Abstract Manual quantitative analysis of large microstructural datasets is a challenging process. To address this, we present a novel pipeline for automated microstructure segmentation and 3D reconstruction in aluminum alloys, combining Mask R-CNN with a custom reconstruction algorithm. Datasets generated from light optical microscopy (LOM), computed tomography (CT) and phase field simulations (PFS) were used to train and evaluate the Mask R-CNN deep learning model. The alloys investigated include AlSi6Cu4Fe1, AlSi6Cu4Fe2 and AlCu10. The microstructures in sections comprise needle-like intermetallic precipitates and irregularly shaped precipitates in the former two alloys, and columnar dendrites formed during directional solidification in the latter. A 3D reconstruction algorithm was developed to generate three-dimensional representations from CT slices of individual dendrites and full monolithic structures of interconnected precipitates based on the Mask R-CNN detections. This reconstruction algorithm was validated using a 3D dataset from phase-field simulations to ensure accuracy and reliability. The deep learning model consistently achieved high detection accuracy across LOM and CT datasets for all investigated microstructural objects, reaching average accuracy of 73%, average precision of 82%, average recall of 79%, and average F1 score of 79%. Additionally, the model demonstrates the ability to distinguish specific geometries that traditional contrast- or color-based techniques cannot reliably differentiate. Furthermore, the 3D reconstruction method enhances the deep learning model capabilities by enabling three-dimensional visualization as well as qualitative and quantitative analyses of the detected objects. An example of quantitative analysis presented in this study includes the calculation of growth misorientation of the reconstructed directionally solidified dendrites.
Theofilatos et al. (2026) studied this question.