
Journal of Northeastern University(Natural Science) ›› 2025, Vol. 46 ›› Issue (8): 11-19.DOI: 10.12068/j.issn.1005-3026.2025.20250092
• Overview • Previous Articles Next Articles
Si-wei WU, Xiao-guang ZHOU, Zhen-yu LIU, Guo-dong WANG
Received:2025-07-23
Online:2025-08-15
Published:2025-11-24
Contact:
Zhen-yu LIU
CLC Number:
Si-wei WU, Xiao-guang ZHOU, Zhen-yu LIU, Guo-dong WANG. Microstructure and Property Prediction of Hot-Rolled Steel: Development and Prospects from Physical Models to Human-Machine Hybrid Intelligence[J]. Journal of Northeastern University(Natural Science), 2025, 46(8): 11-19.
Fig.6 Comparison of temperature field before and after process optimization of high-strength ship plate steel (w(C):0.17%;w(Si):0.28%;w(Mn):1.45%;thickness:20 mm)
| [1] | 丁敬国, 金利, 孙丽荣, 等. 板带热轧过程智能化建模方法的研究现状与展望[J]. 冶金自动化, 2022, 46(6): 25-37. |
| Ding Jing-guo, Jin Li, Sun Li-rong, et al. Research status and prospect of intelligent modeling method for hot strip rolling process [J]. Metallurgical Industry Automation, 2022, 46(6): 25-37. | |
| [2] | 孙一康. 带钢热连轧的模型与控制[M]. 北京: 冶金工业出版社, 2002. |
| Sun Yi-kang. Model and control of hot strip rolling [M]. Beijing: Metallurgical Industry Press, 2002. | |
| [3] | 刘玠, 孙一康. 带钢热连轧计算机控制[M]. 北京: 机械工业出版社, 1997. |
| Liu Jie, Sun Yi-kang. Computer control of hot strip rolling [M]. Beijing: China Machine Press, 1997. | |
| [4] | 刘振宇, 曹光明, 周晓光, 等. 组织性能预测技术及其在智能热轧中的核心作用[J]. 轧钢, 2019, 36(2): 1-7. |
| Liu Zhen-yu, Cao Guang-ming, Zhou Xiao-guang, et al. The predicting technologies for microstructure and properties and their core roles in smart hot rolling processes [J]. Steel Rolling, 2019, 36(2): 1-7. | |
| [5] | Ma L Q, Yuan X Q, Jiao S H, et al. Modeling of dynamic recrystallization and flow stress of Nb-bearing steels[J]. Multidiscipline Modeling in Materials and Structures, 2007, 3(1): 27-41. |
| [6] | Siciliano F J, Minami K, Maccagno T M, et al. Mathematical modeling of the mean flow stress, fractional softening and grain size during the hot strip rolling of C-Mn steels[J]. ISIJ International, 1996, 36(12): 1500-1506. |
| [7] | Martin H, Amoako-Yirenkyi P, Pohjonen A, et al. Statistical modeling for prediction of CCT diagrams of steels involving interaction of alloying elements[J]. Metallurgical and Materials Transactions B, 2021, 52(1): 223-235. |
| [8] | Van Bohemen S M C, Sietsma J. Modeling of isothermal bainite formation based on the nucleation kinetics[J]. International Journal of Materials Research, 2008, 99(7): 739-747. |
| [9] | Sellars C M, Whiteman J A. Recrystallization and grain growth in hot rolling[J]. Metal Science, 1979, 13(3/4): 187-194. |
| [10] | Sellars C M. The kinetics of softening processes during hot working of austenite[J]. Czechoslovak Journal of Physics B, 1985, 35(3): 239-248. |
| [11] | Wang L, Ji L K, Yang K, et al. The flow stress-strain and dynamic recrystallization kinetics behavior of high-grade pipeline steels[J]. Materials, 2022, 15(20): 7356. |
| [12] | Zurob H S, Hutchinson C R, Brechet Y, et al. Rationalization of the softening and recrystallization behaviour of microalloyed austenite using mechanism maps[J]. Materials Science and Engineering: A, 2004, 382(1/2): 64-81. |
| [13] | Zurob H S, Hutchinson C R, Brechet Y, et al. Modeling recrystallization of microalloyed austenite: effect of coupling recovery, precipitation and recrystallization[J]. Acta Materialia, 2002, 50(12): 3077-3094. |
| [14] | Collins J, Piemonte M, Taylor M, et al. A rapid, open-source CCT predictor for low-alloy steels, and its application to compositionally heterogeneous material[J]. Metals, 2023, 13(7): 1168. |
