REFERENCES

1. Javidani, M.; Larouche, D. Application of cast Al–Si alloys in internal combustion engine components. Int. Mater. Rev. 2014, 59, 132-58.

2. Timelli, G.; Caliari, D.; Rakhmonov, J. Influence of process parameters and Sr addition on the microstructure and casting defects of LPDC A356 alloy for engine blocks. J. MaterSci. Technol. 2016, 32, 515-23.

3. Yan, J.; Tong, Q.; Zhang, W.; et al. Research on accelerated thermal fatigue testing and life prediction of Al-Si alloy pistons under start-stop cycles. Int. J. Fatigue. 2025, 191, 108677.

4. Xiong, P.; Liu, S.; Li, Z.; et al. A new accelerated thermal fatigue experiment method of pistons and its application. Eng. Fail. Anal. 2024, 163, 108599.

5. Roy, S.; Allard, L. F.; Rodriguez, A.; Watkins, T. R.; Shyam, A. Comparative evaluation of cast aluminum alloys for automotive cylinder heads: Part I - Microstructure evolution. Metall. Mater. Trans. A. 2017, 48, 2529-42.

6. Rakhmonov, J.; Liu, K.; Pan, L.; Breton, F.; Chen, X. Enhanced mechanical properties of high-temperature-resistant Al–Cu cast alloy by microalloying with Mg. J. Alloys. Compd. 2020, 827, 154305.

7. Jiang, L.; Rouxel, B.; Langan, T.; Dorin, T. Coupled segregation mechanisms of Sc, Zr and Mn at θ′ interfaces enhances the strength and thermal stability of Al-Cu alloys. Acta. Mater. 2021, 206, 116634.

8. Bansal, U.; Singh, M. P.; Mondol, S.; et al. The interplay of precipitation of ordered compounds and interfacial segregation in Al‐Cu‐Hf‐Si alloys for high-temperature strength. Acta. Mater. 2022, 240, 118355.

9. Wu, Y.; Chen, Z.; Le, W.; Zhang, H.; Song, W.; Yang, Z. Optimization of high-temperature mechanical properties of aluminum alloys through exclusive precipitation of T-phase. J. Mater. Res. Technol. 2024, 33, 3144-54.

10. Booth-Morrison, C.; Dunand, D. C.; Seidman, D. N. Coarsening resistance at 400 °C of precipitation-strengthened Al–Zr–Sc–Er alloys. Acta. Mater. 2011, 59, 7029-42.

11. Gao, Y. H.; Yang, C.; Zhang, J. Y.; et al. Stabilizing nanoprecipitates in Al-Cu alloys for creep resistance at 300°C. Mater. Res. Lett. 2019, 7, 18-25.

12. Yang, J.; Ni, Y.; Li, H.; Fang, X.; Lu, B. Heat treatment optimization of 2219 aluminum alloy fabricated by wire-arc additive manufacturing. Coatings 2023, 13, 610.

13. Gariboldi, E.; Sharma, A. Enhancing thermal stability and high-temperature properties of foundry Al alloys through Zr and/or Er as minor alloying elements. Int. J. Metalcast. 2025, 19, 3552-65.

14. Qi, Y.; Zhang, H.; Yang, X.; et al. Achieving superior high-temperature mechanical properties in Al-Cu-Li-Sc-Zr alloy with nano-scale microstructure via laser additive manufacturing. Mater. Res. Lett. 2024, 12, 17-25.

15. Lu, Q.; Wang, J.; Li, H.; et al. Synergy of multiple precipitate/matrix interface structures for a heat resistant high-strength Al alloy. Nat. Commun. 2023, 14, 2959.

16. Mondol, S.; Makineni, S. K.; Kumar, S.; Chattopadhyay, K. Enhancement of high temperature strength of 2219 alloys through small additions of Nb and Zr and a novel heat treatment. Metall. Mater. Trans. A. 2018, 49, 3047-57.

17. Mondol, S.; Kashyap, S.; Kumar, S.; Chattopadhyay, K. Improvement of high temperature strength of 2219 alloy by Sc and Zr addition through a novel three-stage heat treatment route. Mater. Sci. Eng. A. 2018, 732, 157-66.

18. Cheng, S.; Yi, W.; Zhang, M.; Gao, T.; Zhang, L. Forward versus inverse design of Al-Si-Mg-Cu alloys targeting peak comprehensive mechanical properties: a comparative study integrating computational thermodynamics and active learning. Acta. Mater. 2026, 306, 121930.

