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Development of an improved parameter fitting method for planar biaxial testing using rakes

机译:改进的用于使用耙子进行平面双轴测试的参数拟合方法的开发

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A correct estimation of the material parameters from a planar biaxial test is crucial since they will affect the outcome of the finite element model in which they are used. In a virtual planar biaxial experiment, a difference can be noticed in the stress calculated from the force measured experimentally at the rakes and the actual stress at the center of the sample. As a consequence, a classic parameter fitting does not result in a correct estimation of the material parameters. This difference is caused by the boundary conditions of the set-up and is among others dependent on the sample material. To overcome this problem, a new parameter fitting procedure is proposed that takes this difference into account by calculating a finite element-based correction vector. This paper describes the methodology to apply this new parameter fitting procedure on real experimental data from a planar biaxial test using rakes. To this end, image processing is used to extract the experiment characteristics. This information is used to construct a finite element model. Two variations of the new parameter fitting procedure are investigated using two human aortic samples: a basic approach and an image-based approach. The performance of the method is assessed by the difference between the force measured at the rakes during the experiment and the force at the rakes obtained from the finite element simulation. Both approaches of the new parameter fitting procedure lead to an improved estimation of the sample behavior compared with the classic approach.
机译:从平面双轴测试正确估计材料参数至关重要,因为它们会影响使用它们的有限元模型的结果。在虚拟的平面双轴实验中,可以注意到,根据在耙子上实验测得的力和在样品中心处的实际应力计算出的应力有所不同。结果,经典的参数拟合不会导致对材料参数的正确估计。这种差异是由设备的边界条件引起的,其中的差异主要取决于样品材料。为了克服这个问题,提出了一种新的参数拟合程序,该程序通过计算基于有限元的校正向量来考虑该差异。本文介绍了将这种新的参数拟合过程应用于使用耙子进行的平面双轴试验的实际实验数据的方法。为此,使用图像处理来提取实验特征。该信息用于构造有限元模型。使用两个人类主动脉样本研究了新参数拟合过程的两个变体:基本方法和基于图像的方法。该方法的性能通过实验过程中在耙子上测得的力与通过有限元模拟获得的耙子上的力之间的差来评估。与经典方法相比,新参数拟合过程的两种方法都可以改善对样本行为的估计。

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