On micromechanical parameter identification with integrated DIC and the role of accuracy in kinematic boundary conditions

Abstract

Integrated Digital Image Correlation (IDIC) is nowadays a well established full-field experimental procedure for reliable and accurate identification of material parameters. It is based on the correlation of a series of images captured during a mechanical experiment, that are matched by displacement fields derived from an underlying mechanical model. In recent studies, it has been shown that when the applied boundary conditions lie outside the employed field of view, IDIC suffers from inaccuracies. A typical example is a micromechanical parameter identification inside a Microstructural Volume Element (MVE), whereby images are usually obtained by electron microscopy or other microscopy techniques but the loads are applied at a much larger scale. For any IDIC model, MVE boundary conditions still need to be specified, and any deviation or fluctuation in these boundary conditions may significantly influence the quality of identification. Prescribing proper boundary conditions is generally a challenging task, because the MVE has no free boundary, and the boundary displacements are typically highly heterogeneous due to the underlying microstructure. The aim of this paper is therefore first to quantify the effects of errors in the prescribed boundary conditions on the accuracy of the identification in a systematic way. To this end, three kinds of mechanical tests, each for various levels of material contrast ratios and levels of image noise, are carried out by means of virtual experiments. For simplicity, an elastic compressible Neo-Hookean constitutive model under plane strain assumption is adopted. It is shown that a high level of detail is required in the applied boundary conditions. This motivates an improved boundary condition application approach, which considers constitutive material parameters as well as kinematic variables at the boundary of the entire MVE as degrees of freedom in the IDIC procedure, assuring that both are identified with equal precision and importance. This problem has been studied in the literature with a different method, i.e. Finite Element Method Updating framework.

Integrated digital image correlation for micro-mechanical parameter identification in multiscale experiments

“…4 and 5: (i) sequential identification of microscopic displacement fields by GDIC followed by material parameter identification through IDIC (introduced by Hild et al 2016 andShakoor et al 2017), abbreviated as GDIC-IDIC in what follows; and (ii) simultaneous identification of boundary conditions and material parameters, abbreviated as Boundary-Enriched IDIC (BE-IDIC), cf. Fedele and Santoro (2012) and Rokoš et al (2018b). Because the GDIC-IDIC approach is subject to the well-known GDIC compromise (i.e., lack of kinematic freedom for coarse GDIC meshes and random errors due to image noise for fine meshes, cf.…”

“…Because the GDIC-IDIC approach is subject to the well-known GDIC compromise (i.e., lack of kinematic freedom for coarse GDIC meshes and random errors due to image noise for fine meshes, cf. Leclerc et al 2009), the identified kinematic boundary conditions (and hence identified micromechanical parameters) suffer from inaccuracies for highly heterogeneous microstructures (Rokoš et al, 2018b).…”

“…As hinted to in both Eqs. (2) and (3), λ may consist of kinematic Degrees Of Freedom (DOFs), such as kinematic boundary conditions (Rokoš et al, 2018b), material parameters, or topological properties associated with the underlying system. Accuracy of IDIC can further be improved by including the force measurement in the optimization scheme, i.e., by considering…”

“…A potential drawback of this approach is that boundary conditions prescribed to the MVE model are sampled from the meso-or macroscopic model Ω M M , and hence they do not contain any micro-structural features and fluctuations, which are known to be essential for accurate microstructural IDIC material identification without stress normalization, cf. Rokoš et al (2018b).…”

Mechanical Shape Correlation: A novel integrated digital image correlation approach

“…pre-step in the correlation algorithm that uses DIC on subregions of the sample relevant for determining the boundary conditions [34,35]. It applies regular DIC (global or local) on these subregions to acquire the displacements, which can be translated to kinematic boundary conditions on nodes in the corresponding subregions in the FE simulation.…”