Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm

Triply periodic minimal surfaces (TPMSs) have attracted great attention due to their distinct advantages such as high strength and light weight compared to traditional lattice structures. Most previous works focus on forward prediction of the mechanical behaviors of TPMSs. Inverse design of the configurations based on customized loading curves would be of great value in engineering applications such as energy absorption. Inspired by TPMSs, we propose the concept of the shell-based mechanical metamaterial (SMM) in this work, which possesses the main geometrical features and mechanical properties of TPMSs. A novel approach, combining machine learning (ML) for high efficiency and genetic algorithm (GA) for global optimization, is put forward to inversely design the configuration of SMM. Two strategies are introduced to develop artificial neural networks (ANNs) for the prediction of their loading curves under compression. GA is then employed to design objective configurations with customized loading curves. The connection between the loading curves and deformation modes is also illustrated to demonstrate the values of such inverse design. SMM with a strain-hardening curve tends to exhibit globally uniform deformation, while SMM with a strain-softening curve tends to present layer-by-layer deformation during compression, which is demonstrated by experiments and simulations. This work fills the blanks of inverse design of SMM with customized loading curves and contributes to the concept of structure design combining ML and traditional optimization approaches.(c) 2022 Published by Elsevier B.V.

Automated summary

This research presents an inverse design of shell-based mechanical metamaterial (SMM) utilizing a combination of machine learning and genetic algorithm. It fills the gap in the field of inverse design of SMM with customized loading curves.