Predictive Modeling and Optimization of Layer-Cladded Ti-Al-Nb-Zr High-Entropy Alloys Using Machine Learning

In this work, the influence of laser power (LP), scanning speed (SS), and powder feeding speed (PF) on the porosity, dilution, and microhardness of lightweight refractory high-entropy alloy (RHEA) coatings produced via laser cladding (LC) was investigated. Variance analysis (ANOVA) was deployed to ascertain the effect of LP, SS, and PF on performance metrics such as porosity, dilution, and microhardness. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was then applied to optimize these processing parameters to minimize porosity, achieve suitable dilution, and maximize microhardness, enhancing the mechanical properties of RHEA coatings. Finally, machine learning models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Genetic Algorithm-enhanced GBDT (GA-GBDT)—were developed using orthogonal experimental data, with GA-GBDT demonstrating superior predictive accuracy. The proposed approach integrates statistical analysis and advanced ML techniques, providing a better understanding into optimizing LP, SS, and PF for improved RHEA coatings performance in industrial applications, thereby advancing laser cladding technology.