High-fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast Computations

TL;DR

This paper presents a deep learning framework combining ConvLSTM and Autoencoder to predict grain growth rapidly and accurately, significantly reducing computation time compared to traditional methods.

Contribution

It introduces a novel machine learning approach with a composite loss function for high-fidelity grain growth prediction, enabling faster simulations in materials science.

Findings

Achieved up to 89x speedup in grain growth prediction

Maintained high structural similarity score of 86.71%

Accurately captured grain boundary topology and size distribution

Abstract

Grain growth simulation is crucial for predicting metallic material microstructure evolution during annealing and resulting final mechanical properties, but traditional partial differential equation-based methods are computationally expensive, creating bottlenecks in materials design and manufacturing. In this work, we introduce a machine learning framework that combines a Convolutional Long Short-Term Memory networks with an Autoencoder to efficiently predict grain growth evolution. Our approach captures both spatial and temporal aspects of grain evolution while encoding high-dimensional grain structure data into a compact latent space for pattern learning, enhanced by a novel composite loss function combining Mean Squared Error, Structural Similarity Index Measurement, and Boundary Preservation to maintain structural integrity of grain boundary topology of the prediction. Results demonstrated that our machine learning approach accelerates grain growth prediction by up to \SI{89}{\times} faster, reducing computation time from \SI{10}{\minute} to approximately \SI{10}{\second} while maintaining high-fidelity predictions. The best model (S-30-30) achieving a structural similarity score of \SI{86.71}{\percent} and mean grain size error of just \SI{0.07}{\percent}. All models accurately captured grain boundary topology, morphology, and size distributions. This approach enables rapid microstructural prediction for applications where conventional simulations are prohibitively time-consuming, potentially accelerating innovation in materials science and manufacturing.