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Modeling the Flow Behavior, Recrystallization, and Crystallographic Texture in Hot-Deformed Fe-30 Wt Pct Ni Austenite

机译:对热变形的Fe-30 Wt Pct Ni奥氏体中的流动行为,重结晶和晶体织构进行建模

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摘要

The present work describes a hybrid modeling approach developed for predicting the flow behavior, recrystallization characteristics, and crystallographic texture evolution in a Fe-30 wt pct Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 °C and 1050 °C and strain rates between 0.1 and 10 s−1. The evolution of grain structure, crystallographic texture, and dislocation substructure was characterized in detail for a deformation temperature of 950 °C and strain rates of 0.1 and 10 s−1, using electron backscatter diffraction and transmission electron microscopy. The hybrid modeling method utilizes a combination of empirical, physically-based, and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallization behavior is predicted from the measured microstructural state variables of internal dislocation density, subgrain size, and misorientation between subgrains using a physically-based model. The texture evolution is modeled using artificial neural networks.
机译:本工作描述了一种混合建模方法,该方法用于预测经受热平面应变压缩的Fe-30 wt pct Ni奥氏体模型合金中的流动行为,再结晶特性和晶体学织构演变。在850°C至1050°C的温度以及0.1至10 s-1 的应变速率下进行了一系列压缩测试。利用电子反向散射衍射和透射电子显微镜,详细描述了变形温度为950°C,应变速率为0.1和10 s-1 时晶粒结构,晶体结构和位错亚结构的演化。混合建模方法利用了经验模型,基于物理模型和神经模糊模型的组合。使用经验模型将流应力描述为应变率和温度的应用变量的函数。使用基于物理的模型,根据内部位错密​​度,亚晶粒尺寸和亚晶粒间取向错误的微结构状态变量预测重结晶行为。使用人工神经网络对纹理演变进行建模。

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  • 来源
    《Metallurgical and Materials Transactions A》 |2007年第10期|2400-2409|共10页
  • 作者单位

    School of Engineering and Design Brunel University Uxbridge UB8 3PH United Kingdom;

    Department of Engineering Materials The University of Sheffield Sheffield S1 3JD United Kingdom;

    Centre for Material and Fibre Innovation Deakin University Geelong Victoria 3217 Australia;

    Department of Automatic Control and System Engineering The University of Sheffield Sheffield S1 3JD United Kingdom;

    Department of Automatic Control and System Engineering The University of Sheffield Sheffield S1 3JD United Kingdom;

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