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Scanning-Digital Image Correlation for Moving and Temporally Deformed Surfaces in Scanning Imaging Mode

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Abstract

Background

Images from scanning electron microscopes, transmission electron microscopes and atomic force microscopes have been widely used in digital image correlation methods to obtain accurate full-field deformation profiles of tested objects and investigate the object’s deformation mechanism. However, because of the raster-scanning imaging mode used in microscopic observation equipment, the images obtained from these instruments can only be used for quasi-static displacement measurements; otherwise, spurious displacements and strains may be introduced into the deformation results if these scanning microscopic images are used directly in general digital image correlation calculations for moving and temporally deformed surfaces.

Objective

Realizing kinematic parameter and dynamic deformation measurements on a scanning electron microscope platform.

Methods

Establishing a scanning imaging model of moving and temporally deformed objects that contains motion and deformation equations, a scanning equation and an intensity invariance assumption for small deformations. Then proposing a scanning-digital image correlation (S-DIC) method based on combing the characteristics of the scanning imaging mode with digital image correlation.

Results

Quantitatively investigating the effects of the spurious displacements and strains introduced when using scanning images to represent moving and temporally deformed surfaces in the measurement results. Numerical simulations verify that the accuracy of the S-DIC method is 10−2pix for the displacement, 10−4 for the strain, 10−4pix/s for the velocity and 10−6s−1 for the strain rate. Experiments also show that the proposed S-DIC method is effective. Conclusions: The results of this work demonstrate the utility of S-DIC on the field of microscopic dynamic measurement.

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References

  1. Sutton MA, Hild F (2015) Recent advances and perspectives in digital image correlation. Exp Mech 55(1):1–8

    Article  Google Scholar 

  2. Li N, Sutton MA, Li X, Schreier HW (2007) Full-field thermal deformation measurements in a scanning electron microscope by 2D digital image correlation. Exp Mech 48(5):635–646

    Article  Google Scholar 

  3. Sun YF, Pang JR, Wei F, Shi XQ (2006) Micro- and nano-DIC deformation analysis for electronic packaging applications. Electronic Packaging Technology Conference 1:290–296

    Google Scholar 

  4. Liang JC, Wang Z, Xie HF, Li XD (2018) In situ scanning electron microscopy-based high-temperature deformation measurement of nickel-based single crystal superalloy up to 800 °C. Opt Laser Eng 108:1–14

    Article  Google Scholar 

  5. Mello AW, Book TA, Nicolas A, Otto SE, Gilpin CJ, Sangid MD (2017) Distortion correction protocol for digital image correlation after scanning electron microscopy: emphasis on long duration and ex-situ experiments. Exp Mech 57(9):1395–1409

    Article  Google Scholar 

  6. Gao PF, Lei ZN, Wang XX, Zhan M (2019) Deformation in fatigue crack tip plastic zone and its role in crack propagation of titanium alloy with tri-modal microstructure. Mater Sci Eng A 739:198–202

    Article  Google Scholar 

  7. Xu LJ, Tian XR, Li XL, Shang GY, Yao JN (2011) Geometric distortion correction for sinusoidally scanning images. Meas Sci Technol 22(11):9

    Article  Google Scholar 

  8. Burglin TR (2000) A two-channel four-dimensional image recording and viewing system with automatic drift correction. J Microsc 200:75–80

    Article  Google Scholar 

  9. Spagnoli C, Beyder A, Besch SR, Sachs F (2007) Drift-free atomic force microscopy measurements of cell height and mechanical properties. Rev Sci Instrum 78(3):3

    Article  Google Scholar 

  10. Marturi N, Dembele S, Piat N (2013). Fast image drift compensation in scanning electron microscope using image registration. IEEE international conference on automation science and engineering: 807-812

  11. Pluska M, Czerwinski A, Ratajczak J, Katcki J, Rak R (2006) Elimination of scanning electron microscopy image periodic distortions with digital signal-processing methods. J Microsc 224:89–92

    Article  MathSciNet  Google Scholar 

  12. Poncharal P, Wang ZL, Ugarte D, de Heer WA (1999) Electrostatic deflections and electromechanical resonances of carbon nanotubes. Science 283(5407):1513–1516

    Article  Google Scholar 

  13. Yang B, Xuan FZ (2018) Creep behavior of subzones in a CrMoV weldment characterized by the in-situ creep test with miniature specimens. Mater Sci Eng A 723:148–156

    Article  Google Scholar 

  14. Guo Y, Li D, Zhang S, Yang Y, Liu JJ, Wang X, Liu C, Milkie DE, Moore RP, Tulu US, Kiehart DP, Hu J, Lippincott-Schwartz J, Betzig E, Li D (2018) Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales. Cell 175(5):1430–1442

