Transactions of Materials and Heat Treatment

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ClassificationCode：TG335

year,vol(issue):pagenumber：2024,49(2):172-181

Abstract：

The prediction of rolling force is an important prerequisite for the smooth running of hot rolling process. Therefore, in order to predict the rolling force in the hot rolling process, the single-pass hot compression test of 07MnNiMoDR steel was conducted by thermal simulation test machine to obtain the high temperature rheological behavior under the conditions of the deformation temperature of 850-1150 ℃, the strain rate of 0.1-10 s-1 and the true strain of 0.9. Then, the dynamic recrystallization and dynamic recovery behavior of materials under different deformation conditions were analyzed, and based on Arrhenius equation and deformation mechanism, the hot rolling constitutive equation of experimental steel was established. Furthermore, the temperature field parameters of 07MnNiMoDR steel were calculated by the material property analysis software JMatPro, and based on the high temperature rolling constitutive equation and temperature parameters, the prediction accuracy of rolling force was verified by software Deform-3D, The results show that established constitutive equation and temperature parameters of 07MnNiMoDR steel have good prediction accuracy for predicting rolling force with an R value of 0.9875.

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作者简介：汪后明（1982-），男，硕士，工程师

Reference：

［1］王振华, 刘元铭, 王涛, 等. 粗轧过程中轧制力和宽展的预测与分析
［J］. 钢铁, 2022, 57(9): 95-102.

Wang Z H, Liu Y M, Wang T, et al. Prediction and analysis of rolling force and width spread in rough rolling
［J］. Iron & Steel, 2022, 57(9): 95-102.

［2］张书荣. 六辊可逆冷轧机全轧程轧制力预测的方法研究
［D］. 西安:西安理工大学, 2021.

Zhang S R . Research on Rolling Force Prediction Method of Sixhigh Reversible Cold Rolling mill
［D］.Xi′an:Xi′an University of Technology, 2021.

［3］Bagheripoor M, Bisadi H. Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process
［J］. Applied Mathematical Modelling, 2013, 37(7): 593-607.

［4］罗晓东, 柳浩, 阳辉, 等. 轧辊转速对AZ31环件轧制工艺的影响规律研究
［J］. 兵器材料科学与工程, 2015, 38(6): 61-64.

Luo X D, Liu H, Yang H, et al. Effect of roll speed on radialaxial rolling process of AZ31 ring
［J］. Ordnance Material Science and Engineering, 2015, 38(6): 61-64.

［5］张坚, 双远华, 胡建华, 等. 基于改进的BP神经网络无缝钢管连轧轧制力的预测
［J］. 锻压技术, 2022, 47(5): 153- 160.

Zhang J, Shuang Y H, Hu J H, et al. Prediction on rolling force in hot rolling of wide and thick plate based on deep learning
［J］. Forging & Stamping Technology, 2022, 47(5): 153-160.

［6］郭金涛, 王龙, 余建波, 等. 基于深度学习的宽厚板热轧轧制力预测
［J］. 锻压技术, 2022, 47(7): 167- 174.

Guo J T, Wang L, Yu J B, et al. Prediction on rolling force in hot rolling of wide and thick plate based on deep learning
［J］. Forging & Stamping Technology, 2022, 47(7): 167- 174.

［7］马威, 李维刚, 赵云涛, 等. 基于深度学习的热连轧轧制力预测
［J］. 钢铁研究学报, 2019, 31(9): 805-815.

Ma W, Li W G, Zhao Y T, et al. Prediction of hotrolled roll force based on deep learning
［J］. Journal of Iron and Steel Research, 2019, 31(9): 805-815.

［8］吕明桦. 大型环件轧制成形模拟与试验研究
［D］.济南:山东建筑大学, 2019.

Lyu M H. Simulation and Experimental Study on Rolling Forming of Large Ring
［D］. Jinan:Shandong Jianzhu University, 2019.

［9］He Q Q, Sun J, Zhao J Y, et al. 3D simulation of hbeam multipass hot rolling and microstructure evolution
［J］. Applied Mechanics & Materials, 2013, 268-270:297-300.

［10］Dema R R, Shapovalov A N, Alontsev V V, et al. Computer simulation and research of the hot rolling process in Deform-3D
［J］. Materials Today: Proceedings, 2019, 19(23): 12-15.

［11］汪建武, 李淑香, 金彪, 等. 6082铝合金汽车后上控制臂楔横轧轴坯成形模拟研究
［J］. 兵器材料科学与工程, 2022, 45(6): 70-74.

