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TB8钛合金动态再结晶动力学的模拟与实验研究

Simulation and Experimental Study of Dynamical Recrystallization Kinetics of TB8 Titanium Alloys.

作者信息

Zhang Wenwei, Yang Qiuyue, Tan Yuanbiao, Ma Min, Xiang Song, Zhao Fei

机构信息

Guizhou Key Laboratory of Materials Mechanical Behavior and Microstructure, College of Materials and Metallurgy, Guizhou University, Guiyang 550025, China.

College of Materials Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China.

出版信息

Materials (Basel). 2020 Oct 5;13(19):4429. doi: 10.3390/ma13194429.

DOI:10.3390/ma13194429
PMID:33028006
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7579267/
Abstract

The dynamic recrystallization (DRX) behavior in the hot working of TB8 titanium alloy was studied by using the experiment and finite element simulation (FEM) method. The results showed that the DRX behavior of TB8 titanium alloys was drastically affected by the hot processing parameters. The rising deformation temperature and reducing strain rate led to an augmentation in the grain size (dDRX) and volume fraction (XDRX) of DRX grains. In view of the true stress-strain curves gained from the experiment, the dDRX and XDRX models of DRX grains were constructed. Based on the developed models for DRX of TB8 titanium alloy, the isothermal forging process of the cylindrical samples was simulated by the DEFORM-3D software. The distributions of the effective strain and XDRX for DRX were analyzed. A comparison of the dDRX and XDRX of DRX grains in the central regions of the samples between the experimental and FEM results was performed. A good correlation between the experimental and simulation results was obtained, indicating that the established FEM model presented good prediction capabilities.

摘要

采用实验和有限元模拟(FEM)方法研究了TB8钛合金热加工过程中的动态再结晶(DRX)行为。结果表明,TB8钛合金的DRX行为受热加工参数的显著影响。变形温度升高和应变速率降低导致DRX晶粒的尺寸(dDRX)和体积分数(XDRX)增大。基于实验获得的真应力-应变曲线,构建了DRX晶粒的dDRX和XDRX模型。基于所建立的TB8钛合金DRX模型,利用DEFORM-3D软件对圆柱形试样的等温锻造过程进行了模拟。分析了DRX的有效应变和XDRX分布。对试样中心区域DRX晶粒的dDRX和XDRX在实验结果和有限元模拟结果之间进行了比较。实验结果与模拟结果具有良好的相关性,表明所建立的有限元模型具有良好的预测能力。

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