Identification of viscoplastic material parameters from spherical indentation data: Part II. Experimental validation of the method
Identification of viscoplastic material parameters from spherical indentation data: Part II. Experimental validation of the method
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从球形压痕数据识别粘塑性材料参数：第二部分。
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
N. Huber
中科院分区：
文献类型：
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作者：
D. Klötzer;C. Ullner;E. Tyulyukovskiy;N. Huber
一种基于神经网络的分析方法，用于从球形压痕数据识别粘塑性模型，在这项工作的第一部分中开发[J. Mater. Res. 21，664（2006）]，应用于不同的金属材料。除了典型的参数，如杨氏模量和屈服应力与拉伸实验值的比较，在所确定的材料参数代表模量，硬化行为，和粘度的不确定性进行了研究，在不同的来源。被认为是在压痕位置，尖端半径，施力率，和表面制备的变化。广泛的实验验证表明，所应用的神经网络是非常强大的，并显示小的变异系数，特别是关于杨氏模量和屈服应力的重要参数。另一方面，重要的要求是量化的，其中包括一个非常好的球形压头几何形状和良好的表面处理，以获得可靠的结果。
A neural network-based analysis method for the identification of a viscoplasticity model from spherical indentation data, developed in the first part of this work [ J. Mater. Res. 21 , 664 (2006)], was applied for different metallic materials. Besides the comparison of typical parameters like Young’s modulus and yield stress with values from tensile experiments, the uncertainties in the identified material parameters representing modulus, hardening behavior, and viscosity were investigated in relation to different sources. Variations in the indentation position, tip radius, force application rate, and surface preparation were considered. The extensive experimental validation showed that the applied neural networks are very robust and show small variation coefficients, especially regarding the important parameters of Young’s modulus and yield stress. On the other hand, important requirements were quantified, which included a very good spherical indenter geometry and good surface preparation to obtain reliable results.