Wang J W, Li S X, Jin B, et al. Forming of simulation 6082 aluminum alloy cross wedge rolling shaft for automobile rear upper control ararm
［J］. Ordnance Material Science and Engineering, 2022, 45(6): 70-74.

［12］Kumar A, Rath S, Kumar M. Simulation of plate rolling process using finite element method
［J］. Materials Today: Proceedings, 2021, 42: 650-659.

［13］Li J, Li F G, Cai J, et al. Comparative investigation on the modified ZerilliArmstrong model and Arrheniustype model to predict the elevatedtemperature flow behaviour of 7050 aluminium alloy
［J］. Computational Materials Science, 2013, 71: 56-65.

［14］李荣斌, 陈永强, 蒋春霞, 等. 022Cr钢的热变形行为及热加工图
［J］.金属热处理, 2020, 45(7): 51-56.

Li R B, Chen Y Q, Jiang C X, et al.Hot deformation behavior and processing maps of 022Cr steel
［J］. Heat Treatment of Metals, 2020, 45(7): 51-56.

［15］陈雷, 郭晓敏, 贾伟, 等. 航空用近β钛合金TC18热变形过程中流变应力预测
［J］. 燕山大学学报, 2018, 42(6): 486-492.

Chen L, Guo X M, Jia W, et al. Prediction of flow stress in thermal deformation of near-β titanium alloy TC18 for aviation
［J］. Journal of Yanshan University, 2018, 42(6): 486-492.

［16］刘艳芳, 冀国良, 李雷, 等. M50NiL钢热变形过程中的物理型本构方程及微观组织演变
［J］. 材料热处理学报, 2021, 42(8): 170-179.

Liu Y F, Ji G L, Li L, et al. A physicallybased constitutive model and microstructure evolution of M50NiL steel during hot deformation
［J］. Transactions of Materials and Heat Treatment, 2021, 42(8): 170-179.

［17］GB/T 19189—2011，压力容器用调质高强度钢板
［S］.

GB/T 19189—2011，Quenched and tempered high strength steel plates for pressure vessels
［S］.

［18］向彪, 孙朝远, 陈雷. F22高强钢的热变形行为与晶粒组织预测
［J］. 塑性工程学报, 2022, 29(9): 144-150.

Xiang B, Sun C Y, Chen L. Hot deformation behavior and grain structure prediction of F22 highstrength steel
［J］. Journal of Plasticity Engineering, 2022,29 (9): 144-150.

［19］罗锐, 陈乐利, 程晓农, 等. 高温合金Inconel 617B的热变形及动态再结晶行为
［J］. 压力容器, 2020, 37(10): 7-14.

Luo R, Chen L L, Cheng X N, et al. Thermal deformation and dynamic recrystallization behavior of inconel 617B superalloy
［J］. Pressure Vessel Technology, 2020, 37(10): 7-14.

［20］曹辉. 07MnNiMoVDR压力容器用钢生产工艺与组织性能
［D］. 沈阳：东北大学, 2012.

Cao H.Manufacturing Process, Microstructures and Mechanical Properties of Pressure Vessel Steel 07MnNiMoVDR
［D］. Shenyang:Northeastern University, 2012.

［21］罗远, 庞玉华, 孙琦, 等. 07MnNiMoDR板轧制热变形本构方程
［J］. 钢铁研究学报, 2020, 32(11): 977-983.

Luo Y, Pang Y H, Sun Q, et al. Constitutive equation for thermal deformation of 07MnNiMoDR plate in rolling
［J］. Journal of Iron and Steel Research, 2020, 32(11): 977-983.

［22］Ji G L, Li L, Qin F L, et al. Comparative study of phenomenological constitutive equations for an asrolled M50NiL steel during hot deformation
［J］. Journal of Alloys and Compounds, 2017, 695: 2389-2399.

［23］孙宇, 周琛, 万志鹏, 等. 金属材料动态再结晶模型研究现状
［J］. 材料导报, 2017, 31(13): 12-16.

Sun Y, Zhou C, Wan Z P, et al. Current research status of dynamic recrystallization model of metallic materials
［J］. Materials Reports, 2017, 31(13): 12-16.

［24］王锡臣. 中厚板轧机轧制参数模型的研究
［D］. 秦皇岛：燕山大学, 2009.

Wang X C. Research on Rolling Parameter Model of Plate Mill
［D］. Qinhuangdao:Yanshan University, 2009.

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