Parameter identification for rubber materials with artificial higher dimensional data
Parameter identification for rubber materials with artificial higher dimensional data
复制标题
利用人工高维数据识别橡胶材料的参数
DOI：
10.1002/pamm.201410201
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
R. Mahnken
中科院分区：
文献类型：
--
作者：
N. Nörenberg;R. Mahnken
通常，实验室测试只提供有限数量的实验数据。因此，物质行为的预测成为一项困难的任务，而且，用基于统计的方法进行统计分析几乎是不可能的。作为增加数据数量的补救措施，人工数据是通过随机模拟产生的。因此，可以获得任意数量的数据，并且可以对参数识别过程进行统计分析。在这里，特别的挑战是考虑空间和不均匀问题。在这项工作中，产生的人工数据的弹性体条带孔洞在拉伸。非均匀应力/应变场是用Aramis/GOM系统进行光学测量的，为了产生人工数据，必须将其拟合到随机模型。采用B-样条法对试件的几何形状和测量数据在空间和时间上进行拟合。对应用的参数识别和所得到的材料参数进行了统计分析。在该实例中，对Ogden模型进行了统计分析。(2014Wiley-VCH Verlag GmbH&Co.KGaA，Weinheim)
In general, laboratory test render only a limited number of experimental data. Consequently, the prediction of material behaviour becomes a difficult task and, moreover, a statistical analysis with a statistically based approach is almost impossible. As a remedy to increase the number of data, artificial data are generated by stochastic simulation. As a consequence an arbitrary number of data is available and the process of parameter identification can be analysed statistically. Here, the special challenge is the consideration of spatial and inhomogeneous problems. In this work artificial data are generated for a elastomer strip with hole under tension. The inhomogeneous stress/strain fields are optically measured with an Aramis/GOM system and have to be fitted to a stochastic model in order to generate artificial data. B‐Splines are applied to fit the geometry of the test specimen and the measured data in space as well as in time. Parameter identifications applied and the resulting material parameters are statistically analysed. In the example, a statistical analysis of an Ogden model is performed. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
影响因子：
2.8
作者：
Nicole Nörenberg;R. Mahnken
通讯作者：
R. Mahnken