Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning

Multi-principal element alloys (MPEAs), distinguished by their complex compositions and exceptional mechanical properties, pose significant challenges for conventional predictive approaches in mechanical property optimization. This study proposes an innovative intelligent optimization algorithm (OA) to refine feature selection in machine learning (ML) models, targeting the prediction of ultimate tensile strength (UTS) and fracture elongation (FE) in MPEAs. Comparative analysis with genetic algorithms (GA) reveals that the OA achieves high computational efficiency and improved prediction accuracy, demonstrating superior convergence rates and feature recognition capability. Additionally, low-cost experimental data from Fe-Cr-Ni-Al/Ti alloys are used to revise the model, thereby enhancing its prediction accuracy for high-cost processes. Validation experiments on Al 5 (Fe 10 Cr 35 Ni 55 ) 95 and Al 2 Ti 1 (Fe 10 Cr 35 Ni 55 ) 97 alloys yielded UTS prediction errors of 7.80% and 2.56%, with corresponding FE errors of 0.35% and 1.09%. These results confirm that strategic integration of experimental data within defined compositional ranges can substantially improve ML model performance.