Comparison of the identification performance of conventional FEM updating and integrated DIC

Abstract

Summary Full‐field identification methods are increasingly used to adequately identify constitutive parameters to describe the mechanical behavior of materials. This paper investigates the more recently introduced one‐step method of integrated digital image correlation (IDIC) with respect to the most commonly used two‐step method of finite element model updating (FEMU), which uses a subset‐based DIC algorithm. To make the comparison as objective as possible, both methods are implemented in the most equivalent manner and use the same FE model. Various virtual test cases are studied to assess the performance of both methods when subjected to different error sources: (1) systematic errors, (2) poor initial guesses for the constitutive parameters, (3) image noise, (4) constitutive model errors, and (5) experimental errors. Results show that, despite the mathematical similarity of both methods, IDIC produces less erroneous and more reliable results than FEMU, particularly for more challenging test cases exhibiting small displacements, complex kinematics, misalignment of the specimen, and image noise. Copyright © 2015 John Wiley & Sons, Ltd.

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

“…(i) whereas the work of Fedele (2015) is set within the Finite Element Method Updating (FEMU) framework, BE-IDIC is defined within the realm of IDIC, with demonstrated advantages in terms of robustness and accuracy (see Ruybalid et al 2016), (ii) as a consequence of (i), the resulting IDIC problem is well-posed and hence solvable even for full kinematic resolution of the boundary; this is in contrast with the method by Fedele (2015), for which the author himself points its ill-posedness, (iii) because the proposed methodology addresses the general case of highly heterogeneous nonlinear materials, smooth regularization of boundary data is not possible (in contrast to the method of Fedele (2015)), (iv) for cases slightly less heterogeneous, in which full resolution of the boundary kinematics is not required, an adaptive algorithm is proposed to automatically find the correct boundary kinematics regularization (with option to reach the full resolution case); the method by Fedele (2015) requires, on the other hand, a prior choice of regularization (properly selected by the user).…”

“…In particular, its integrated variant called Integrated Digital Image Correlation (IDIC) has proven to be a reliable and accurate technique for the identification of material parameters, see e.g. Roux and Hild (2006); Leclerc et al (2009); Réthoré et al (2009Réthoré et al ( , 2013; Neggers et al (2015); Ruybalid et al (2016). It relies on the minimization of the difference between two images captured during an experiment (corresponding to the reference and a deformed configuration) inside the Region Of Interest (ROI).…”

Spatiotemporal regularization for digital image correlation: Application to infrared camera frames

“…The constrained registration is then limited to the sensitivity fields (ie, variation of the displacement field with respect to unknown parameters of the model) provided by either closed‐form solutions() or FE predictions. ()…”

“…The constrained registration is then limited to the sensitivity fields (ie, variation of the displacement field with respect to unknown parameters of the model) provided by either closed-form solutions 20,21 or FE predictions. [22][23][24][25][26] All those regularization methods address DIC between a single pair of images, but many experimental applications necessitate a full movie or sequence of images and the spatiotemporal information is of interest. Along the previous lines, it is also possible to include the time dimension in the DIC formalism.…”

A Uni-Axial Nano-Displacement Micro-Tensile Test of Individual Constituents from Bulk Material

“…Here, however, the Pt pattern has been optimized (in terms of speckle size and contrast) to yield the maximum displacement resolution for DIC applied to optical profilometry images, which explains the choice for a much coarser pattern with smooth edges of the speckles. This reduces the interpolation error in the DIC algorithm and makes the DIC more robust against out of plane displacement [41][42][43]. Usually, such a smooth speckle pattern is hard to make, but in our case the ion-beam assisted Pt deposition gives full control to create speckles with smooth edges.…”