Search for compatible Zirconia-based shape memory ceramics

搜索兼容的氧化锆基形状记忆陶瓷

• 批准号: 453203767
• 负责人: Professor Dr.-Ing. Eckhard Quandt
• 金额: --
• 依托单位: Department of Aerospace Engineering and Mechanics
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/453203767?language=en
• 关键词: Search; compatible; Zirconia; based; shape

Metallic shape memory alloys (SMA) show two properties of interest: the shape memory effect used for solid state actuators and the superelasticity e.g. for self-expanding medical implants. They are based on a reversible, thermal or stress-induced phase transition of the first order between austenite and martensite phases with large internal stresses, which lead to large strains and enthalpy changes. The reversibility is achieved through the compatibility of the two phases, which leads to a reduction in the local stresses at the interface and the hysteresis. This compatibility can be expressed by the condition that the mean eigenvector of the transformation matrix ideally has to be one.Due to the elements used in metallic SMA, use under corrosive chemical conditions or at extreme temperatures is difficult. This motivates a new class of shape memory materials, which consist of metal oxides and are called shape memory ceramics (SMC). Although Swain first identified a suitable oxide (MgO-stabilized ZrO2) in 1986, the lack of compatibility to date leads to the development of cracks and severe fatigue upon cycling.In prior own research the findings regarding compatibility in metallic SMA were transferred to their ceramic counterparts in the system (Y0.5Ta0.5O2)1-x (Zr0.5Hf0.5O2)x. The main results were a significant dependence of the transformation hysteresis on the composition and a strong dependency of the stress-induced orientation of the martensitic variants on the hysteresis, without however approaching the properties of metallic SMA. The dependence on the mean eigenvalue also differs: Given the two probable transformation mechanisms, the results suggest that the composition, in which the compatibility for both transformations is equally well fulfilled, defines the point of lowest hysteresis (equidistance condition).The main goal of this application is the search for SMC with low hysteresis and good shape memory properties. This search is based on a data set containing approximately 70 different ZrO2-based SMC with the transformation temperatures and the crystallographic data. The corresponding working hypotheses are that for SMC the mean eigenvalues ​​of both lattice correspondences must be as close as possible to one and that these conditions lead to two lattice parameter relations that suggest searching for dopants that reduce the tetragonality.In addition to this main objective we will investigate whether a machine learning approach based on the available data set of physical parameters will be appropriate to identify compositions for SMC with significantly improved properties in terms of hysteresis and transformation reversibility.

金属形状存储合金（SMA）显示了两个感兴趣的属性：用于固态执行器的形状存储效果和超弹性，例如用于自我扩展的医疗裁员。它们基于具有较大内部应力的奥氏体和马氏体相之间的一阶的可逆，热或应力诱导的相变，这会导致较大的菌株和焓变。可逆性是通过两个阶段的兼容性实现的，从而导致界面和磁滞处的局部应力减少。这种兼容性可以通过以下条件表达，即理想情况下，转换矩阵的平均特征向量必须是一个。与金属SMA中使用的元素，在腐蚀性化学条件下或在极端温度下使用是困难的。 This motivates a new class of shape memory materials, which consist of metal oxides and are called shape memory ceramics (SMC).Although Swain first identified a suitable oxide (MgO-stabilized ZrO2) in 1986, the lack of compatibility to date leads to the development of cracks and severe fatigue upon cycling.In prior own research the findings regarding compatibility in metallic SMA were transferred to their ceramic counterparts in the system （Y0.5TA0.5O2）1-X（ZR0.5HF0.5O2）x。主要的结果是转化滞后对组成的重要依赖性，并且在不接近金属SMA的特性的情况下，马氏体变体的应力诱导的方向的强烈依赖性。对平均特征值的依赖性也有所不同：鉴于两个有问题的转换机制，结果表明，这两种转换的兼容性同样得到很好的实现，定义了最低的滞后点（等效性条件）。该应用的主要目标是搜索SMC具有较低的SMC具有较低的滞疗形状和良好的形状和良好的形状。此搜索基于一个数据集，该数据集包含大约70种不同的基于ZRO2的SMC，并具有转换温度和晶体学数据。相应的工作假设是，对于SMC，两个晶格对应关系的平均特征值必须尽可能接近一个，并且这些条件会导致两个晶格参数关系，这些条件暗示搜索搜索降低四方的掺杂剂。在此主要目标中，我们将根据适当的属性和SMC的属性来调查该主要目标，以调查机器学习方法是否适用于SMC，以识别SMC的特性，该方法是否会符合SMC的列表，该方法是SMC的介绍。可逆性。