Customizing mechanical properties of additive-manufactured metallic meta grain structure with sheet-based gyroid architecture

Abstract

Abstract Cellular structures achieved using high-precision additive manufacturing techniques have enabled extraordinary results, such as negative Poisson ratio, negative thermal expansion, and optical metamaterials. However, the use of cellular structures in mechanical applications is disadvantageous because of 1) limited design options, causing limited mechanical performances and 2) the absence of systematic design methodologies. Hence, we propose a novel structure inspired by the polycrystalline structure to enhance design variability as well as mechanical reliability. This is called meta grain structure (MGS). A systematic design methodology is also proposed to obtain the optimal design of MGS that satisfies the target mechanical property. A database with 138 different MGSs is built and tested numerically, so that the correlation between the relative densities of grains in the MGS and the corresponding mechanical properties is analyzed. In addition, the optimum design parameters of the MGS that satisfy the target Young’s modulus are obtained using particle swarm optimization (PSO) and machine learning (ML)-based regression models. The proposed design methodology enables us to build a unique structure that offers various design options and overcomes the current limitations of lattice structures. Furthermore, a systematic inverse design methodology using PSO and ML-based regression models helps us to create an MGS that satisfies the required mechanical performance