a library of material options.

The automotive industry is facing significant challenges for next-generation vehicle design as fuel economy regulations and tailpipe emission standards continue to strive for greater efficiency. In order to ensure vehicle design reaches these sustainability targets, lightweighting through multi-material design and topology optimization (TO) has been suggested as the leading method to reduce weight from conventional component and small assembly structures. More effective tools, techniques, and methodologies are now required to advance the development of multi-phase optimization tools beyond current commercial capability, and help automotive designers achieve critical efficiency improvements without sacrificing performance. Presented here is a unique tool description and practical application of multi-material topology optimization (MMTO), a direct extension of the classical single-material problem statement (SMTO).