Self-consistent Clustering Analysis-Based Moving Morphable Component (SMMC) Method for Multiscale Topology Optimization

Abstract

Current multiscale topology optimization restricts the solution space by enforcing the use of a few repetitive microstructures that are predetermined, and thus lack the ability for structural concerns like buckling strength, robustness, and multi-functionality. Therefore, in this paper, a new multiscale concurrent topology optimization design, referred to as the self-consistent analysis-based moving morphable component (SMMC) method, is proposed. Compared with the conventional moving morphable component method, the proposed method seeks to optimize both material and structure simultaneously by explicitly designing both macrostructure and representative volume element (RVE)-level microstructures. Numerical examples with transducer design requirements are provided to demonstrate the superiority of the SMMC method in comparison to traditional methods. The proposed method has broad impact in areas of integrated industrial manufacturing design: to solve for the optimized macro and microstructures under the objective function and constraints, to calculate the structural response efficiently using a reduced-order model: self-consistent analysis, and to link the SMMC method to manufacturing (industrial manufacturing or additive manufacturing) based on the design requirements and application areas.