Work

Design of Novel Auxetic Bi-Materials Using Convolutional Neural Networks

Year: 2025

Type: article

Abstract: A convolutional neural network (CNN) was developed to predict the Poisson's ratio of representative volume elements (RVEs) composed of a bi-material system with soft and hard phases. The CNN was trained on a dataset of binary microstructure configurations, learning to approximate the effective Poisson's ratio based on spatial material distribution. Once trained, the network was integrated into a greedy optimization algorithm to identify microstructures with auxetic behavior. The algorithm iteratively modified material arrangements, leveraging the CNN's rapid inference to explore and refine configurations efficiently. The results demonstrate the feasibility of using deep learning for microstructure evaluation and optimization, offering a computationally efficient alternative to traditional finite element simulations. This approach provides a promising tool for the design of advanced metamaterials with tailored mechanical properties.

Source: Materials

DOI: 10.3390/ma18081772

Authors: Iulian Constantin Coropețchi, Dan Mihai Constantinescu, Alexandru Vasile, Andrei Ioan Indreș, Ștefan Sorohan

Institutions: Military Technical Academy, Universitatea Națională de Știință și Tehnologie Politehnica București, Institute of Solid Mechanics

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Ministerul Educaţiei Naţionale