REFERENCES

1. Erden, M. A.; Aydın, F. Wear and mechanical properties of carburized AISI 8620 steel produced by powder metallurgy. Int. J. Miner. Metall. Mater. 2021, 28, 430-9.

2. Xue, H.; Peng, W.; Yu, L.; et al. Effect of hardenability on microstructure and property of low alloy abrasion-resistant steel. Mater. Sci. Eng. A. 2020, 793, 139901.

3. Di Schino, A.; Emilio Di Nunzio, P.; Maria Cabrera, J. Effect of quenching & partitioning process on a low carbon steel. Adv. Mater. Lett. 2017, 8, 641-4.

4. Jominy, W.; Boegehold, A. L. A hardenability test for carburizing steel. Trans. ASM. 1938, 26, 574-606.

5. Tenaglia, N. E.; Boeri, R. E.; Massone, J. M.; Basso, A. D. Assessment of the austemperability of high-silicon cast steels through Jominy hardenability tests. Mater. Sci. Technol. 2018, 34, 1990-2000.

6. Çakir, M.; Özsoy, A. Investigation of the correlation between thermal properties and hardenability of Jominy bars quenched with air–water mixture for AISI 1050 steel. Mater. Design. 2011, 32, 3099-105.

7. Newkirk, J.; Mackenzie, D. The Jominy end quench for light-weight alloy development. J. Mater. Eng. Perform. 2000, 9, 408-15.

8. Hömberg, D. A numerical simulation of the Jominy end-quench test. Acta. Mater. 1996, 44, 4375-85.

9. Zhu, D.; Wang, B.; Zhao, H.; et al. Enhanced hardenability prediction in 20CrMo special steel via XGBoost model. J. Iron Steel Res. Int. 2025, 32, 1023-33.

10. Jin, M.; Lian, J.; Jiang, Z. New method for prediction of Jominy curve of structural steel. Acta. Metall. Sin. 2006, 42, 405-10. https://www.ams.org.cn/EN/Y2006/V42/I4/405. (accessed on 6 Aug 2026).

11. Geng, X.; Cheng, Z.; Wang, S.; et al. A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design. J. Mater. Sci. 2022, 57, 10755-68.

12. Shang, C.; Zhu, D.; Wu, H.; et al. A quantitative relation for the ductile-brittle transition temperature in pipeline steel. Scr. Mater. 2024, 244, 116023.

13. Fu, Y.; Zhu, D.; Wu, H.; et al. Predicting the yield strength ratio of spring steels via machine learning with experimental validation. cScience 2026, 2, e70024.

14. Song, L.; Wang, C.; Li, Y.; Wei, X. Predicting stacking fault energy in austenitic stainless steels via physical metallurgy-based machine learning approaches. J. Mater. Inf. 2025, 5, 2.

15. Jiang, J.; Hu, L.; Hu, C.; Liu, J.; Wang, Z. BACombo - bandwidth-aware decentralized federated learning. Electronics 2020, 9, 440.

16. Hao, M.; Li, H.; Luo, X.; Xu, G.; Yang, H.; Liu, S. Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE. Trans. Ind. Inf. 2020, 16, 6532-42.

17. da Silveira Dib, M. A.; Prates, P.; Ribeiro, B. SecFL - secure federated learning framework for predicting defects in sheet metal forming under variability. Expert. Syst. Appl. 2024, 235, 121139.

18. Chakraborty, S.; Guha, S.; Mishra, D.; Pal, S. K. Federated learning for weld quality prediction. J. Dyn. Monit. Diagn. 2024, 3, 237-45.

19. Wu, H.; Li, H.; Chi, H.; Kou, W.; Wu, Y.; Wang, S. A hierarchical federated learning framework for collaborative quality defect inspection in construction. Eng. Appl. Artif. Intell. 2024, 133, 108218.

20. Shang, C.; Jiang, T.; Wu, H.; et al. Efficient design of hydrogen-resistant ultra-high-strength steels via active learning and multiscale characterization. Corros. Commun. 2026, 21, 60-72.

21. Xu, M.; Xing, S.; Hong, J.; et al. A hybrid deep learning model for robust and data-efficient lithium-ion battery remaining useful life prediction. J. Mater. Inf. 2026, 6, 26.

22. McMahan, H. B.; Moore, E.; Ramage, D.; Hampson, S.; Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. arXiv 2016, arXiv:1602.05629. Available online: https://doi.org/10.48550/arXiv.1602.05629. (accessed on 6 Aug 2026).

23. Yang, Q.; Liu, Y.; Chen, T.; Tong, Y. Federated machine learning: concept and applications. ACM. Trans. Intell. Syst. Technol. 2019, 10, 1-19.

24. Hao, C.; Kuai, P.; Duan, J.; et al. Machine-learning-enabled composition–process co-design of heat-resistant cast aluminum alloys with superior elevated-temperature strength. J. Mater. Inf. 2026, 6, 38.

25. 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.

26. Shen, C.; Wang, C.; Wei, X.; Li, Y.; van der Zwaag, S.; Xu, W. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel. Acta. Mater. 2019, 179, 201-14.

27. Li, A.; Zhang, L.; Wang, J.; Han, F.; Li, X. Privacy-preserving efficient federated-learning model debugging. IEEE. Trans. Parallel. Distrib. Syst. 2022, 33, 2291-303.

28. Li, T.; Sahu, A. K.; Talwalkar, A.; Smith, V. Federated learning: challenges, methods, and future directions. IEEE. Signal. Process. Mag. 2020, 37, 50-60.

29. Rieke, N.; Hancox, J.; Li, W.; et al. The future of digital health with federated learning. NPJ. Digit. Med. 2020, 3, 119.

30. Zheng, Z.; Zhou, Y.; Sun, Y.; Wang, Z.; Liu, B.; Li, K. Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. arXiv 2021, arXiv:2102.01375. Available online: https://doi.org/10.48550/arXiv.2102.01375. (accessed on 6 Aug 2026).

31. Zhou, H.; Yang, G.; Dai, H.; Liu, G. PFLF: privacy-preserving federated learning framework for edge computing. IEEE. Trans. Inform. Forensic. Secur. 2022, 17, 1905-18.

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/