Nickel-based polycrystalline superalloy composition design framework based on non-dominated sorting genetic algorithm II
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: High Temperature Alloys and Creep · Additive Manufacturing Materials and Processes · Aluminum Alloy Microstructure Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
Show source-provided references
1. Powder bed fusion of nickel-based superalloys: A review10.1016/j.ijmachtools.2021.103729 ↗
2. Nickel-based superalloys for advanced turbine engines: Chemistry, microstructure and properties10.2514/1.18239 ↗
3. Effect of thermal cycles on laser direct energy deposition repair performance of nickel-based superalloy: Microstructure and tensile properties10.1016/j.ijmecsci.2022.107173 ↗
4. Development of strong, oxidation and corrosion resistant nickel-based superalloys: Critical review of challenges, progress and prospects10.1080/09506608.2018.1516713 ↗
5. Nickel-based materials for advanced rechargeable batteries10.1002/adfm.202107928 ↗
6. Computational design of a single crystal nickel-based superalloy with improved specific creep endurance at high temperature10.1016/j.commatsci.2019.109194 ↗
7. Computer-aided complexity classification of combinational problems10.1145/358690.363066 ↗
8. Ruan · 2020
9. N 18, a new damage tolerant PM superalloy for high temperature turbine discs
10. N18, powder metallurgy superalloy for disks: Development and applications10.1007/BF02661740 ↗
11. Analysis of the chemistry of Ni-base turbine disk superalloys using an alloys-by-design modeling approach10.1007/s11661-012-1569-7 ↗
12. CALPHAD design and high-throughput search of novel Ni-based superalloys that are reinforced by γ′+ γ′′
13. Alloys-by-design: Application to nickel-based single crystal superalloys10.1016/j.actamat.2009.08.018 ↗
14. Isolation of optimal compositions of single crystal superalloys by mapping of a material’s genome10.1016/j.actamat.2015.01.040 ↗
15. Single-crystal nickel-based superalloys developed by numerical multi-criteria optimization techniques: Design based on thermodynamic calculations and experimental validation10.1088/0965-0393/23/3/035004 ↗
16. High γ′ solvus new generation nickel-based superalloys for single crystal turbine blade applications
17. Coupling high-throughput experiment and machine learning to optimize elemental composition in nickel-based superalloys10.1557/s43579-021-00045-9 ↗
18. Application of artificial neural networks for prediction of nickel-based superalloys service properties based on the chemical composition
19. Modeling the heat resistance of nickel-based superalloys by a deep learning neural network
20. The learning of the precipitates morphological parameters from the composition of nickel-based superalloys10.1016/j.matdes.2021.109747 ↗
21. Design of Ni-based turbine disc superalloys with improved yield strength using machine learning10.1007/s10853-022-07295-5 ↗
22. Calculation of phase diagrams: Calphad10.1016/S1359-0286(96)80025-8 ↗
23. Linear programming techniques for regression analysis10.1080/01621459.1959.10501506 ↗
24. Application and comparison of several machine learning algorithms and their integration models in regression problems10.1007/s00521-019-04644-5 ↗
25. A unifying view of sparse approximate Gaussian process regression
26. A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions10.1016/j.jmp.2018.03.001 ↗
27. Gaussian process regression for materials and molecules10.1021/acs.chemrev.1c00022 ↗
28. A. Wilson, R. Adams, Gaussian process kernels for pattern discovery and extrapolation, in: Proceedings of 30th International Conference on Machine Learning, Atlanta, 2013, pp. 1067–1075.
29. A fast and elitist multiobjective genetic algorithm: NSGA-II10.1109/4235.996017 ↗
30. Overview of NSGA-II for optimizing machining process parameters10.1016/j.proeng.2011.08.745 ↗
31. A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems10.1109/ACCESS.2021.3070634 ↗
32. Genetic algorithm crossover operators for ordering applications10.1016/0305-0548(93)E0024-N ↗
33. Woodward · 2012 · 67
34. Using JMatPro to model materials properties and behavior10.1007/s11837-003-0013-2 ↗
35. Quench crack behavior of nickel-base disk superalloys10.1007/BF02717409 ↗
36. The Chinese society for metals academic committee of superalloys
37. Thermo-Calc & DICTRA, computational tools for materials science10.1016/S0364-5916(02)00037-8 ↗
38. SPEA2: Improving the strength Pareto evolutionary algorithm
39. The Pareto envelope-based selection algorithm for multiobjective optimization
40. C.L. Valenzuela, A simple evolutionary algorithm for multi-objective optimization (SEAMO), in: Proceedings of the 2002 Congress on Evolutionary Computation., Honolulu, 2002, pp. 717–722.10.1109/CEC.2002.1007014 ↗