    Article  Google Scholar 

  15. Jin P, Li X (2015) Correction of image drift and distortion in a scanning electron microscopy. J Microsc 260(3):268–280

    Article  Google Scholar 

  16. Maraghechi S, Hoefnagels JPM, Peerlings RHJ, Rokoš O, Geers MGD (2019) Correction of scanning electron microscope imaging artifacts in a novel digital image correlation framework. Exp Mech 59(4):1–28

    Article  Google Scholar 

  17. Sutton MA, Li N, Garcia D, Cornille N, Orteu JJ, McNeill SR, Schreier HW, Li X, Reynolds AP (2007) Scanning electron microscopy for quantitative small and large deformation measurements part II: experimental validation for magnifications from 200 to 10,000. Exp Mech 47(6):789–804

    Article  Google Scholar 

  18. Sutton MA, Li N, Garcia D, Cornille N, Orteu JJ, McNeill SR, Schreier HW, Li X (2006) Metrology in a scanning electron microscope: theoretical developments and experimental validation. Meas Sci Technol 17(10):2613–2622

    Article  Google Scholar 

  19. Jung KO, Kim SJ, Kim DH (2012) An approach to reducing the distortion caused by vibration in scanning electron microscope images. Nucl Instrum Methods Phys Res Sect A 676:5–17

    Article  Google Scholar 

  20. Tracy J, Waas A, Daly S, Hsueh CH (2015) A new experimental approach for in situ damage assessment in fibrous ceramic matrix composites at high temperature. J Am Ceram Soc 98(6):1898–1906

    Article  Google Scholar 

  21. Lu J, Chang L, Wang J, Sang L, Wu S, Zhang Y (2018) In-situ investigation of the anisotropic mechanical properties of laser direct metal deposition Ti6Al4V alloy. Mater Sci Eng A 712:199–205

    Article  Google Scholar 

  22. Payton OD, Picco L, Scott TB (2016) High-speed atomic force microscopy for materials science. Int Mater Rev 61(8):473–494

    Article  Google Scholar 

  23. Mohammed OF, Yang DS, Pal SK, Zewail AH (2011) 4D scanning ultrafast electron microscopy: visualization of materials surface dynamics. J Am Chem Soc 133(20):7708–7711

    Article  Google Scholar 

  24. Yang DS, Mohammed OF, Zewail AH (2010) Scanning ultrafast electron microscopy. Proc Natl Acad Sci 107(34):14993–14998

    Article  Google Scholar 

  25. Gilles JP, Megherbi S, Raynaud G, Parrain F, Mathias H, Leroux X, Bosseboeuf A (2008) Scanning electron microscopy for vacuum quality factor measurement of small-size MEMS resonators. Sens Actuators A 145:187–193

    Article  Google Scholar 

  26. Shao LC, Wong CL, Palaniapan M (2008) Study of the nonlinearities in micromechanical clamped–clamped beam resonators using stroboscopic SEM. J Micromech and Microeng 18(8):085019

    Article  Google Scholar 

  27. Wong CL, Wong WK (2007) In-plane motion characterization of MEMS resonators using stroboscopic scanning electron microscopy. Sens Actuators A 138(1):167–178

    Article  MathSciNet  Google Scholar 

  28. Pan B, Xie H, Xu B, Dai F (2006) Performance of sub-pixel registration algorithms in digital image correlation. Meas Sci Technol 17(6):1615–1621

    Article  Google Scholar 

  29. Lavatelli A, Zappa E (2017) A displacement uncertainty model for 2-D DIC measurement under motion blur conditions. IEEE T Instrum Meas 66(3):451–459

    Article  Google Scholar 

  30. Zappa E, Matinmanesh A, Mazzoleni P (2014) Evaluation and improvement of digital image correlation uncertainty in dynamic conditions. Opt Laser Eng 59:82–92

    Article  Google Scholar 

  31. Ye X, Cui ZG, Fang HJ, Li XD (2017) A multiscale material testing system for in situ optical and electron microscopes and its application. Sens 17(8):1–21

    Article  Google Scholar 

  32. Pan B (2009) Reliability-guided digital image correlation for image deformation measurement. Appl Opt 48(8):1535–1542

    Article  Google Scholar 

  33. Lenthe WC, Stinville JC, Echlin MP, Chen Z, Daly S, Pollock TM (2018) Advanced detector signal acquisition and electron beam scanning for high resolution SEM imaging. Ultramicroscopy 195:93–100

