Plastic yielding in solids strongly depends on various conditions, such as temperature and loading rate and indeed, sample-dependent knowledge of yield points in structural materials promotes reliability in mechanical behavior. Commonly, yielding is measured through controlled mechanical testing at small or large scales, in ways that either distinguish elastic (stress) from total deformation measurements, or by identifying plastic slip contributions. In this paper we argue that instead of separate elastic/plastic measurements, yielding can be unraveled through statistical analysis of total strain fluctuations during the evolution sequence of profiles measured in-situ, through digital image correlation. We demonstrate two distinct ways of precisely quantifying yield locations in widely applicable crystal plasticity models, that apply in polycrystalline solids, either by using principal component analysis or discrete wavelet transforms. We test and compare these approaches in synthetic data of polycrystal simulations and a variety of yielding responses, through changes of the applied loading rates and the strain-rate sensitivity exponents.
翻译:在固体中,塑料的产生在很大程度上取决于各种条件,例如温度和装载率,而且事实上,对结构材料中产值点的样本知识能够促进机械行为的可靠性。通常,产量是通过小型或大尺度的受控机械测试测量的,这种测试的方式既可以区分弹性(压力)和整体变形测量,也可以确定塑料滑块贡献物。在本文中,我们认为,不是分别进行弹性/塑料测量,而是可以通过在现场测量的剖面的进化序列中,通过数字图像相关性,通过对总压力波动的统计分析而解析。我们展示了两种截然不同的方法,在广泛应用的晶晶可塑性模型中精确量化产值位置,这些模型适用于聚晶状固体,或者使用主要部件分析或离散波子变换。我们在多晶体模拟的合成数据中测试和比较这些方法,通过改变应用的装载率和压力率灵敏度指数来进行各种产生反应。