| [15] | Zhang S H, Deng L, Che L Z. An integrated model of rolling force for extra-thick plate by combining theoretical model and neural network model[J]. Journal of Manufacturing Processes, 2022, 75: 100-109. |
| [16] | Shen S H, Guye D, Ma X P, et al. Multistep networks for roll force prediction in hot strip rolling mill[J]. Machine Learning with Applications, 2022, 7: 100245. |
| [17] | 李元, 刘文仲, 孙一康. 神经元网络在热连轧精轧机组轧制力预报的应用[J]. 钢铁, 1996(1): 54-57, 39. |
| Li Yuan, Liu Wen-zhong, Sun Yi-kang. Application of neural network to predicting rolling force for the finisher [J]. Iron and Steel, 1996(1): 54-57, 39. | |
| [18] | Dong Z S, Li X, Luan F, et al. Fusion of theory and data-driven model in hot plate rolling: a case study of rolling force prediction[J]. Expert Systems with Applications, 2024, 245: 123047. |
| [19] | Wang Q N, Song L B, Zhao J W, et al. Application of the gradient boosting decision tree in the online prediction of rolling force in hot rolling[J]. The International Journal of Advanced Manufacturing Technology, 2023, 125(1/2): 387-397. |
| [20] | Rahaman M, Mu W Z, Odqvist J, et al. Machine learning to predict the martensite start temperature in steels[J]. Metallurgical and Materials Transactions A, 2019, 50(5): 2081-2091. |
| [21] | Pattanayak S, Dey S, Chatterjee S, et al. Computational intelligence based designing of microalloyed pipeline steel[J]. Computational Materials Science, 2015, 104: 60-68. |
| [22] | Hu X B, Li H, Liu C, et al. Multi-objective design of Ni-B-Al master alloy by adaptive machine learning-driven aluminothermic reduction experiment[J]. Journal of Alloys and Compounds, 2025, 1010: 177403. |
| [23] | Conrad F, Stöcker J P, Signorini C, et al. Exploring design space: machine learning for multi-objective materials design optimization with enhanced evaluation strategies[J]. Computational Materials Science, 2025, 246: 113432. |
| [24] | Wu S W, Zhou X G, Ren J K, et al. Optimal design of hot rolling process for C-Mn steel by combining industrial data-driven model and multi-objective optimization algorithm[J]. Journal of Iron and Steel Research International, 2018, 25(7): 700-705. |
| [25] | Pan G F, Wang F Y, Shang C L, et al. Advances in machine learning-and artificial intelligence-assisted material design of steels[J]. International Journal of Minerals, Metallurgy and Materials, 2023, 30(6): 1003-1024. |
| [26] | Wang X J, Li X, Yuan H, et al. Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning[J]. Materials Today Communications, 2024, 40: 109997. |
| [27] | Song K, Yan F, Ding T, et al. A steel property optimization model based on the XGBoost algorithm and improved PSO[J]. Computational Materials Science, 2020, 174: 109472. |
| [28] | Diao Y P, Yan L C, Gao K W. A strategy assisted machine learning to process multi-objective optimization for improving mechanical properties of carbon steels[J]. Journal of Materials Science & Technology, 2022, 109: 86-93. |
| [29] | Jiang X, Jia B R, Zhang G F, et al. A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data[J]. Scripta Materialia, 2020, 186: 272-277. |
| [30] | Li H W, Li Y, Huang J, et al. Physical metallurgy guided industrial big data analysis system with data classification and property prediction[J]. Steel Research International, 2022, 93(8): 2100820. |
| [31] | 任鹏帆, 王振华, 贾燚, 等. 机理和数据融合的304不锈钢极薄带轧制力模型[J]. 钢铁, 2024, 59(10): 64-76. |
| Ren Peng-fan, Wang Zhen-hua, Jia Yi, et al. Rolling force model for 304 stainless steel ultra-thin strip based on mechanism and data fusion[J]. Iron & Steel, 2024, 59(10): 64-76. | |
| [32] | 李鑫, 周晓光, 曹光明, 等.融合物理冶金学与机器学习的组织性能预测及热轧工艺优化[J]. 冶金自动化, 2023, 47(2): 16-26. |
