Three-dimensional digital image correlation system for deformation measurement in experimental mechanics

A three-dimensional (3-D) digital image correlation system for deformation measurement in experimental mechanics has been developed. The key technologies applied in the system are discussed in detail, including stereo camera calibration, digital image correlation, 3-D reconstruction, and 3-D displacement/strain computation. A stereo camera self-calibration algorithm based on photogrammetry is proposed. In the algorithm, the interior and exterior orientation parameters of stereo cameras and the 3-D coordinates of calibration target points are estimated together, using the bundle adjustment technique, so the 3-D coordinates of calibration target points are not needed in advance to get a reliable camera calibration result. An efficient image correlation scheme with high precision is developed using the iterative least-squares nonlinear optimization algorithm, and a method based on a seed point is proposed to provide a reliable initial value for the nonlinear optimization. After the 3-D coordinates of the object points are calculated using the triangulation method, the 3-D displacement/strain field could then be obtained from them. After calibration, the system accuracy for static profile, displacement, and strain measurement is evaluated through a series of experiments. The experiment results confirm that the proposed system is accurate and reliable for deformation measurement in experimental mechanics.

[1]  U. Halici,et al.  3D face reconstruction using stereo images and structured light , 2008, 2008 IEEE 16th Signal Processing, Communication and Applications Conference.

[2]  Jeffrey D. Helm,et al.  Improved three-dimensional image correlation for surface displacement measurement , 1996 .

[3]  W. F. Ranson,et al.  Applications of digital-image-correlation techniques to experimental mechanics , 1985 .

[4]  A. Asundi,et al.  Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements , 2009 .

[5]  W. H. Peters,et al.  Application of an optimized digital correlation method to planar deformation analysis , 1986, Image Vis. Comput..

[6]  Stuart Robson,et al.  Close Range Photogrammetry , 2007 .

[7]  Michael A. Sutton,et al.  Advances in light microscope stereo vision , 2004 .

[8]  Michael A. Sutton,et al.  Deformations in wide, center-notched, thin panels, part I: three-dimensional shape and deformation measurements by computer vision , 2003 .

[9]  Michael A. Sutton,et al.  Application of stereo vision to three-dimensional deformation analyses in fracture experiments , 1994 .

[10]  M. A. Sutton,et al.  Systematic errors in digital image correlation caused by intensity interpolation , 2000 .

[11]  Huimin Xie,et al.  Full-field strain measurement using a two-dimensional Savitzky-Golay digital differentiator in digital image correlation , 2007 .

[12]  Jianwei Liu,et al.  Videogrammetric system for dynamic deformation measurement during metal sheet welding processes , 2010 .

[13]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[14]  Anand Asundi,et al.  Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review , 2009 .

[15]  Hugh Alan Bruck,et al.  Digital image correlation using Newton-Raphson method of partial differential correction , 1989 .

[16]  M. A. Sutton,et al.  Accurate measurement of three-dimensional deformations in deformable and rigid bodies using computer vision , 1993 .

[17]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Michael A. Sutton,et al.  Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading , 2007 .

[19]  Thomas S. Huang,et al.  BOOK REVIEW: Calibration and Orientation of Cameras in Computer Vision , 2001 .