The mechanical properties of an extruded Mg-10Gd sample, specifically designed for vascular stents, are crucial for predicting its behavior under service conditions. Achieving homogeneous stresses in the hoop direction, essential for characterizing vascular stents, poses challenges in experimental testing based on standard specimens featuring a reduced cross section. This study utilizes an elasto-visco-plastic self-consistent polycrystal model (ΔEVPSC) with the predominant twinning reorientation (PTR) scheme as a numerical tool, offering an alternative to mechanical testing. For verification, various mechanical experiments, such as uniaxial tension, compression, notched-bar tension, three-point bending, and C-ring compression tests, were conducted. The resulting force vs. displacement curves and textures were then compared with those based on the ΔEVPSC model. The computational model's significance is highlighted by simulation results demonstrating that the differential hardening along with a weak strength differential effect observed in the Mg-10Gd sample is a result of the interplay between micromechanical deformation mechanisms and deformation-induced texture evolution. Furthermore, the study highlights that incorporating the axisymmetric texture from the as-received material incorporating the measured texture gradient significantly improves predictive accuracy on the strength in the hoop direction. Ultimately, the findings suggest that the ΔEVPSC model can effectively predict the mechanical behavior resulting from loading scenarios that are impossible to realize experimentally, emphasizing its valuable contribution as a digital twin.

中文翻译：

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织构梯度挤压 Mg-10Gd 棒的晶体塑性有限元模拟


专为血管支架设计的挤压 Mg-10Gd 样品的机械性能对于预测其在使用条件下的行为至关重要。在环向上实现均匀应力对于表征血管支架至关重要，这对基于具有减小横截面的标准样本的实验测试提出了挑战。本研究利用弹粘塑性自洽多晶模型 (ΔEVPSC) 和主要孪晶重定向 (PTR) 方案作为数值工具，提供了机械测试的替代方案。为了进行验证，进行了各种力学实验，例如单轴拉伸、压缩、缺口杆拉伸、三点弯曲和 C 形环压缩测试。然后将所得的力与位移曲线和纹理与基于 ΔEVPSC 模型的曲线和纹理进行比较。模拟结果强调了计算模型的重要性，表明在 Mg-10Gd 样品中观察到的差异硬化和弱强度差异效应是微机械变形机制和变形引起的织构演化之间相互作用的结果。此外，该研究强调，将收到的材料中的轴对称纹理与测量的纹理梯度相结合，可以显着提高环向强度的预测准确性。最终，研究结果表明，ΔEVPSC 模型可以有效预测实验无法实现的加载场景所产生的机械行为，强调了其作为数字孪生的宝贵贡献。