Design of Novel Auxetic Bi-Materials Using Convolutional Neural Networks
Iulian Constantin Coropețchi, Dan Mihai Constantinescu, Alexandru Vasile, Andrei Ioan Indreș, Ștefan Sorohan
TL;DR
This paper introduces a deep learning approach to design auxetic materials by predicting and optimizing their mechanical properties using a convolutional neural network.
Contribution
The novel use of a CNN to predict and optimize auxetic bi-material microstructures for efficient metamaterial design.
Findings
The CNN accurately predicts Poisson’s ratio based on material distribution in bi-material systems.
The greedy optimization algorithm efficiently identifies auxetic microstructures using CNN inference.
The approach offers a computationally efficient alternative to traditional finite element simulations.
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…
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Taxonomy
TopicsCellular and Composite Structures · Machine Learning in Materials Science · Topology Optimization in Engineering