| Li Xin, Zhou Xiao-guang, Cao Guang-ming, et al. Microstructure and properties prediction and optimization of hot rolling process based on physical metallurgy and machine learning[J]. Metallurgical Industry Automation, 2023, 47(2): 16-26. | |
| [33] | 吴思炜. 基于工业大数据的热轧带钢组织性能预测与优化技术研究[D]. 沈阳: 东北大学, 2018. |
| Wu Si-wei. Research on microstructure and property prediction and optimization technology of hot rolled strips based on industrial big data[D]. Shenyang: Northeastern University, 2018. | |
| [34] | Li X, Zhou X G, Cao G M, et al. Machine learning hot deformation behavior of Nb micro-alloyed steels and its extrapolation to dynamic recrystallization kinetics[J]. Metallurgical and Materials Transactions A, 2021, 52(7): 3171-3181. |
| [35] | Jiang L, Fu H D, Zhang H T, et al. Physical mechanism interpretation of polycrystalline metals’ yield strength via a data-driven method: a novel Hall-Petch relationship[J]. Acta Materialia, 2022, 231: 117868. |
| [36] | Zhang X C, Gong J G, Xuan F Z. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures[J]. Engineering Fracture Mechanics, 2021, 258: 108130. |
| [37] | 王毅, 李高楠, 刘哲, 等. 材料基因工程与智能科学:AI+时代无尽前沿[J]. 科技导报, 2025, 43(12): 93-109. |
| Wang Yi, Li Gao-nan, Liu Zhe, et al. Materials genome engineering and intelligent science: the endless frontier in AI+ era [J]. Science & Technology Review, 2025, 43(12): 93-109. | |
| [38] | Li Y F, Wang Z H, Zhang L Y, et al. Arrhenius-type constitutive model and dynamic recrystallization behavior of V-5Cr-5Ti alloy during hot compression[J]. Transactions of Nonferrous Metals Society of China, 2015, 25(6): 1889-1900. |
| [39] | Maccagno T M, Jonas J J, Hodgson P D. Spreadsheet modelling of grain size evolution during rod rolling[J]. ISIJ International, 1996, 36(6): 720-728. |
| [40] | Gao Z W, Wu S W, Li X, et al. Modelling strain-induced precipitation kinetics of Nb (C, N) by symbolic regression machine learning[J]. Journal of Materials Research and Technology, 2025, 35: 1712-1721. |
| [41] | Cui C Y, Wang H, Gao X Y, et al. Machine learning model for thickness evolution of oxide scale during hot strip rolling of steels[J]. Metallurgical and Materials Transactions A, 2021, 52(9): 4112-4124. |
| [42] | Cao Y, Cao G M, Cui C Y, et al. Modeling continuous cooling transformations for HSLA steels with physical metallurgy guided hereditary machine learning[J]. Metallurgical and Materials Transactions A, 2023, 54(12): 4891-4904. |
| [43] | Cao Y, Wu S W, Tang S, et al. Dynamic deep learning to predict mechanical properties of high-strength low-alloy steels[J]. Metallurgical and Materials Transactions A, 2025, 56(1): 168-179. |
| [44] | Choudhary A, Kumar M, Unune D R. Experimental investigation and optimization of weld bead characteristics during submerged arc welding of AISI 1023 steel [J]. Defence Technology, 2019, 15(1): 72-82. |
| [45] | Rao V D P, Ali S R S M, Ali S M Z M S, et al. Multi-objective optimization of cutting parameters in CNC turning of stainless steel 304 with TiAlN nano coated tool [J]. Materials Today: Proceedings, 2018, 5(12): 25789-25797. |
| [46] | Wu S W, Cao G M, Zhou X G, et al. High dimensional data-driven optimal design for hot strip rolling of C-Mn steels[J]. ISIJ International, 2017, 57(7): 1213-1220. |
| [47] | 崔春圆. 热轧板带材集成机器学习模型开发与应用[D]. 沈阳: 东北大学, 2023. |
| Cui Chun-yuan. The development and application of integrated machine learning models for hot rolled plate and strip [D]. Shenyang: Northeastern University, 2023. | |
| [48] | Cao Y, Zhang C D, Tang S, et al. Machine learning to predict phase transformation products and their morphologies-application in design of lean high strength steel[J]. Materials & Design, 2025, 258: 114642. |
