REFERENCES
1. Zhang, Q.; Wang, Y. Research on mechanical property prediction of hot rolled steel based on lightweight multi-branch convolutional neural network. Mater. Today. Commun. 2023, 37, 107445.
2. Liu, S.; Long, M.; Zhang, S.; et al. Study on the prediction of tensile strength and phase transition for ultra-high strength hot stamping steel. J. Mater. Res. Technol. 2020, 9, 14244-53.
3. dos Santos, A. A.; Barbosa, R. Model for microstructure prediction in hot strip rolled steels. Steel. Res. Int. 2010, 81, 55-63.
4. Han, H. N.; Lee, J. K.; Kim, H. J.; Jin, Y. A model for deformation, temperature and phase transformation behavior of steels on run-out table in hot strip mill. J. Mater. Process. Technol. 2002, 128, 216-25.
5. Zhang, X.; Jiang, Z.; Tieu, A.; Liu, X.; Wang, G. Numerical modelling of the thermal deformation of CVC roll in hot strip rolling. J. Mater. Process. Technol. 2002, 130-1, 219-23.
6. Militzer, M.; Hawbolt, E. B.; Meadowcroft, T. R. Microstructural model for hot strip rolling of high-strength low-alloy steels. Metall. Mater. Trans. A. 2000, 31, 1247-59.
7. Wei, X.; van, Z. S.; Jia, Z.; Wang, C.; Xu, W. On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets. Acta. Mater. 2022, 235, 118103.
8. Lee, J.; Kim, M.; Lee, Y. Design of high strength medium-Mn steel using machine learning. Mater. Sci. Eng. A. 2022, 843, 143148.
9. Abd-Elaziem, W.; Elkatatny, S.; Sebaey, T. A.; Darwish, M. A.; Abd, E. M. A.; hamada, A. Machine learning for advancing laser powder bed fusion of stainless steel. J. Mater. Res. Technol. 2024, 30, 4986-5016.
10. Geng, X.; Wang, F.; Wu, H.; et al. Data-driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology. MGE. Advances. 2023, 1, e10.
11. Zhu, L.; Luo, Q.; Chen, Q.; et al. Prediction of ultimate tensile strength of Al-Si alloys based on multimodal fusion learning. MGE. Advances. 2024, 2, e26.
12. Shi, Z.; Du, L.; He, X.; et al. Prediction model of yield strength of V–N steel hot-rolled plate based on machine learning algorithm. JOM. 2023, 75, 1750-62.
13. Xu, G.; He, J.; Lü, Z.; Li, M.; Xu, J. Prediction of mechanical properties for deep drawing steel by deep learning. Int. J. Miner. Metall. Mater. 2023, 30, 156-65.
14. He, X.; Zhou, X.; Tian, T.; Li, W. Prediction of mechanical properties of hot rolled strips with generalized RBFNN and composite expectile regression. IEEE. Access. 2022, 10, 106534-42.
15. Xu, Z.; Liu, X.; Zhang, K. Mechanical properties prediction for hot rolled alloy steel using convolutional neural network. IEEE. Access. 2019, 7, 47068-78.
16. Jiang, X.; Jia, B.; Zhang, G.; et al. A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data. Scr. Mater. 2020, 186, 272-7.
17. 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.
18. Li, W.; Xie, L.; Zhao, Y.; Li, Z.; Wang, W. Prediction model for mechanical properties of hot-rolled strips by deep learning. J. Iron. Steel. Res. Int. 2020, 27, 1045-53.
19. Xie, Q.; Suvarna, M.; Li, J.; Zhu, X.; Cai, J.; Wang, X. Online prediction of mechanical properties of hot rolled steel plate using machine learning. Mater. Design. 2021, 197, 109201.
20. Yang, Z.; Wang, Y.; Xu, F.; et al. Online prediction of mechanical properties of the hot rolled steel plate using time-series deep neural network. ISIJ. Int. 2023, 63, 746-57.
21. Cui, C.; Cao, G.; Li, X.; Gao, Z.; Liu, J.; Liu, Z. A strategy combining machine learning and physical metallurgical principles to predict mechanical properties for hot rolled Ti micro-alloyed steels. J. Mater. Process. Technol. 2023, 311, 117810.
22. Li, F.; He, A.; Song, Y.; et al. Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems. Int. J. Miner. Metall. Mater. 2023, 30, 1093-103.
23. Li, H.; Li, Y.; Huang, J.; et al. Physical metallurgy guided industrial big data analysis system with data classification and property prediction. Steel. Res. Int. 2022, 93, 2100820.
24. Thomas, A.; Durmaz, A. R.; Alam, M.; Gumbsch, P.; Sack, H.; Eberl, C. Materials fatigue prediction using graph neural networks on microstructure representations. Sci. Rep. 2023, 13, 12562.
25. Dai, M.; Demirel, M. F.; Liang, Y.; Hu, J. Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials. npj. Comput. Mater. 2021, 7, 574.
26. Sadeghpour, E.; Nonn, A. Data-driven models for structure-property prediction in additively manufactured steels. Comput. Mater. Sci. 2022, 215, 111782.
27. Karimi, K.; Salmenjoki, H.; Mulewska, K.; et al. Prediction of steel nanohardness by using graph neural networks on surface polycrystallinity maps. Scr. Mater. 2023, 234, 115559.
28. Li, Y.; Wang, C.; Zhang, Y.; et al. Thermodynamically informed graph for interpretable and extensible machine learning: martensite start temperature prediction. Calphad 2024, 85, 102710.
29. Shi, X.; Zhou, L.; Huang, Y.; Wu, Y.; Hong, Z. A review on the applications of graph neural networks in materials science at the atomic scale. MGE. Advances. 2024, 2, e50.
30. Celebi, M. E.; Kingravi, H. A.; Vela, P. A. A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert. Syst. Appl. 2013, 40, 200-10.
31. Liu, Y.; Wu, J.; Wang, Z.; et al. Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning. Acta. Mater. 2020, 195, 454-67.
32. Pourbahrami, S.; Balafar, M. A.; Khanli, L. M.; Kakarash, Z. A. A survey of neighborhood construction algorithms for clustering and classifying data points. Comput. Sci. Rev. 2020, 38, 100315.
33. Hussain, S. F.; Haris, M. A k-means based co-clustering (kCC) algorithm for sparse, high dimensional data. Expert. Syst. Appl. 2019, 118, 20-34.
34. Tzortzis, G.; Likas, A. The MinMax k-Means clustering algorithm. Pattern. Recogn. 2014, 47, 2505-16.
