Automated quantification of dihedral angles in sintered tungsten heavy alloys using image processing

The dihedral angle plays a critical role in determining the microstructure and mechanical behavior of tungsten heavy alloys (WHAs) made through liquid-phase sintering (LPS). It is a direct effect of the balance of interfacial energies at grain junctions and directly influences densification, grain connectivity, and shape stability. However, conventional ways of measuring these angles — usually by manually inspecting scanning electron micrographs — are time-consuming, subjective, and not suitable for analyzing large datasets. In this work, we present a fully automated and consistent method developed in Wolfram Mathematica for measuring dihedral angles in WHA microstructures. The method uses image preprocessing, noise reduction, and morphological operations to clearly identify grain boundaries. Using a graph-based representation, we detect grain junctions and apply curvature-based corner detection and line fitting to estimate the angles with high precision. This approach removes user bias, improves the reproducibility, and allows the analysis of hundreds of grain junctions in a single run. We have applied this framework to micrographs from WNiFe based WHA systems and confirmed that the results match expected trends. The proposed technique makes it easier to carry out large-scale studies on liquid phase sintered alloys, helping researchers understand the links between processing, microstructure, and properties in a more data-driven manner. It is particularly useful in applications where reliability is critical — such as defence, aerospace, and nuclear industries — where a well-engineered microstructure can make all the difference.