Abstract
In practice, out-of-plane motions usually are not avoidable during experiments. Since 2D–DIC measurements are vulnerable to parasitic deformations due to out-of-plane specimen motions, three-dimensional digital image correlation (StereoDIC or 3D–DIC) oftentimes is employed. The StereoDIC method is known to be capable of accurate deformation measurements for specimens subjected to general three-dimensional motions, including out-of-plane rotations and displacements. As a result, there has been limited study of the deformation measurements obtained when using StereoDIC to measure the displacement and strain fields for a specimen subjected only to out-of-plane rotation. To assess the accuracy of strain measurements obtained using stereovision systems and StereoDIC when a specimen undergoes appreciable out of plane rotation, rigid body out-of-plane rotation experiments are performed in the range −400 ≤ θ ≤ 400 using a two-camera stereovision system. Results indicate that (a) for what would normally be considered “small angle” calibration processes, the measured normal strain in the foreshortened specimen direction due to specimen rotation increases in a non-linear manner with rotation angle, with measurement errors exceeding ±1400με and (b) for what would normally be considered “large angle” calibration processes, the magnitude of the errors in the strain are reduced to ±300με. To theoretically assess the effect of calibration parameters on the measurements, two separate analyses are performed. First, theoretical strains due to out-of-plane rigid body rotation are determined using a pinhole camera model to project a series of three-dimensional object points into the image plane using large angle calibration parameters and then re-project the corresponding sensor plane coordinates back into the plane using small angle calibration parameters. Secondly, the entire imaging process is also simulated in order to remove experimental error sources and to further validate the theory. Results from both approaches confirmed the same strain error trends as the experimental strain measurements, providing confidence that the source of the errors is the calibration process. Finally, variance based sensitivity analyses show that inaccuracy in the calibrated stereo angle parameter is the most significant factor affecting the accuracy of the measured strain.
Similar content being viewed by others
Notes
The model used in this study does not include image distortion parameters (e.g. radial, tangential), which can be included if needed for a specific imaging configuration.
References
Schreier HW, Orteu JJ, Sutton MA (2009). Image correlation for shape, motion and deformation measurements. Springer US
Sutton MA, Wolters WJ, Peters WH, Ranson WF, McNeill SR (1983) Determination of displacements using an improved digital correlation method. Image Vis Comput 1(3):133–139
Kahn-Jetter ZL, Chu TC (1990) Three-dimensional displacement measurements using digital image correlation and photogrammic analysis. Exp Mech 30(1):10–16
Luo PF, Chao YJ, Sutton MA, Peters Iii WH (1993) Accurate measurement of three-dimensional deformations in deformable and rigid bodies using computer vision. Exp Mech 33(2):123–132
Luo PF, Chao YJ, Sutton MA (1994) Application of stereo vision to three-dimensional deformation analyses in fracture experiments. Opt Eng 33(3):981–990
Sutton MA, Yan JH, Tiwari V, Schreier HW, Orteu JJ (2008) The effect of out-of-plane motion on 2D and 3D digital image correlation measurements. Opt Lasers Eng 46(10):746–757
Tiwari V, Sutton MA, McNeill SR (2007) Assessment of high speed imaging systems for 2D and 3D deformation measurements: methodology development and validation. Exp Mech 47(4):561–579
Yan JH, Sutton MA, Deng X, Cheng CS (2007) Mixed-mode fracture of ductile thin-sheet materials under combined in-plane and out-of-plane loading. Int J Fract 144(4):297–321
Sutton MA, Yan J, Deng X, Cheng CS, Zavattieri P (2007) Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading. Opt Eng 46(5):051003
Ravn O, Andersen NA, Sorensen AT Auto-calibration in Automation Systems using Vision. In 3rd International Symposium on Experimental Robotics (ISER’93), pages 206–218, Japan, 1993
Triggs B, McLauchlan PF, Hartley RI, Fitzgibbon AW (1999) Bundle adjustment—a modern synthesis. In International workshop on vision algorithms. Springer Berlin Heidelberg, pp 298–372
Hartley, R., & Zisserman, A. (2000). Multiple view geometry in computer vision. Cambridge University Press, Cambridge, UK
Yasmeen F, Rajan S, Sutton MA, Schreier HW (2017) Experimental study of measurement errors in 3D-DIC due to out-of-plane specimen rotation. In International Digital Imaging Correlation Society. Springer, Cham, pp 211–215
Sutton MA, Helm JD, Boone ML (2001) Experimental study of crack growth in thin sheet 2024-T3 aluminium under tension-torsion loading. Int J Fract 109(3):285–301
Shukla A, Dally JW (2010) Experimental solid mechanics. College House Enterprises, Knoxville, p 668
VIC-3D, Correlated Solutions Incorporated, 121 Dutchman Blvd, Irmo, SC 29063. Retrieved January13, 2018 from the World Wide Web: http://www.correlatedsolutions.com
Sutton MA (2008) Digital image correlation. In: Sharpe Jr. WN (ed) Springer handbook of experimental solid mechanics. Springer, Berlin
Balcaen R, Wittevrongel L, Reu PL, Lava P, Debruyne D (2017) Stereo-DIC calibration and speckle image generator based on FE formulations. Exp Mech 57(5):703–718
Balcaen R, Reu PL, Lava P, Debruyne D (2017) Stereo-DIC uncertainty quantification based on simulated images. Exp Mech 57(6):939–951
MatchID, MatchIDMBC, Wijmenstraat 21T, B-9030 Gent-Belgium. Retrieved January13, 2018 from the World Wide Web: http://www.matchidmbc.com/
Reu PL (2013) A study of the influence of calibration uncertainty on the global uncertainty for digital image correlation using a monte carlo approach. Exp Mech 53(9):1661–1680
Christopher Frey H, Patil SR (2002) Identification and review of sensitivity analysis methods. Risk Anal 22(3):553–578
Salehi F, Prasher SO, Amin S, Madani A, Jebelli SJ, Ramaswamy HS, Tan C, Drury CF, Yang CC (2000) Prediction of annual nitrate-N losses in drain outflows with artificial neural networks. Transactions of the ASAE 43(5):1137–1143
Andersson FO, Åberg M, Jacobsson SP (2000) Algorithmic approaches for studies of variable influence, contribution and selection in neural networks. Chemom Intell Lab Syst 51(1):61–72
Geldermann J, Rentz O (2001) Integrated technique assessment with imprecise information as a support for the identification of best available techniques (BAT). OR-Spektrum 23(1):137–157
Sobol’, I. (1990). Sensitivity estimates for nonlinear mathematical models. Matematicheskoe Modelirovanie 2, 112–118. in Russian, translated in English in Sobol’, I. (1993). Sensitivity analysis for nonlinear mathematical models. Mathematical Modeling & Computational Experiment (Engl. Transl.), 1993, 1, 407–414
Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181(2):259–270
Acknowledgements
The authors are grateful for the financial support from SPARC Graduate Research Grant Program, University of South Carolina, under grant numbers # 15540-17-43825 and the Department of Mechanical Engineering, University of South Carolina, through a Teaching Assistantship.
Author information
Authors and Affiliations
Corresponding author
Appendix 1
Appendix 1
Rights and permissions
About this article
Cite this article
Yasmeen, F., Balcaen, R., Sutton, M. et al. Sensitivity of in-Plane Strain Measurement to Calibration Parameter for out-of-Plane Specimen Rotations. Exp Mech 58, 1115–1132 (2018). https://doi.org/10.1007/s11340-017-0370-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11340-017-0370-3