Deformation and failure mechanisms in austenitic steel under coupled compressive and torsional loading

压缩和扭转耦合载荷下奥氏体钢的变形和失效机制

基本信息

项目摘要

The understanding of material behavior under multiaxial mechanical loads is of great importance for the application of structural materials, because components are usually exposed to multiaxial stresses in practice. In addition, the load is typically time-dependent and reversing. Experimental investigations under such complex loading conditions are very laborious, such that our understanding of the mechanisms leading to plastic deformation and failure of materials under multi-axial reversing loads is quite insufficient. This leads, moreover, to an uncertainty whether the common solid mechanics failure hypotheses are valid under such conditions. The proposed research project seeks to close this gap in the current state of research by experimentally investigating the deformation and damage mechanisms in a nitrogen-alloyed austenitic steel with superimposed compressive and cyclic torsional loading. Effects of the special loading condition on microstructural mechanisms of strain and damage accumulation and their changes will be analyzed by high resolution microscopy. Based on the experimental results, a constitutive model is formulated within the framework of crystal plasticity that reliably describes cyclic plasticity and damage under multiaxial loads on the microstructural level. In addition to pure dislocation plasticity, mechanical twinning and grain boundary sliding are also considered within the model. Concerning the damage mechanisms, in particular the processes taking place at the material surface are characterized and modeled.Under combined compression and cyclic torsional loading, the phenomenon is observed that samples undergo plastic axial strain, although the compressive load by itself is well below the yield strength of the material. This compressive strain occurs as soon as a critical angle for the cyclic torsion is exceeded. The proposed research project investigates whether this phenomenon can be described with the common failure hypotheses or whether they need to be generalized accordingly. Moreover, the comparison of experimental findings and numerical modeling can lead to a fundamental understanding of the deformation and damage mechanisms under complex mechanical loads. These mechanisms can further be correlated with softening and hardening behavior of the material. In order to ensure the widest possible scope of the micromechanical model of fatigue under multiaxial loads, the influence of prior cold working on the material behavior under superimposed compressive-torsional loads is experimentally investigated and used for model validation.
在多轴机械载荷下对材料行为的理解对于应用结构材料至关重要,因为在实践中,组件通常会暴露于多轴应力。另外,负载通常是时间依赖和逆转的。在这种复杂的负载条件下进行的实验研究非常费力,因此我们对导致多轴反向载荷下材料的塑性变形和故障的机制的理解非常不足。此外,这导致不确定性在这种情况下是否有效,常见的固体力学故障假设是否有效。拟议的研究项目试图通过实验研究氮合成的奥氏体钢的变形和损伤机制,以缩小叠加的压缩和环状扭转负荷来缩小这一差距。特殊载荷条件对应变和损伤积累的显微结构机制及其变化的影响将通过高分辨率显微镜进行分析。基于实验结果,在晶体可塑性的框架内制定了组成型模型,该模型可靠地描述了微观结构水平的多轴负载下的环状可塑性和损害。除了纯粹的脱位可塑性外,模型中还考虑了机械孪晶和晶界滑动。关于损伤机制,尤其是在材料表面进行的过程的表征和建模。在压缩和环状扭转负载下,观察到该现象是样品经历塑料轴向应变,尽管其本身的压缩负荷本身远低于材料的产量强度。超过循环扭转的临界角度,就会发生这种压力应变。拟议的研究项目调查了是否可以用共同的失败假设来描述这种现象,或者是否需要相应地将其概括。此外,实验发现和数值建模的比较可以导致对复杂机械负荷下的变形和损伤机制的基本理解。这些机制可以与材料的软化和硬化行为进一步相关。为了确保在多轴载荷下疲劳的微机械模型的最广泛范围,在实验中研究了对叠加的压缩式载荷下对材料行为的影响的影响,并用于模型验证。

项目成果

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Dr.-Ing. Stefanie Hanke其他文献

Dr.-Ing. Stefanie Hanke的其他文献

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{{ truncateString('Dr.-Ing. Stefanie Hanke', 18)}}的其他基金

Influence of Mg and Si Content in Aluminium Alloys on Severe Plastic Deformation Behaviour during Solid-State Coating Deposition using Friction Surfacing
铝合金中 Mg 和 Si 含量对摩擦堆焊固态涂层沉积过程中严重塑性变形行为的影响
  • 批准号:
    323162991
  • 财政年份:
    2016
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Optical in-situ analysis of the cavitation damage on technical alloys under repeated single bubbles
重复单气泡作用下技术合金空化损伤的光学原位分析
  • 批准号:
    451715773
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Classifying wear characteristics in lubricated sliding wear based on time series sensor signals using artificial intelligence
使用人工智能根据时间序列传感器信号对润滑滑动磨损的磨损特征进行分类
  • 批准号:
    525173005
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants

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