Abstract
This paper is concerned with an in-depth study of the interactions between full-field measurements errors and material identification. It is a further step in a research plan that aims to create a simulation procedure of actual experiments, with the final goal of using the simulator to optimise the test set-up in terms of specimen shape, measurement technique, applied load etc. In particular,here, Digital Image Correlation (DIC) is used as a full-field technique to obtain strain and displacement fields. These maps are used as input in an inverse methodology as, for instance, the virtual fields method (VFM) to obtain the material parameters introducing uncertainties in the characterization. The purpose of this contribution is to bridge the gap between experiments and simulations, in order to obtain predictions as close as possible to reality in terms of identification error. That will be used, as final goal of the general study, to optimize numerically a test set-up configuration, giving a priori the best parameters to use to experimentally identify a specimen. In the present contribute, the operating procedure is to perform real experiments and then to reproduce them numerically. Experimental uncertainties such as noise, lighting conditions, in-plane and out-of-plane motions are treated separately and introduced in the simulator. As such, their impact on the identified material properties can be unambiguously investigated. Here, focus is on the elastic properties of aluminium specimens, i.e. the Young’s modulus and the Poisson ratio and their specific variances due to the aforementioned errors. The simulator predicts reality to a large extent.
Similar content being viewed by others
References
Grédiac M (2004) The use of full-field measurement methods in composite material characterization: interest and limitations. Compos Part A-Appl S 35:751–761
Avril S, Bonnet M, Bretelle A-S, Grédiac M, Hild F, Ienny P, Latourte F, Lemosse D, Pagano S, Pagnacco E, Pierron F (2008) Overview of identification methods of mechanical parameters based on full-field measurements. Exp Mech 48:381–402
Pierron F, Grédiac M (2012) The virtual fields Method. Springer
Sutton M.A., Orteu J-J, Schreier HW (2009) Image Correlation for Shape, Motion and Deformation Measurements. Springer
Piro J-L, Grédiac M (2004) Producing and transferring low-spatial-frequency grids fo measuring displacement fields with moiré and grid methods. Exp Tech 28:23–26
Grédiac M, Pierron F, Avril S, Toussaint E (2006) The virtual fields method for extracting constitutive parameters from full-field measurements: a review. Strain 42:233–253
Lecompte D, Cooreman S, Coppieters S, Vantomme H, andand Sol J, Debruyne D (2009) Parameter identification for anisotropic plasticity model using digital image correlation, Comparison Between Uni-axial Bi-axial Tensile Test. Eur J Comput Mech 18:393–418
Cooreman S, Lecompte D, Sol H, Vantomme J, Debruyne D (2008) Identification of mechanical material behavior through inverse modeling and DIC. Exp Mech 48(4):421–433
Grédiac M, Vautrin A (1990) A new method for determination of bending rigidities of thin anisotropic plates. J Appl Mech 57:964–968
Moulart R, Avril S, Pierron F (2006) Identification of the through-thickness rigidities of a thick laminated composite tube. Compos Part A-Appl S 37:326–336
Giraudeau A, Pierron F (2005) Identification of stiffness and damping properties of thin isotropic vibrating plates using the virtual fields method. theory and simulations. J Sound Vib 284:757–781
Rossi M, Pierron F (2012) On the use of simulated experiments in designing test from material characterization from full-field measurement. Int J Solids Struct 49:420–435
Lava P, Rossi M, Badaloni M, Debruyne D, Pierron F (2013) Digital image correlation and virtual fields to design an optimized experimental setup for material identification. In: Photomechanics 2013
Wang Y, Lava P, Coppieters S, De Strycker P, Van Houtte P, Debruyne D (2012) Investigation of the uncertainty of DIC under heterogeneous strain states with numerical test. Strain 48(6):453–462
Bornet M, Brémand F, Doumalin P, Dupré JC, Fazzini M, Grédiac M, Hild F, Mistou S, Molimard J, Orteu JJ, Robert L, Surrel Y, Vacher P, Wattrisse B (2009) Assessment of digital image correlation measurement errors: methodology and results. Exp Mech 49:353–370
Lava P, Cooreman S, Coppieters S, De Strycker M, Debruyne D (2009) Assessment of measuring errors in DIC using deformation fields generated by plastic FEA. Opt Lasers Eng 47:747–753
Lava P, Cooreman S, Debruyne D (2010) Study of systematic errors in strain fields obtained via DIC using heterogeneous deformation generated by plastic FEA. Opt Lasers Eng 48:457–468
Rossi M, Pierron F, Lava P, Debruyne D, Sasso M (2015) Effect of DIC spatial resolution, noise and interpolation error on identification results with the VFM. Strain, doi:10.1111/str12134
Wittewrongel L, Lava P, Lomov S, Debruyne D (2014) A self adaptive global digital image correlation algorithm. Experimental Mechanics, doi:10.1007/s11340-014-9946-3
Lecompte D, Smits A, Bossuyt S, Sol H, Vantomme J, Van Hemelrijck D, Habraken AM (2006) Quality assessment of speckle patterns for digital image correlation. Opt Lasers Eng 44:1132–1145
Hijazi A, Friedl A, Kahler CJ (2011) Influence of camera’s optical axis non-perpendicularity on measurement accuracy of two-dimensional digital image correlation. Jourdan J Mech Ind Eng 4:373–382
Lava P, Coppieters S, Wang Y, Van Houtte P, Debruyne D (2012) Error estimation in measuring strain fields with dic on planar sheet metal specimens with a non-perpendicular camera alignement. Opt Lasers Eng 49:57–65
www.matchID.org. matchID 2D and 3D software
Reu PI (2011) Experimental and numerical methods for exact subpixel shifting. Exp Mech 51:443–452
Grediac M, Sur F (2013) Effect of sensor noise on the resolution and spatial resolution of displacement and strain maps estimated with the grid method. Strain 50:1–27
Surrel Y (1999) Photomecanics, chapter Fringe analysis. Springer Berlin, pp 57–104
Chalal H, Avril S, Pierron F, Meraghni F (2006) Experimental identification of a nonlinear model for composites using the grid technique coupled to the virtual fields method. Compos Part A-Appl S 37(2):315–325
Sur F, Grédiac M (2014) Sensor noise modeling by stacking pseudo-periodic grid images affected by vibrations. IEEE Signal Process Lett 21(4):432–436
Réthouré J (2010) A fully integrated noise robust strategy for the identification of constitutive laws from digital images. Int Numer Methods Eng 84(6):631–660
Gras R, Leclerc H, Hild F, Roux S, Schneider J Identification of a set of macroscopic elastic parameters in a 3D woven composite: Uncertainty analysis and recularization. Int J Solid Struct 201:2–16
Sutton MA, Yan JH, Tiwari V, Schreier HW, Orteu J-J (2008) The effect of out-of-plane motion on 2d and 3d digital image correlation measurements. Opt Lasers Eng 46:746–757
Pan B, Xie H, Wang Z (2010) Equivalence of digital image correlation criteria for pattern matching. Appl Opt 49:5501–5509
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Badaloni, M., Rossi, M., Chiappini, G. et al. Impact of Experimental Uncertainties on the Identification of Mechanical Material Properties using DIC. Exp Mech 55, 1411–1426 (2015). https://doi.org/10.1007/s11340-015-0039-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11340-015-0039-8