High energy absorption design of porous metals using deep learning

Abstract

Due to its remarkable energy absorption properties, porous metals have widespread applications in engineering. However, the high randomness of pore morphology greatly hinders the effective design and analysis of high energy absorption structures. To address this challenge, this paper first introduces a deep learning-based framework for high energy absorption-oriented design of random porous metals structures. The framework comprises two steps: (i) a generator powered by Wasserstein deep convolutional generative adversarial network is developed to swiftly generate a vast design space (∼one million samples) of porous metals with real random pore morphology. (ii) an inverse search strategy based on convolutional neural network is applied to quickly pick out the optimal structure with the best energy absorption from the design space. Results show that the optimal energy absorption is about 17.71 % higher than the maximum value of initial structures from CT scan. Additionally, a 575-fold increase in computational efficiency is achieved compared to the traversal search using finite element method. Subsequently, the deformation process of the optimal structure is analyzed focusing on the pore morphology and compression performance, showing that random porous metals with uniformly sized pores are capable of withstanding higher stress under the same strain and exhibit no yield band during compression. Inspired by this, a structural homogenization method is introduced and validated to create porous metal structure with stable microstructure evolution, extended plateau stress and high energy absorption.