Automatic Detection of Dendritic Microstructure Using Computer Vision Deep Learning Models Trained with Phase Field Simulations

Abstract In this study, a novel approach to analyze the dendritic microstructure in solidification processes is presented, using an innovative method to prepare datasets for deep learning training with minimal human intervention through phase field simulations. This simulation technique, known for its accurate description of dendritic morphologies, enables the creation of comprehensive and precise microstructure datasets. By using advanced deep learning techniques, in particular Faster R-CNN and Mask R-CNN methods, we have successfully automated the detection of dendritic growth in various scenarios. In microgravity and terrestrial solidification experiments with optically transparent alloys, faster R-CNN was particularly effective in identifying loosely connected dendrites in experimental images and showed superior performance over thresholding methods, especially in detecting optically overlapping dendrites. For contiguous dendrites in directionally solidified polycrystalline metal alloys, mask R-CNN proved to be extremely proficient due to its ability to accurately delineate closely spaced dendrites. The use of phase field simulations to generate datasets played a crucial role in training and testing these models. Our research highlights the significant potential of deep learning in describing complex microstructural patterns, contributing to a deeper understanding of the solidification process and its effects on material properties, with the added benefit of facilitating dataset generation through a highly accurate microstructure generation method. Furthermore, this method can be applied to any type of microstructure and to different types of materials, as the phase field simulations can accurately simulate many microstructural properties.