Design of Functionally Graded Materials Using Transient Nonlinear Simulations and Genetic Algorithm Optimization

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The objective of this research is to develop a robust methodology for the design of functionally graded materials (FGMs). FGMs are advanced composite materials that are engineered to have a smooth spatial variation of material properties. This is achieved by gradually varying the relative volume fractions and microstructure of the material constituents during fabrication. FGM components typically exhibit smaller stresses and higher factors of safety than discretely bonded monolithic materials. The aim of this research project is to create a unified framework for the simultaneous optimization of structural shape, compositional profile and microstructure of metal/ceramic FGMs that are subjected to time varying thermal and mechanical loads. The proposed technique utilizes a nonlinear elastoplastic model and numerical simulations using a meshless method to accurately analyze candidate designs. A robust multi-objective genetic algorithm will be used to simultaneously optimize structure shape, fractional composition and microstructure of the material. If successful, this project will result in a powerful design tool that could assist engineers and other professionals engaged in the design process with FGMs. It will benefit society by contributing new knowledge regarding the simulation and optimization of FGMs and by reducing the failure of mechanical components. A user-friendly software package implementing the proposed method will be developed and distributed freely on the Internet through the PI's web page. The educational plan will emphasize design as an important element of engineering education by incorporating computer-aided analysis, shape and material optimization to the PI's machine design and composite materials courses.