journal article·2025-11

A hybrid optimization method for determining laser cladding process parameters to control geometric morphology in CoCrFeNiMn high-entropy alloy

Xin Lei, Yanbin Du, Hongxi Chen, Yunchuan Peng, Wensheng Ma, Jian Tu

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References (46)

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1. High-entropy alloys10.1038/s41578-019-0121-4 ↗
2. Machine learning for high-entropy alloys: progress, challenges and opportunities10.1016/j.pmatsci.2022.101018 ↗
3. Machine learning–enabled high-entropy alloy discovery10.1126/science.abo4940 ↗
4. Review on wear resistance of laser cladding high-entropy alloy coatings10.1016/j.jmrt.2023.11.138 ↗
5. Twenty years of the CoCrFeNiMn high-entropy alloy: achieving exceptional mechanical properties through microstructure engineering10.1016/j.jmrt.2023.01.181 ↗
6. Low cycle fatigue properties of CoCrFeNiMn high-entropy alloy with heterogeneous microstructure10.1016/j.msea.2024.147004 ↗
7. Exceptional thermal stability of additively manufactured CoCrFeMnNi high-entropy alloy with cellular dislocation structures10.1016/j.msea.2023.145650 ↗
8. Corrosion resistance and wear behavior of CoCrFeNiMn@Gr high entropy alloy-based composite coatings prepared by laser cladding10.1016/j.jmrt.2024.10.053 ↗
9. High temperature deformation behavior and dynamic recrystallization in CoCrFeNiMn high entropy alloy10.1016/j.msea.2015.03.097 ↗
10. High-entropy alloys: advantages and applications in challenging environments
11. Effect of synergistic cavitation erosion-corrosion on cavitation damage of CoCrFeNiMn high entropy alloy layer by laser cladding10.1016/j.surfcoat.2023.129940 ↗
12. The pre-corrosion fatigue behavior of 321 steel coated by laser additively manufactured FeCoNiCrMn high-entropy alloy10.1016/j.oceaneng.2024.117008 ↗
13. Direct laser deposition cladding of AlxCoCrFeNi high entropy alloys on a high-temperature stainless steel10.1016/j.surfcoat.2017.09.072 ↗
14. A physics-informed neural network framework to predict 3D temperature field without labeled data in process of laser metal deposition10.1016/j.engappai.2023.105908 ↗
15. Effect of heat treatment on microstructure and mechanical properties of Fe60 coating by laser cladding on 304 stainless steel10.1016/j.jmrt.2024.02.046 ↗
16. Effect of laser specific energy on microstructure and properties of Nano-TiC/15-5PH composite coatings on 17-4PH by laser cladding10.1016/j.optlastec.2025.112598 ↗
17. Effect of double aging treatment on the tribological properties of 15-5PH coating on 17-4PH stainless steel by laser cladding10.1016/j.jmrt.2025.02.104 ↗
18. Microstructure and wear resistance of CoCrFeNiMn coatings prepared by extreme-high-speed laser cladding10.1016/j.surfcoat.2023.129821 ↗
19. Microstructural evolution and wear characteristics of laser-clad CoCrFeNiMn high-entropy alloy coatings incorporating tungsten carbide10.1016/j.jallcom.2023.173124 ↗
20. Effect of process parameters on the microstructure and properties of laser-clad FeNiCoCrTi0.5 high-entropy alloy coating10.1007/s12613-019-1958-7 ↗
21. A critical review of high entropy alloys and related concepts10.1016/j.actamat.2016.08.081 ↗
22. 2016
23. Microstructural development in equiatomic multicomponent alloys10.1016/j.msea.2003.10.257 ↗
24. Effect of laser cladding parameters on Inconel 718 coating performance and multi-parameter optimization10.1016/j.optlastec.2022.108850 ↗
25. Statistical analysis and optimization of process parameters in Ti6Al4V laser cladding using Nd:YAG laser10.1016/j.optlaseng.2012.01.018 ↗
26. Modeling and optimization method of laser cladding based on GA-ACO-RFR and GNSGA-II10.1007/s40684-022-00492-2 ↗
27. Fundamental requirements of a machine learning operations platform for industrial metal additive manufacturing10.1016/j.compind.2023.104037 ↗
28. Predictability of mechanical behavior of additively manufactured particulate composites using machine learning and data-driven approaches10.1016/j.compind.2022.103739 ↗
29. Application of artificial neural network for the prediction of laser cladding process characteristics at Taguchi-based optimized condition10.1007/s00170-013-5393-z ↗
30. An overview of technological parameter optimization in the case of laser cladding10.3390/coatings13030496 ↗
31. Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning10.1016/j.compind.2023.104066 ↗
32. Prediction for dilution rate of AlCoCrFeNi coatings by laser cladding based on a BP neural network10.3390/coatings11111402 ↗
33. Prediction of clad characteristics using ANN and combined PSO-ANN algorithms in laser metal deposition process
34. Parameters optimization for single-track laser cladding based on MPA-SVR and A-NSGA-III10.1016/j.optlastec.2023.110069 ↗
35. Optimization and characterization of laser cladding of 15-5PH coating on 20Cr13 stainless steel10.1007/s11665-022-07157-w ↗
36. Rapid optimization of iron-based alloy laser cladding process based on orthogonal experiment and machine learning for Q34510.1016/j.optlastec.2024.112086 ↗
37. Newton-Raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems10.1016/j.engappai.2023.107532 ↗
38. A general convergence analysis method for evolutionary multi-objective optimization algorithm10.1016/j.ins.2024.120267 ↗
39. Online monitoring of welding status based on a DBN model during laser welding10.1016/j.eng.2019.01.016 ↗
40. The development of a deep neural network and its application to evaluating the interior sound quality of pure electric vehicles10.1016/j.ymssp.2018.09.035 ↗
41. Prediction of viscosity of blast furnace slag based on NRBO-DNN model10.1016/j.aej.2025.01.126 ↗
42. Elmi Z, Elmi S, Danishvar S. NRBO-AGP: a novel feature selection approach for accurate protein solubility prediction. Expert Syst Appl 2026;296(Part D):129194. https://doi.org/10.1016/j.eswa.2025.129194.10.1016/j.eswa.2025.129194 ↗
43. Gradient-based optimizer: a new metaheuristic optimization algorithm10.1016/j.ins.2020.06.037 ↗
44. Deep belief network with fuzzy parameters and its membership function sensitivity analysis10.1016/j.neucom.2024.128716 ↗
45. Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm10.1016/j.knosys.2019.01.015 ↗
46. Optimizing laser cladding process parameters for transformation-induced plasticity FeMnCoCr high entropy alloy: a multi-objective approach10.1016/j.surfcoat.2024.131536 ↗