    Article  Google Scholar 

  34. Reu PL, Toussaint E, Jones E, Bruck HA, Iadicola M, Balcaen R, Turner DZ, Siebert T, Lava P, Simonsen M (2018) DIC challenge: developing images and guidelines for evaluating accuracy and resolution of 2D analyses. Exp Mech 58(7):1067–1099

    Article  Google Scholar 

  35. Blaber J, Adair B, Antoniou A (2015) Ncorr: open-source 2D digital image correlation matlab software. Exp Mech 55(6):1105–1122

    Article  Google Scholar 

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Acknowledgements

We would like to thank Ms. Man-Qiong Xu for her help with the in-Situ experiments in AML, school of Aerospace Engineering, Tsinghua University, Beijing, China. Thanks for the financial support of the National Natural Science Foundation of China (grant numbers 11632010, and 11872035).

Funding

This work was financially supported by the National Natural Science Foundation of China (grant numbers 11632010, and 11872035).

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Authors and Affiliations

  1. Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, China

    H. Xie, Z. Wang, J. Liang & X. Li

  2. Center for Nano and Micro Mechanics, Tsinghua University, Beijing, 100084, China

    H. Xie, Z. Wang, J. Liang & X. Li

Authors
  1. H. Xie
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  2. Z. Wang
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  3. J. Liang
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  4. X. Li
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Correspondence to X. Li.

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Appendix

Appendix

To further verify the performance of the proposed S-DIC method, we apply the published image data provided by Society for Experimental Mechanics (https://sem.org/2ddic) [34] and S-DIC to calculate the displacement and strain fields they contain. Although these images are not captured with a scanning method, S-DIC can calculate the displacement and strain fields of the measured objects contained in these images in the same way as the traditional DIC method, as long as we set the scanning parameters\( {t}_D^{\prime }=0 \), tR = 0 and \( {t}_D^{\prime }={t}_E \) (tE is the Exposure time). Here, we select images of three motion and deformation modes, the Strain Gradient (contrast: 60 to 130), the Plate Hole (experiment 1) and the Rigid Motion experiment [34]. The calculation results are shown in Figs. 15 and 16, respectively. Figure 15(a) shows the Y-direction displacement fields of the first and tenth deformed images calculated by S-DIC with subset = 21, step = 5 for the rigid motion experiment (Sample 16 in the public image data). The mean displacements are −0.1003 pixels and − 1.006 pixels while the stage positions are at −0.1001 pixels and − 1.002 pixels respectively. These results indicate that the displacement accuracy of S-DIC could reach 10−3 pixels for such global imaging. The calculation results for non-uniformly deformed images caused by lens distortion (lower part of Fig. 15 (a)) show that our calculation results are consistent with those in [34]. Figure 15(b) shows the principal strain field of the image “oht_cfrp_11.tiff” for the plate hole specimen (Sample 12 in the public image data [34]) calculated by S-DIC with subset = 15, step = 3, and strain window = 5. Line cut plots show the principal strain taken vertically through the center of the specimen. The distribution of principal strain of the cut line is the same as the result in [34], the principal strain is changes from 0.0008 to 0.0068. The left picture of Fig. 16 shows the Y-direction strain field of the image “aab_b2_05.tif” calculated by S-DIC with subset = 21, step = 5, and strain window = 15 for the sinusoidal strain gradient (Sample 11 in the public image data) [34]. The right picture of Fig. 16 shows the strain of the cut line taken vertically through the center of the specimen. We compare the results calculated by S-DIC and ncorr-2D [35] under the same conditions in the right picture, which indicate that the strain values calculated by S-DIC and the general DIC method are consistent.

The above results show that our proposed S-DIC method has the same ability to calculate the displacement and strain fields for the global image as the conventional DIC method. As the conclusion given in the article, the S-DIC method is a further development and extension of the DIC method in the scanning imaging mode.

Fig. 15
figure 15

Specific image sets, the Rigid Motion experiment (Sample 16) and the Plate Hole (Sample 12), from Society for Experimental Mechanics (https://sem.org/2ddic) [34] and displacement and strain fields calculated by S-DIC

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Fig. 16
figure 16

Specific image sets, the Strain Gradient (Sample 11), from Society for Experimental Mechanics (https://sem.org/2ddic) [34] and the strain in Y direction calculated by S-DIC and ncorr-2D [35]

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Xie, H., Wang, Z., Liang, J. et al. Scanning-Digital Image Correlation for Moving and Temporally Deformed Surfaces in Scanning Imaging Mode. Exp Mech 60, 1079–1101 (2020). https://doi.org/10.1007/s11340-020-00634-0

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  • DOI: https://doi.org/10.1007/s11340-020-00634-0

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