Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning
The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.