Predicting creep behavior in composites from microstructural features using deep learning

This study uses a multilayer perceptron deep learning model to predict creep strain vs time curves in composite materials based on their microstructural features. Finite element simulations generate ground truth data for model training and validation. The multilayer perceptron model, trained on this comprehensive dataset, effectively captures the complex relationships between the microstructure and creep behavior, achieving high accuracy. Comparative analysis with traditional models shows the multilayer perceptron model's superior performance. This demonstrates the model's potential for reliable application in various engineering fields, offering improved predictions of composite material behavior under creep conditions. (c) 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND) license (https://creativecommons.org/licenses/by-nc-nd/4.0/).