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Mechanical Shape Correlation: A novel integrated digital image correlation approach

Authors :
SM Kleinendorst
Marc G.D. Geers
Jpm Johan Hoefnagels
Mechanics of Materials
Group Geers
Group Hoefnagels
Source :
Computer Methods in Applied Mechanics and Engineering, 345, 983-1006. Elsevier
Publication Year :
2019
Publisher :
Elsevier BV, 2019.

Abstract

Mechanical Shape Correlation (MSC) is a novel Integrated Digital Image Correlation (IDIC) based technique used for parameter identification. Digital images taken during an experiment are correlated and coupled to a Finite Element model of the specimen, in order to find the correct parameters in this numerical model. In contrast to regular IDIC techniques , where the images consist of a grayscale speckle pattern applied to the sample, in MSC the images are projections based on the contour lines of the test specimen only. This makes the technique suitable in cases where IDIC cannot be used, e.g., when large deformations and rotations cause parts of the sample to rotate in or out-of-view, or when the speckle pattern degrades due to large or complex deformations, or when application of the pattern is difficult because of small or large specimen dimensions . The method targets problems for which the outline of the specimen is large with respect to the volume of the structure and changes significantly upon deformation. The technique is here applied to virtual experiments with stretchable electronic interconnects, for identification of both elastic and plastic properties. Furthermore, attention is paid to the influence of algorithmic choices and experimental issues. The method reveals good convergence and adequate initial guess robustness. The results are promising and indicate that the method can be used in cases of either large, complex or three-dimensional displacements and rotations on any scale.

Details

ISSN :
00457825
Volume :
345
Database :
OpenAIRE
Journal :
Computer Methods in Applied Mechanics and Engineering
Accession number :
edsair.doi.dedup.....a7796514fbe7c356f690dedd15598f82
Full Text :
https://doi.org/10.1016/j.cma.2018.10.014