Data-driven multi-objective optimization of ultralight hierarchical origami-corrugation meta-sandwich structures

Abstract

With advantages in stiffness, strength, and energy absorption, hierarchical origami-corrugation meta-sandwich (HOCM) structures are envisioned as a novel ultralight multifunctional sandwich construction for various practical applications. Firstly, the macro-equivalent compressive modulus is analytically derived using a two-level homogenization approach. The HOCM samples manufactured by selective laser melting are then tested under quasi-static out-of-plane compressive loading. A representative volume element model is proposed for finite element simulations to explore further the compressive performance, with good agreement achieved between measurements and simulations. Influences of critical geometric parameters on compressive properties, including initial failure modes, specific peak strength (SPS) and specific energy absorption (SEA), are numerically analyzed. Subsequently, the surrogate model based on a fully connected neural network algorithm is selected as the machine learning strategy to approximate the SPS and SEA, with cross-validation conducted to verify its accuracy. Finally, a multi-objective optimization method incorporating the surrogate model and the non-dominated sorting genetic algorithm II is implemented to carry out optimal design for HOCM structures possessing simultaneous superior SPS, SEA with assured stiffness. Such a data-driven optimization procedure based on the machine learning method exhibits high accuracy for strongly nonlinear problems, especially for SEA in current work, leading to highly efficient optimization.