Machine learning assisted study of phase and properties in cobalt-free AlCrxCuFeNi2 high-entropy alloys

Abstract

The application of machine learning (ML) accelerates the discovery of new materials, the prediction and optimization of properties, and the analysis of the correlation between structure and properties. The design, characterization, and mechanical properties of the novel AlCrxCuFeNi2 cobalt-free high-entropy alloys (HEAs) were investigated in this study. Through the experimental validation combined with the predictive K-nearest neighbor model (KNN) and the extreme gradient boosting model (XGBoost), the influence of Cr content on phase evolution and mechanical behavior were analyzed comprehensively. The results indicate that the optimized KNN and XGBoost models can achieve the 10-fold average cross validation (CV) accuracies of 0.761 and 0.836, respectively. Eight components of AlCrxCuFeNi2 HEAs are selected for experimental verification. The experimental results shows that the microstructures of the eight alloys are composed of FCC + BCC phases, which is agreement with the prediction. With the increase of Cr content, the dendrite morphology becomes finer and the lattice constants increase. The addition of Cr results in the hardness values from 332.4 HV to 447.7 HV and the deviation from the predicted ones is between 5.4 HV and 37.3 HV. It is worth noting that wear resistance is consistent with the change in hardness values, the specific wear rate decreases from 3.152 × 10−4 mm3/Nm to 6.357 × 10−5 mm3/Nm, and when x = 2.0, the wear resistance is the best. This research underscores the efficacy of ML in expediting the discovery and optimization of HEAs, offering valuable insights for promoting materials design and engineering applications.