High-Temperature Viscoplastic Constitutive Model and Rheological Behavior Analysis for Axle Steel

被引：0
|
作者：
Liu G. [1 ]
Ning X.-Y. [1 ]
Li T.-X. [1 ]
Li H.-J. [1 ]
机构：
[1] State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2021年 / 42卷 / 10期
关键词：
Casting microstructure; Dynamic recrystallization; High-temperature viscoplastic constitutive model; Rheological behavior; Work hardening;
D O I：
10.12068/j.issn.1005-3026.2021.10.006
中图分类号：
学科分类号：
摘要：
A high-temperature viscoplastic constitutive model is the basis of numerical simulation on the deformation behaviors of continuous casting billet in a near-solidification-finishing reduction(NSFR) process. However, the lack of the stress and strain data under this condition extremely limits the development of new continuous casting processes. The rheological behaviors of axle steel under the NSFR and conventional hot deformation processes are studied through thermal simulation experiments. Based on the theory of dynamic recovery and recrystallization, a revised constitutive model in the NSFR process is established. The results show that the austenite grains are coarser and the rheological stress is lower under the NSFR process, compared with those under the conventional one. Under the same reduction, the volume fraction of dynamic recrystallization grain is higher. The predicted stresses from the constitutive model under different conditions are in good agreement with the experimental ones and the average relative error is about 2.62%. © 2021, Editorial Department of Journal of Northeastern University. All right reserved.
引用
收藏
页码：1407 / 1413and1426
相关论文
共 12 条
  • [1] Shui Heng-yong, Zhang Yong-quan, Yang Cai-fu, Development trend of materials for high-speed train wheels, Journal of Iron and Steel Research, 3, pp. 66-69, (2003)
  • [2] Du Song-lin, Wang Kai-zhong, Hu Fang-zhong, Overview and prospect of high speed train axle technology at home and abroad, Progress of Materials in China, 38, 7, pp. 641-650, (2019)
  • [3] Cai Kai-ke, Billet quality control, (2010)
  • [4] Li T, Li H, Li R, Et al., Work roll surface profile design and optimization for hot-core heavy reduction rolling process[J], ISIJ International, 59, 7, pp. 1314-1322, (2019)
  • [5] Farabi E, Zarei-Hanzaki A, Abedi H R., High temperature formability predication of dual phase brass using phenomenological and physical constitutive models, Journal of Materials Engineering & Performance, 24, 1, pp. 209-220, (2015)
  • [6] Haghdadi N, Zarei-Hanzaki A, Khalesian A R, Et al., Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy, Materials & Design, 49, pp. 386-391, (2013)
  • [7] Zener C, Hollomon J H., Effect of strain rate upon plastic flow of steel[J], Journal of Applied Physics, 15, 1, pp. 22-32, (1944)
  • [8] Chen Yong, Wen Guang-qi, Zhang Xiao-ming, Et al., Hot deformation behavior of a high-Mn TWIP steel and establishment of strain-compensated constitutive equation, Journal of Northeastern University(Natural Science), 42, 3, pp. 325-332, (2021)
  • [9] Du S, Chen S, Song J, Et al., Hot deformation behavior and dynamic recrystallization of medium carbon LZ50 steel[J], Metallurgical and Materials Transactions A, 48, 3, pp. 1310-1320, (2017)
  • [10] Laasraoui A, Jonas J J., Prediction of steel flow stresses at high temperatures and strain rates[J], Metallurgical Transactions A, 22, 7, pp. 1545-1558, (1991)
12