Predictive Modeling and Optimization of Layer-Cladded Ti-Al-Nb-Zr High-Entropy Alloys Using Machine Learning
Source abstract
Abstract
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.
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Research context
OpenAlex topics: High Entropy Alloys Studies · Additive Manufacturing Materials and Processes · High-Temperature Coating Behaviors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Shandong Provincial Geo-Mineral Engineering Exploration Institute (801 Institute of Hydrogeology and Engineering Geology), Shandong Provincial Bureau of Geology & Mineral Resources, Jinan 250014, China
School of Mechatronics Engineering, China University of Mining and Technology, Xuzhou 221116, China
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Dipartimento di Scienze e Metodi dell’Ingegneria, Università di Modena e Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy
Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
Key Laboratory of Geological Disaster Risk Prevention and Control, Emergency Management Department of Shandong Province, Jinan 250014, China
Funding
Key Laboratory of Geological Disaster Risk Prevention and Control, Emer-gency Management Department of Shandong Province · China Postdoctoral Science Foundation Funded Project · Jiangsu Provincial Postdoctoral Science Foundation Funded Project · Jiangsu Provincial Double-Innovation Doctor Program · China Postdoctoral International Exchange Program · University of Waterloo
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