19. Jiang, L.; Zhang, Z.; Hu, H.; He, X.; Fu, H.; Xie, J. A rapid and effective method for alloy materials design via sample data transfer machine learning. npj. Comput. Mater. 2023, 9, 979.

20. Yin, J.; Lei, Q.; Li, X.; et al. A novel neural network-based alloy design strategy: gated recurrent unit machine learning modeling integrated with orthogonal experiment design and data augmentation. Acta. Mater. 2023, 243, 118420.

21. Yang, C.; Ren, C.; Jia, Y.; Wang, G.; Li, M.; Lu, W. A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness. Acta. Mater. 2022, 222, 117431.

22. Wang, J.; Kwon, H.; Oh, S.; et al. Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis. npj. Comput. Mater. 2025, 11, 1730.

23. Kavousi, S.; Asle, Zaeem. M. Integration of multiscale simulations and machine learning for predicting dendritic microstructures in solidification of alloys. Acta. Mater. 2025, 289, 120860.

24. Jiang, L.; Fu, H.; Zhang, Z.; et al. Synchronously enhancing the strength, toughness, and stress corrosion resistance of high-end aluminum alloys via interpretable machine learning. Acta. Mater. 2024, 270, 119873.

25. Jiang, L.; Zhoutai, W.; Zhang, X.; et al. Interpretable machine learning design for concurrent and significant enhancement of the mechanical properties and corrosion resistance of low-density Mg-Li alloys. J. Magnes. Alloys. 2025, 13, 6001-20.

26. Hu, M.; Tan, Q.; Knibbe, R.; et al. Designing unique and high-performance Al alloys via machine learning: mitigating data bias through active learning. Comput. Mater. Sci. 2024, 244, 113204.

27. Li, Y.; Pang, J.; Li, Z.; et al. Developing novel low-density high-entropy superalloys with high strength and superior creep resistance guided by automated machine learning. Acta. Mater. 2025, 285, 120656.

28. He, S.; Xiao, F.; Li, L.; et al. Design biomedical β-Ti alloys with exceptional strength-ductility balance via domain knowledge-based machine learning. Acta. Mater. 2025, 301, 121550.

29. Guru, M. K.; Bohlen, J.; Aydin, R. C.; Khalifa, N. B. Machine learning pipeline for Structure–Property modeling in Mg-alloys using microstructure and texture descriptors. Acta. Mater. 2025, 295, 121132.

30. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 2011, 12, 2825-30. https://jmlr.org/papers/v12/pedregosa11a.html. (accessed on 27 Jul 2026).

31. Lundberg, S. M.; Lee, S. I. A unified approach to interpreting model predictions. In 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, USA. 2017. https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf. (accessed on 27 Jul 2026).

32. Jiang, L.; Wu, G.; Yang, W.; Zhao, Y.; Liu, S. Effect of heat treatment on microstructure and dimensional stability of ZL114A aluminum alloy. Trans. Nonferrous. Met. Soc. China. 2010, 20, 2124-8.

33. Li, Y.; Zhang, G. W.; Xu, H.; Zhang, J. Q. Study on heat treatment process for ZL205A alloy (in Chinese). Foundry. Technol. 2017, 38, 68-70.

34. Xu, S.; Khanlari, K.; Lin, B.; et al. Effects of Fe-addition as a beneficial modifying element on the microstructure and mechanical properties of an Al–Si–Cu–Mg–Ni–Mn piston alloy. Metallogr. Microstruct. Anal. 2023, 12, 401-12.

35. Liao, H.; Xu, H.; Hu, Y. Effect of RE addition on solidification process and high-temperature strength of Al−12%Si−4%Cu−1.6%Mn heat-resistant alloy. Trans. Nonferrous. Met. Soc. China. 2019, 29, 1117-26.

36. Chen, J.; Liao, H.; Wu, Y.; Li, H. Contributions to high temperature strengthening from three types of heat-resistant phases formed during solidification, solution treatment and ageing treatment of Al-Cu-Mn-Ni alloys respectively. Mater. Sci. Eng. A. 2020, 772, 138819.

37. Li, G.; Liao, H.; Zheng, J.; et al. Synergistic effect of joint addition of Sb+Mn on high temperature strengthening in Al–4Cu heat-resistant alloy. Mater. Sci. Eng. A. 2022, 851, 143623.

38. Li, G.; Liao, H.; Zheng, J.; et al. Micro-alloying effects of Mn and Zr on the evolution of ageing precipitates and high temperature strength of Al-11.5Si–4Cu alloy after a long-time heat exposure. Mater. Sci. Eng. A. 2021, 828, 142121.

