REFERENCES

1. Kim, Y.; Kim, K.; Song, Y.; Park, J. H.; Lee, K. 2.47 GPa grade ultra-strong 15Co-12Ni secondary hardening steel with superior ductility and fracture toughness. J. Mater. Sci. Technol. 2021, 66, 36-45.

2. Li, J.; Zhan, D.; Jiang, Z.; Zhang, H.; Yang, Y.; Zhang, Y. Progress on improving strength-toughness of ultra-high strength martensitic steels for aerospace applications: a review. J. Mater. Res. Technol. 2023, 23, 172-90.

3. Hammarberg, S.; Kajberg, J.; Larsson, S.; Jonsén, P. Ultra high strength steel sandwich for lightweight applications. SN. Appl. Sci. 2020, 2, 2773.

4. Zhao, J.; Jiang, Z. Thermomechanical processing of advanced high strength steels. Prog. Mater. Sci. 2018, 94, 174-242.

5. Hörhold, R.; Müller, M.; Merklein, M.; Meschut, G. Mechanical properties of an innovative shear-clinching technology for ultra-high-strength steel and aluminium in lightweight car body structures. Weld. World. 2016, 60, 613-20.

6. Hilditch, T. B.; de Souza, T.; Hodgson, P. D. 2 - Properties and automotive applications of advanced high-strength steels (AHSS). In Welding and joining of advanced high strength steels (AHSS). Elsevier; 2015. pp. 9-28.

7. Seede, R.; Shoukr, D.; Zhang, B.; et al. An ultra-high strength martensitic steel fabricated using selective laser melting additive manufacturing: densification, microstructure, and mechanical properties. Acta. Mater. 2020, 186, 199-214.

8. Gao, Y.; Liu, S.; Hu, X.; et al. A novel low cost 2000 MPa grade ultra-high strength steel with balanced strength and toughness. Mater. Sci. Eng. A. 2019, 759, 298-302.

9. Xue, J.; Zhang, Y.; Guo, W.; et al. Significantly improving mechanical properties of ultra-high strength steel joints via optimal in-situ post-weld heat treatment. J. Mater. Process. Technol. 2025, 337, 118741.

10. Chen, S.; Zhu, J.; Liu, T.; et al. Machine learning optimized by sparrow search for co-design of heat treatment process, microstructure, and properties in ultra-high-strength maraging steels. J. Mater. Res. Technol. 2025, 39, 8500-11.

11. Ramprasad, R.; Batra, R.; Pilania, G.; Mannodi-Kanakkithodi, A.; Kim, C. Machine learning in materials informatics: recent applications and prospects. npj. Comput. Mater. 2017, 3, 56.

12. Su, Y.; Fu, H.; Bai, Y.; Jiang, X.; Xie, J. Progress in materials genome engineering in China. Acta. Metall. Sin. 2020, 56, 1313-23.

13. Agrawal, A.; Choudhary, A. Perspective: Materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL. Mater. 2016, 4, 053208.

14. Yang, R. X.; McCandler, C. A.; Andriuc, O.; et al. Big data in a nano world: a review on computational, data-driven design of nanomaterials structures, properties, and synthesis. ACS. Nano. 2022, 16, 19873-91.

15. Merayo, D.; Rodriguez-Prieto, A.; Camacho, A. M. Prediction of physical and mechanical properties for metallic materials selection using big data and artificial neural networks. IEEE. Access. 2020, 8, 13444-56.

16. Guo, S.; Yu, J.; Liu, X.; Wang, C.; Jiang, Q. A predicting model for properties of steel using the industrial big data based on machine learning. Comput. Mater. Sci. 2019, 160, 95-104.

17. Yang, Y.; Zhao, L.; Han, C.; et al. Taking materials dynamics to new extremes using machine learning interatomic potentials. J. Mater. Inf. 2021, 1, 10.

18. He, L.; Chen, X.; Wang, Z. Study on the penetration performance of concept projectile for high-speed penetration (CPHP). Int. J. Impact. Eng. 2016, 94, 1-12.

19. Kılıç, N.; Bedir, S.; Erdik, A.; Ekici, B.; Taşdemirci, A.; Güden, M. Ballistic behavior of high hardness perforated armor plates against 7.62 mm armor piercing projectile. Mater. Design. 2014, 63, 427-38.

20. Børvik, T.; Dey, S.; Olovsson, L. Penetration of granular materials by small-arms bullets. Int. J. Impact. Eng. 2015, 75, 123-39.

21. Manes, A.; Serpellini, F.; Pagani, M.; Saponara, M.; Giglio, M. Perforation and penetration of aluminium target plates by armour piercing bullets. Int. J. Impact. Eng. 2014, 69, 39-54.

22. Zhu, F.; Chen, Y.; Zhu, G. Numerical simulation study on penetration performance of depleted Uranium (DU) alloy fragments. Def. Technol. 2021, 17, 50-5.

23. Mao, L.; Zhao, W.; Liu, C.; Pang, Z.; Du, Z. Fracture and damage evolution of metal molybdenum based on a modified Johnson–Cook model under high-temperature conditions. Int. J. Impact. Eng. 2026, 212, 105663.

24. Huang, Z.; Gao, L.; Wang, Y.; Wang, F. Determination of the Johnson-Cook constitutive model parameters of materials by cluster global optimization algorithm. J. Mater. Eng. Perform. 2016, 25, 4099-107.

25. May, R. M. Simple mathematical models with very complicated dynamics. Nature 1976, 261, 459-67.

26. Geisel, T.; Fairen, V. Statistical properties of chaos in Chebyshev maps. Phys. Lett. A. 1984, 105, 263-6.

27. He, D.; He, C.; Jiang, L. G.; Zhu, H. W.; Hu, G. R. Chaotic characteristics of a one-dimensional iterative map with infinite collapses. IEEE. Trans. Circuits. Syst. I. Fundam. Theory. Appl. 2001, 48, 900-6.

28. Yang, D.; Li, G.; Cheng, G. On the efficiency of chaos optimization algorithms for global optimization. Chaos. Solitons. Fractals. 2007, 34, 1366-75.

29. Shrot, A.; Bäker, M. Determination of Johnson-Cook parameters from machining simulations. Comput. Mater. Sci. 2012, 52, 298-304.

30. Zeng, Z.; Liang, W.; Wang, T.; Hong, Z.; Chang, Q.; Yang, S. A method for predicting the capacity of lithium-ion batteries based on Pearson correlation coefficient-guided multi-objective particle swarm optimization. Comput. Ind. Eng. 2025, 210, 111514.

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/