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首页> 外文学位 >Microstructural modeling during multi-pass rolling of a nickel-base superalloy.
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Microstructural modeling during multi-pass rolling of a nickel-base superalloy.

机译：镍基高温合金多道次轧制过程中的显微组织建模。

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摘要

Microstructure present at the end of rolling and cooling operations controls the product properties. Therefore, control of grain size is an important characteristic in any hot-working. The narrow temperature range for hot working of Alloy 718 makes the grain size control more difficult. In the current work, a systematic numerical approach to predict the microstructure of Alloy 718 during multi-pass rolling is developed. This approach takes into account the severe deformation that takes place during each pass and also the possible reheating between passes. In order to predict the grain size at the end of rolling process, microstructural processes such as dynamic recrystallization (DRX), metadynamic recrystallization (MDRX), and static grain growth need to be captured at every deformation step for superalloys. Empirical relationships between the average grain size from various microstructural processes and the macroscopic variables such as temperature (T), effective strain 3&d1 and strain rate 3&d1&d2 form the basis for the current work. The empirical relationships considered in this work are based on Avrami equations and utilize data taken from various forging analyses. The macroscopic variables are calculated using the Finite Element Method (FEM) by modeling the rolling process as a creeping flow problem. FEM incorporates a mesh re-zoning algorithm that enables the analysis to continue for several passes. A two-dimensional transient thermal analysis is carried out between passes that can capture the MDRX and/or static grain growth during the microstructural evolution. The microstructure prediction algorithm continuously updates two families of grains, namely, the recrystallized family and strained family at the start of deformation in any given pass. In addition, the algorithm calculates various subgroups within these two families at every deformation step within a pass. As the material undergoes deformation between the rolls, recrystallization equations are invoked depending on critical strain and strain rate conditions that are characteristics of Alloy 718. This approach predicts the microstructural evolution based on recrystallization kinetics and static grain growth only. Precipitation of phases such as gamma', gamma'' and delta are not considered. Modeling this complex precipitation is difficult and requires a more detailed understanding than is presently available. Nevetheless, comparisons of the grain sizes from the proposed numerical models with experimental results for 16-stand rolling process show very good agreement.
机译：轧制和冷却操作结束时存在的微结构控制产品性能。因此，晶粒尺寸的控制是任何热加工的重要特征。合金718的热加工温度范围狭窄，使得晶粒尺寸控制更加困难。在当前的工作中，开发了一种系统的数值方法来预测多道次轧制过程中718合金的组织。该方法考虑了每次通过期间发生的严重变形以及两次通过之间可能的重新加热。为了预测轧制过程结束时的晶粒尺寸，需要在超级合金的每个变形步骤中捕获微观结构过程，例如动态再结晶（DRX），超动态再结晶（MDRX）和静态晶粒长大。来自各种微结构过程的平均晶粒尺寸与宏观变量（例如温度（T），有效应变3＆d1和应变速率3＆d1＆d2）之间的经验关系构成了当前工作的基础。在这项工作中考虑的经验关系是基于Avrami方程，并利用了从各种锻造分析中获得的数据。使用有限元方法（FEM）通过将轧制过程建模为蠕变流动问题来计算宏观变量。 FEM合并了网格重新分区算法，该算法可使分析继续进行数次。在通过之间可以进行二维瞬态热分析，这些分析可以捕获微结构演化过程中的MDRX和/或静态晶粒生长。显微组织预测算法在任何给定道次的变形开始时连续更新两个晶粒家族，即再结晶家族和应变家族。另外，该算法在遍中的每个变形步骤中计算这两个族内的各个子组。当材料在轧辊之间变形时，取决于关键应变和应变速率条件（取决于718合金的特性），将调用再结晶方程。这种方法仅基于再结晶动力学和静态晶粒生长来预测微观结构的演变。不考虑诸如γ′，γ′和δ的相的沉淀。对这种复杂的降水进行建模很困难，并且需要比目前可用的方法更详细的理解。然而，将所提出的数值模型的晶粒尺寸与16机架轧制过程的实验结果进行比较显示出很好的一致性。

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  • 作者

    Subramanian, Kannan.;

  • 作者单位

    The University of North Carolina at Charlotte.;

  • 授予单位 The University of North Carolina at Charlotte.;
  • 学科 Engineering Mechanical.Engineering Materials Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 124 p.
  • 总页数 124
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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