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. This allows designers to produce performance-driven concepts and obtain valuable component insights such as optimum material configuration and composition.
Presented in this paper are conventional multi-material selection and design techniques, with an emphasis on MMTO background, theory, and implementation. Existing challenges within MMTO for material selection and design are presented in a numerical case study, demonstrating the impact of constraint-levels and design space definitions on relative material ratios and final optimized mass. Ultimately, this paper provides a foundation for further research into multi-material applications under varying levels of design freedom.