Contrastive learning based on hierarchical graph of microstructures through directed energy deposition process to establish process-structure–property relationship via autoencoder

Abstract

n this study, an innovative algorithm is developed to transform microstructures to hierarchical graphs without manual feature engineering. The hierarchical graph comprises two layers. Initially, pixel data, which includes Euler angles, phase, and position, forms the pixel-wise graphs within individual grains. Following this foundation, the grains serve as nodes, constituting the second layer. Importantly, the hierarchical graph preserves essential measurement data and structural details for training machine learning models. After that, a contrastive learning model based on hierarchical graph is designed to capture representations of microstructures obtained by the electron backscatter diffraction (EBSD) technique. This model can be directly extended for other microscopy techniques, such as Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX), and Transmission Electron Microscopy (TEM). Using the learned microstructure representations, an autoencoder model for the directed energy deposition (DED) is developed to establish the relationship among process parameters, microstructures, and material properties, completing the process-structure–property cycle quantitatively. The performance of the present model is naturally benchmarked against two other models: a contrastive learning model based on pixel-wise graph (without manual feature engineering) and a contrastive learning model based on grain-wise graph (employing manual feature engineering). The results of the present model highlight the potential in decoding the process-structure–property relationships in the DED process.

Keywords

• Contrastive learning
• Hierarchical graph
• Electron backscatter diffraction (EBSD)
• Process-structure–property
• Directed energy deposition (DED)