| [1] | ZHU Qing-feng, YAN Bo, FENG Zhi-xin, ZUO Yu-bo. Numerical Simulation and Experimental Investigation on Hot Rolling Process of 2195 Aluminum Alloy at Different Speeds [J]. Journal of Northeastern University(Natural Science), 2023, 44(4): 502-509. |
| [2] | LI Tian-xiang, LI Hai-jun, WANG Zhao-dong, WANG Guo-dong. Numerical Simulation of Shrinkage Porosities and Surface Cracks of Slab with Hot-Core Heavy Reduction Rolling [J]. Journal of Northeastern University(Natural Science), 2021, 42(7): 913-919. |
| [3] | REN Jia-kuan, YAN Dong, CHEN Jun, LIU Zhen-yu. Effect of QT Treatment on Microstructure and Obdurability of a B-Nb Low Carbon Bainite Steel [J]. Journal of Northeastern University Natural Science, 2019, 40(11): 1561-1567. |
| [4] | PENG Wen, JI Ya-feng, CHEN Xiao-rui, ZHANG Dian-hua. Optimization of Rolling Force Self-learning Model in Unsteady Process of Hot Rolling [J]. Journal of Northeastern University Natural Science, 2019, 40(10): 1408-1412. |
| [5] | CHEN Qi-yuan, ZHOU Xiao-guang, LIU Zhen-yu, WU Si-wei. Microstructure and Properties of Ti Microalloyed Automobile Frame Steel 510L [J]. Journal of Northeastern University Natural Science, 2018, 39(3): 339-344. |
| [6] | LI Cheng-ning, YUAN Guo, KANG Jian, WANG Guo-dong. Effect of Asymmetric Hot Rolling on Microstructure and Mechanical Properties in Low Alloy Steel [J]. Journal of Northeastern University Natural Science, 2017, 38(7): 941-945. |
| [7] | PENG Wen, MA Geng-sheng, GONG Dian-yao, ZHANG Dian-hua. Prediction Method of Rough Rolling Thickness Based on the Soft Sensor Model [J]. Journal of Northeastern University Natural Science, 2017, 38(3): 366-369. |
| [8] | SHEN Xin-jun, TANG Shuai, YANG Xiao-long, WANG Guo-dong. Simulation of Hot Rolling Texture Under Plane Strain Condition by Thermo-mechanical Simulator [J]. Journal of Northeastern University Natural Science, 2016, 37(8): 1104-1107. |
| [9] | PENG Wen, MA Geng-sheng, CAO Jian-zhao, ZHANG Dian-hua. Adaptive Strategy of Short Stroke Control in Tandem Hot Rolling [J]. Journal of Northeastern University Natural Science, 2016, 37(3): 343-346. |
| [10] | HU Zhi-ping, XU Yun-bo, TAN Xiao-dong. Intercritical Austenite Stabilization of a Mn-Al TRIP Steel [J]. Journal of Northeastern University Natural Science, 2016, 37(2): 179-183. |
| [11] | KANG Jian, ZHAO Jin-hua, WANG Xue-qiang, DI Hong-shuang. Hot Rolling Process and Microstructures of X70 Pipeline Steel Under Ultra-Fast Cooling [J]. Journal of Northeastern University Natural Science, 2015, 36(11): 1576-1580. |
| [12] | LU Ri-huan, LIU Xiang-hua, YAN Shu, LIU Li-zhong. Mechanism of Microstructure Evolution in Low Carbon Steels by Flash Processing [J]. Journal of Northeastern University Natural Science, 2015, 36(11): 1567-1571. |
| [13] | SHEN Xinjun, PEI Xinhua, TANG Shuai, WANG Guodong. Effects of Coiling Temperature on Microstructure and Mechanical Properties of HotRolled Ferrite/Bainite Dual Phase Steel〓 [J]. Journal of Northeastern University Natural Science, 2014, 35(8): 1120-1123. |
| [14] | XIE Hui, DU Linxiu, HU Jun. Effects of Cooling Parameters on Microstructures and Mechanical Properties of Hot Rolled TiMicroalloyed LowCarbon Ultra HighStrength Steels [J]. Journal of Northeastern University Natural Science, 2014, 35(4): 508-511. |
| [15] | CHEN Zejun, LI Hongtu, ZHANG Jing, HUANG Guangjie. Simulation on Effect of Electric Induction Reheating on Microstructure of Hot Rolled Low Alloy Steel [J]. Journal of Northeastern University Natural Science, 2014, 35(10): 1427-1431. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||