Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks

This paper investigates the structure-property relations of thin-walled lattices, characterized by their cross-sections and heights, under dynamic longitudinal compression. These relations elucidate the inter-actions of different geometric features of a design on mechanical response, including energy absorption. We proposed a combinatorial, key-based design system to generate different lattice designs and used the finite element method to simulate their response with the Johnson-Cook material model. Using an autoencoder, we encoded the cross-sectional images of the lattices into latent design feature vectors, which were supplied to the neural network model to generate predictions. The trained models can accu-rately predict lattice energy absorption curves in the key-based design system and can be extended to new designs outside of the system via transfer learning.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the structure-property relations of thin-walled lattices under dynamic longitudinal compression. A combinatorial, key-based design system is proposed to generate different lattice designs, and the finite element method is used to simulate their response. The trained models accurately predict lattice energy absorption curves and can be extended to new designs via transfer learning.