Abstract The mechanical characterization of anisotropic thin walled-tubes along hoop direction is not a trivial task. It is necessary to develop experimental techniques, numerical methods and design test samples, which enable to determine the real tube properties along hoop direction without any external influences. In this study, first we propose a surrogate based-model for the mechanical design of the ring hoop tensile test (RHTT) specimen, in order to obtain the effective homogeneous stress and strain distribution of the uniaxial tensile test along hoop direction. Second, the optimized sample is used to carry out RHTT and to obtain the actual flow stress curve and the anisotropy coefficients of AA6063-O extruded tube. However, the experimental curve measured from RHTT (force –displacement) is a degenerate response, since it suffers from intermixture effects of the effective material behaviour with the friction between the sample and the sample-holding tool. Hence, we developed an inverse parameter identification method, which uses design of experiments, finite element analysis and artificial neural network to separate out the tubular material parameters from the friction coefficient. The assessment of the developed method is achieved by comparing the predicted material parameters and the identified flow stress curve obtained by artificial neural network algorithm. The finite element simulation results corroborate the obtained findings.

中文翻译：

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用于逆材料参数识别的替代建模环形拉伸试样的力学设计

摘要 沿箍方向对各向异性薄壁管进行力学表征并非易事。有必要开发实验技术、数值方法和设计测试样品，从而能够在不受任何外部影响的情况下确定沿箍方向的真实管材特性。在这项研究中，我们首先提出了一种基于代理的环箍拉伸试验 (RHTT) 试样的机械设计模型，以获得沿环向的单轴拉伸试验的有效均匀应力和应变分布。其次，利用优化后的样品进行RHTT，得到AA6063-O挤压管的实际流动应力曲线和各向异性系数。然而，从 RHTT（力-位移）测量的实验曲线是退化响应，因为它受到有效材料行为与样品和样品夹持工具之间摩擦的混合影响。因此，我们开发了一种反参数识别方法，该方法使用实验设计、有限元分析和人工神经网络从摩擦系数中分离出管材参数。所开发方法的评估是通过比较预测的材料参数和人工神经网络算法获得的识别流动应力曲线来实现的。有限元模拟结果证实了所获得的发现。有限元分析和人工神经网络从摩擦系数中分离出管材参数。所开发方法的评估是通过比较预测的材料参数和人工神经网络算法获得的识别流动应力曲线来实现的。有限元模拟结果证实了所获得的发现。有限元分析和人工神经网络从摩擦系数中分离出管材参数。所开发方法的评估是通过比较预测的材料参数和人工神经网络算法获得的识别流动应力曲线来实现的。有限元模拟结果证实了所获得的发现。