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Detecting precursors of localization by strain-field analysis

机译:通过应变场分析检测定位的前体

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Localization in the deformation field, even though initiated locally at the microscopic scale, leads upon increased deformation to fracture at the macroscopic scale, thereby violating the separation of length scales. Localization and damage can be accounted for in macroscopic modeling by appropriate enrichments at that level, however doing so requires (i) detecting the onset of localization prior to its actual occurrence and (ii) quantifying the kinematical characteristics of the localization band. This paper serves that goal. A methodology is developed to analyze the evolution of strain- and displacement-fields during deformation. A key ingredient in this analysis is the use of the Minkowski functionals (also known as intrinsic volumes, quermass integrals, or curvature integrals) from integral geometry, to detect emerging patterns in thresholded strain- and displacement-fields. Doing so, the onset of localization in the microstructure is detected as the emergence of a correlated and narrow pattern of high strains, prior to the actual loss of material stability. Furthermore, the developed localization band is characterized in terms of a weak displacement discontinuity, incorporating the width and direction of the band. The developed methodology uses kinematical fields only, and is therefore applicable to both numerical and experimental deformation-field data. For illustration purposes, numerical data from a finite-element simulation of a deformed voided microstructure is used, without any loss of generality. (C) 2017 Elsevier Ltd. All rights reserved.
机译:即使在微观尺度上局部地开始,变形场中的局部化也导致变形增加而在宏观尺度上断裂,从而违反了长度尺度的分离。可以通过在该级别进行适当的富集在宏观建模中解决定位和损坏,但是,这样做需要(i)在定位实际发生之前检测定位的开始,以及(ii)量化定位带的运动学特征。本文达到了这个目标。开发了一种方法来分析变形过程中应变场和位移场的演变。该分析中的关键要素是使用来自积分几何的Minkowski泛函(也称为内在体积,量子积分或曲率积分),以检测阈值应变场和位移场中出现的模式。这样做,在实际失去材料稳定性之前,检测到微结构中定位的开始是高应变的相关且狭窄的模式的出现。此外,发达的定位带的特征在于弱的位移不连续性,并结合了带的宽度和方向。所开发的方法仅使用运动场,因此适用于数值和实验变形场数据。为了说明的目的,使用了变形的有孔微结构的有限元模拟的数值数据,而没有任何一般性的损失。 (C)2017 Elsevier Ltd.保留所有权利。

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