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.
Abstract (translated)
金属材料在退火过程中的微观结构演变及最终机械性能的预测对于材料设计和制造至关重要。然而，传统的基于偏微分方程的方法计算成本高昂，成为了瓶颈。本文中，我们引入了一种结合卷积长短时记忆网络与自动编码器的机器学习框架，用于高效地预测晶粒生长演化。 我们的方法能够捕捉晶粒演变的空间和时间特性，并将高维晶粒结构数据编码到一个紧凑的潜在空间中以进行模式学习。此外，通过一种新的复合损失函数（结合均方误差、结构相似性指数测量以及边界保持）来维护预测中的晶界拓扑结构完整性。 实验结果表明，我们的机器学习方法能够加速晶粒生长预测高达89倍，计算时间从10分钟减少到大约10秒，同时维持高精度的预测。最佳模型（S-30-30）达到了86.71%的结构相似度得分和仅0.07%的平均晶粒尺寸误差。所有模型均准确捕捉了晶界拓扑、形态及大小分布。 这种快速微观结构预测方法适用于传统模拟过于耗时的应用场景，有可能加速材料科学与制造领域的创新。
URL
https://arxiv.org/abs/2505.05354