39. Chen, J.; Liao, H.; Xu, H.; Lejcek, P. Uneven precipitation behavior during the solutionizing course of Al‐Cu‐Mn alloys and their contribution to high temperature strength. Adv. Mater. Sci. Eng. 2018, 2018, 6741502.

40. Su, R.; Xiao, J.; Jia, Y.; Wang, K.; Qu, Y. Study on properties and microstructure of an Al–Cu–Mg–Fe–Ni alloy with two-stage aging treatment. Mater. Res. Express. 2019, 6, 126561.

41. Lin, B.; Zhang, W.; Zheng, X.; Zhao, Y.; Lou, Z.; Zhang, W. Developing high performance mechanical properties at elevated temperature in squeeze cast Al-Cu-Mn-Fe-Ni alloys. Mater. Charact. 2019, 150, 128-37.

42. Ritchie, R. O. The conflicts between strength and toughness. Nat. Mater. 2011, 10, 817-22.

43. Ma, Y.; Chen, H.; Zhang, M.; et al. Break through the strength-ductility trade-off dilemma in aluminum matrix composites via precipitation-assisted interface tailoring. Acta. Mater. 2023, 242, 118470.

44. Ma, Y.; Wang, Q.; Jiang, B.; et al. Controlled formation of coherent cuboidal nanoprecipitates in body-centered cubic high-entropy alloys based on Al2(Ni,Co,Fe,Cr)14 compositions. Acta. Mater. 2018, 147, 213-25.

45. Ma, Y.; Hao, J.; Jie, J.; Wang, Q.; Dong, C. Coherent precipitation and strengthening in a dual-phase AlNi2Co2Fe1.5Cr1.5 high-entropy alloy. Mater. Sci. Eng. A. 2019, 764, 138241.

46. Klenam, D.; Bamisaye, O.; Asumadu, T.; Bodunrin, M.; Soboyejo, W. Solving the strength-ductility trade-off using complex concentrated alloy design strategy: an overview. Smart. Mater. Manuf. 2025, 3, 100091.

47. Seidman, D. N.; Marquis, E. A.; Dunand, D. C. Precipitation strengthening at ambient and elevated temperatures of heat-treatable Al(Sc) alloys. Acta. Mater. 2002, 50, 4021-35.

48. Koju, R.; Mishin, Y. Atomistic study of grain-boundary segregation and grain-boundary diffusion in Al-Mg alloys. Acta. Mater. 2020, 201, 596-603.

49. Belov, N.; Alabin, A.; Matveeva, I. Optimization of phase composition of Al–Cu–Mn–Zr–Sc alloys for rolled products without requirement for solution treatment and quenching. J. Alloys. Compd. 2014, 583, 206-13.

50. Liao, H.; Tang, Y.; Suo, X.; et al. Dispersoid particles precipitated during the solutionizing course of Al-12 wt%Si-4 wt%Cu-1.2 wt%Mn alloy and their influence on high temperature strength. Mater. Sci. Eng. A. 2017, 699, 201-9.

51. Wolverton, C. Crystal structure and stability of complex precipitate phases in Al–Cu–Mg–(Si) and Al–Zn–Mg alloys. Acta. Mater. 2001, 49, 3129-42.

52. Gazizov, M.; Belyakov, A.; Holmestad, R.; et al. The coarsening behavior of strengthening particles in an Al–Cu–Mg–Ag alloy during creep. Mater. Sci. Eng. A. 2023, 884, 145515.

53. Chen, X. J.; Wang, B.; Wang, Z.; et al. Unveiling micromechanism of Fe minor addition‐induced property degradation of an Al‐5.1Cu‐0.65Mg‐0.8Mn (wt%) alloy. Rare. Metals. 2025, 44, 3496-513.

54. Lu, G.; Sun, B.; Wang, J.; Liu, Y.; Liu, C. High-temperature age-hardening behavior of Al–Mg–Si alloys with varying Sn contents. J. Mater. Res. Technol. 2021, 14, 2165-73.

55. Hu, K.; Zou, C.; Wang, H.; Wei, Z. The role of in-situ Al3Ti formed during solidification in improving the high-temperature properties of Al–Cu alloy. Mater. Sci. Eng. A. 2024, 902, 146585.

56. Bate, P. The effect of deformation on grain growth in Zener pinned systems. Acta. Mater. 2001, 49, 1453-61.

Journal of Materials Informatics
ISSN 2770-372X (Online)
Follow Us

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/