journal article·2026-01

Data-driven multi-process modeling and integrated design strategy for complex high-performance alloys

Hongtao Zhang, Huadong Fu, Jingtai Sun, Lei Jiang, Shuaicheng Zhu, Jianxin Xie

Acta Materialia· Volume 302
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Abstract

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OpenAlex topics: Machine Learning in Materials Science · Advanced Materials Characterization Techniques · Additive Manufacturing Materials and Processes

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References (59)

Show source-provided references
1. Composition design, phase transition and fabrication of copper alloys with high strength and electrical conductivity
2. Microstructure evolution and dislocation strengthening mechanism of Cu-Ni-Co-Si alloy10.1016/j.msea.2021.142023 ↗
3. Enhanced mechanical and electrical properties of a Cu-Ni-Si alloy by thermo-mechanical processing10.1007/s11661-019-05507-3 ↗
4. Precipitation behavior of Cu-3.0 Ni-0.72 Si alloy10.1016/j.actamat.2018.12.047 ↗
5. Precipitation behavior of a new Cu-Ni-Co-Si-Fe-Mg alloy designed with low cobalt content10.1016/j.matdes.2024.112833 ↗
6. Microstructure and properties of a high-strength Cu-Ni-Si-Co-Zr alloy10.1007/s11665-013-0482-6 ↗
7. Aging behavior and precipitation analysis of Cu-Ni-Co-Si alloy10.3390/cryst8110435 ↗
8. Microstructure and properties of a novel Cu-Ni-Co-Si-Mg alloy with super-high strength and conductivity10.1016/j.msea.2018.12.075 ↗
9. Simultaneous enhancement of mechanical and electrical properties of Cu-Ni-Si alloys via thermo-mechanical process10.1016/j.msea.2022.142815 ↗
10. Relationship and mechanism between double cold rolling-aging process, microstructure and properties of Cu-Ni-Si alloy prepared by two-phase zone continuous casting10.1016/j.msea.2020.140148 ↗
11. Microstructure and properties of Cu-Ni-Co-Si-Cr-Mg alloys with different Si contents after multi-step thermo-mechanical treatment10.1016/j.msea.2022.143532 ↗
12. Materials informatics: From the atomic-level to the continuum10.1016/j.actamat.2019.01.051 ↗
13. Machine learning for molecular and materials science10.1038/s41586-018-0337-2 ↗
14. Machine Learning for Materials Research and Development
15. Recent progress in the machine learning-assisted rational design of alloys10.1007/s12613-022-2458-8 ↗
16. A general-purpose machine learning framework for predicting properties of inorganic materials10.1038/npjcompumats.2016.28 ↗
17. New tool in the box
18. Machine learning-enabled high-entropy alloy discovery10.1126/science.abo4940 ↗
19. Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening10.1016/j.actamat.2020.09.068 ↗
20. A property-oriented design strategy for high-performance copper alloys via machine learning10.1038/s41524-019-0227-7 ↗
21. A novel neural network-based alloy design strategy: Gated recurrent unit machine learning modeling integrated with orthogonal experiment design and data augmentation10.1016/j.actamat.2022.118420 ↗
22. An online tool for predicting fatigue strength of steel alloys based on ensemble data mining10.1016/j.ijfatigue.2018.04.017 ↗
23. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel10.1016/j.actamat.2019.08.033 ↗
24. Machine learning algorithms for the prediction of non-metallic inclusions in steel wires for tire reinforcement10.1007/s10845-020-01623-9 ↗
25. A vision of materials genome engineering in China10.1016/j.eng.2021.12.008 ↗
26. A data-science approach to predict the heat capacity of nanoporous materials10.1038/s41563-022-01374-3 ↗
27. Prospects of materials genome engineering frontiers10.1002/mgea.17 ↗
28. Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning10.1007/s12613-022-2479-3 ↗
29. A rapid and effective method for alloy materials design via sample data transfer machine learning10.1038/s41524-023-00979-9 ↗
30. Effect of aluminum on microstructure and property of Cu-Ni-Si alloys10.1016/j.msea.2013.02.024 ↗
31. Accelerated search for materials with targeted properties by adaptive design10.1038/ncomms11241 ↗
32. An informatics approach to transformation temperatures of NiTi-based shape memory alloys10.1016/j.actamat.2016.12.009 ↗
33. Accelerated discovery of large electrostrains in BaTiO3-based piezoelectrics using active learning10.1002/adma.201702884 ↗
34. The search for BaTiO3-based piezoelectrics with large piezoelectric coefficient using machine learning10.1109/TUFFC.2018.2888800 ↗
35. Designing high dielectric permittivity material in barium titanate10.1021/acs.jpcc.7b04636 ↗
36. Machine learning assisted design of high entropy alloys with desired property10.1016/j.actamat.2019.03.010 ↗
37. Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models10.1016/j.actamat.2019.11.067 ↗
38. Machine learning assisted composition effective design for precipitation strengthened copper alloys10.1016/j.actamat.2021.117118 ↗
39. Empowering the sustainable development of high-end alloys via interpretive machine learning10.1002/adma.202404478 ↗
40. Effect of aging precipitation on properties of Cu-Ni-Si-Mg alloy10.4028/www.scientific.net/AMR.197-198.1315 ↗
41. Optimization of conductivity and strength in Cu-Ni-Si alloys by suppressing discontinuous precipitation10.1007/s12540-016-6156-9 ↗
42. Relationship between the microstructure and properties of a peak aged Cu-Ni-Co-Si alloy10.1080/02670836.2019.1576374 ↗
43. Microstructure and mechanical properties of a high strength Cu-Ni-Si alloy treated by combined aging processes10.1016/j.jallcom.2016.11.137 ↗
44. Properties and precipitates of the high strength and electrical conductivity Cu-Ni-Co-Si-Cr alloy10.1016/j.jmst.2021.03.049 ↗
45. Accelerated discovery of high-performance Cu-Ni-Co-Si alloys through machine learning10.1016/j.matdes.2021.109929 ↗
46. The cleavage strength of polycrystals
47. Hall-Petch relation and boundary strengthening10.1016/j.scriptamat.2004.06.002 ↗
48. Influence of stacking-fault energy on microstructural characteristics of ultrafine-grain copper and copper-zinc alloys10.1016/j.actamat.2007.10.053 ↗
49. Precipitation hardening in metals10.1179/026708399773002782 ↗
50. The dislocation cell size and dislocation density in copper deformed at temperatures between 25 and 700°C10.1016/0001-6160(72)90012-0 ↗
51. Density functional investigation on electronic structure and elastic properties of BeX at high pressure10.1007/s12648-015-0744-2 ↗
52. Effects of microstructure on mechanical properties of CuNiSi alloys10.1016/j.jallcom.2016.11.233 ↗
53. X-ray line broadening from field aluminium and wolfram10.1016/0001-6160(53)90006-6 ↗
54. Fracture and strength of solids10.1088/0034-4885/12/1/309 ↗
55. Strengthening mechanisms of Mg-Si alloys10.1016/1359-6454(96)00072-9 ↗
56. The transformation behavior of Cu-8.0 Ni-1.8 Si-0.6 Sn-0.15 Mg alloy during isothermal heat treatment10.1016/j.matchar.2011.06.008 ↗
57. Davis · 2001
58. Studies on recrystallization of single-phase copper alloys by resistance measurements10.1016/j.actamat.2009.12.018 ↗
59. Grain boundary contribution to the electrical conductivity of polycrystalline Cu films10.1088/0305-4608/5/9/009 ↗