Machine learning-based screening of high-entropy alloy AlCoCrFeNi particles produced via high-energy mechanical alloying method

There is a growing interest in exploring high entropy alloys (HEAs) due to their superior mechanical and thermal properties. Although numerous routes exist for producing HEA particles, mechanical alloying (MA) remains the most conductive method at the laboratory level due to its simplicity and cost-effectiveness. In powder metallurgy, the degree of alloying plays a crucial role in achieving the desired quality of final manufactured parts. However, in mechanically milled HEA powders, segregation often arises from localized energy concentrations, leading to significant compositional inhomogeneity. Currently, researchers rely on conventional energy-dispersive spectroscopy (EDS) to assess alloying state. SEM alone is less time-consuming than with EDS integration. Yet, EDS maps can be inaccurate, have low reproducibility, and be misleading for HEA powder, indicating alloyed composition, even when the opposite is true. This study proposes a deep learning regression approach to predict compositional homogeneity, which can be thresholded to classify alloyed and segregated particles directly from scanning electron microscopy (SEM) images. The approach utilizes compositional/alloying uniformity derived from per-particle EDS composition vectors of the AlCoCrFeNi HEA system, serving as a ground truth. Different regression models were trained to directly predict Shannon entropy (H), a nondimensional measure of compositional uniformity, with DenseNet121 achieving an R² of 0.918 and an MAE of 0.03, corresponding to an average deviation of approximately 3% of the full alloying uniformity scale. This approach enables rapid, objective, reproducible, and automated screening of HEA powder, offering a scalable alternative to conventional microanalysis techniques. The proposed model can be integrated into existing laboratory workflows for mechanically alloyed AlCoCrFeNi systems, enabling quality assessment during the intermediate and final stages of milling using only bench-top SEMs without requiring an EDS detector.