Predicting composite microstructure from deformation data using deep learning

Abstract

Predicting the microstructure of composite plates based on deformation data under static loads is crucial for advanced materials design and optimization. This study utilizes finite element simulations to generate deformation data, capturing the complex mechanical behavior of composite materials under static loading conditions. We developed a deep learning model based on a multi-layer perceptron (MLP) architecture to predict the microstructure of these composite plates from the simulated deformation data. The model is trained on a dataset comprising diverse microstructural patterns and their corresponding deformation responses. Our results demonstrate the MLP’s capability to accurately infer microstructural details, highlighting the potential of deep learning in materials science. This approach not only enhances the understanding of the relationship between deformation and microstructure but also provides a robust framework for designing composite materials with desired properties through computational methods.