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Experimental Mechanics
Erschienen in: Experimental Mechanics 3/2021

27.10.2020 | Research paper

Fractal Pattern for Multiscale Digital Image Correlation

verfasst von: Raphaël Fouque, Robin Bouclier, Jean-Charles Passieux, Jean-Noël Périé

Erschienen in: Experimental Mechanics | Ausgabe 3/2021

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Abstract

Background:

Digital Image Correlation (DIC) is based on the matching, between reference and deformed state images, of features contained in patterns that are deposited on test sample surfaces. These features are often suitable for a single scale, and there is a current lack of multiscale patterns capable of providing reliable displacement measurements over a wide range of scales.

Objective:

Here, we aim to demonstrate that a pattern based on a fractal (self-affine) surface would make a suitable pattern for multiscale DIC.

Methods:

A method to numerically generate patterns directly from a desired auto-correlation function is introduced. It is then enhanced by a Mean Intensity Gradient (MIG) improvement process based on grey level redistribution. Numerical experiments at multiple scales are performed for two different imposed displacement fields and results for one of the patterns generated are compared with those obtained for a random pattern and a Perlin noise one.

Results:

The proposed pattern is shown to lead to DIC errors comparable to those found with the two others for the first scales, but has much greater robustness. More importantly, the pattern generated here exhibits stable errors and robustness with respect to the scale whereas these two outputs become significantly degraded for the other two patterns as the scale increases.

Conclusions:

As a result, scale invariance properties of the pattern based on fractal surfaces correspond to scale invariance in DIC errors as well. This is of great interest regarding the use of such patterns in multiscale DIC.
Vorheriger Artikel Physical Evidence of Stress-Induced Conformational Changes in Polymers
Nächster Artikel Residual Intensity as a Morphological Identifier of Twinning Fields in Microscopic Image Correlation

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Literatur
1.
Anandan P (1989) A computational framework and an algorithm for the measurement of visual motion. Int J Comput Vis 2(3):283–310
2.
Besnard G, Hild F, Roux S (2006) “finite-element” displacement fields analysis from digital images: Application to portevin–le châtelier bands. Exp Mech 46(6):789–803
3.
Blaysat B, Grédiac M, Sur F (2016) On the propagation of camera sensor noise to displacement maps obtained by dic - an experimental study. Exp Mech 56(6):919–944
4.
Bomarito G, Hochhalter J, Ruggles T (2018) Development of optimal multiscale patterns for digital image correlation via local grayscale variation. Exp Mech 58(7):1169–1180
5.
Bomarito G, Hochhalter J, Ruggles T, Cannon A (2017) Increasing accuracy and precision of digital image correlation through pattern optimization. Opt Lasers Eng 91:73–85
6.
Bossuyt S (2013) Optimized patterns for digital image correlation. In: Jin H, Sciammarella C, Furlong C, Yoshida S (eds) Imaging methods for novel materials and challenging applications, vol 3. Springer, New York, pp 239–248
7.
de Castro CP, Lukovic M, Andrade RFS, Herrmann HJ (2017) The influence of statistical properties of fourier coefficients on random gaussian surfaces. Scientific Reports 7(1):1961
8.
Chen Z, Quan C, Zhu F, He X (2015) A method to transfer speckle patterns for digital image correlation. Measure Sci Technol 26(9):095201
9.
Chevalier L, Marco Y (2002) Tools for multiaxial validation of behavior laws chosen for modeling hyper-elasticity of rubber-like materials. Polymer Engineering & Science 42(2):280–298
10.
Crammond G, Boyd S, Dulieu-Barton J (2013) Speckle pattern quality assessment for digital image correlation. Opt Lasers Eng 51(12):1368–1378
11.
Devroye L (1986) General principles in random variate generation. In: Devroye L. (ed) Non-Uniform random variate generation. Springer, New York, pp 27–82
12.
13.
Funamizu H, Uozumi J, Aizu Y (2013) Digital holographic fractal speckle. J Opt 15 (3):035704
14.
Genovese K, Sorgente D (2018) A morphing-based scheme for large deformation analysis with stereo-dic. Optics and Lasers in Engineering 104:159–172. Optical Tools for Metrology, Imaging and Diagnostics
15.
Grédiac M, Blaysat B, Sur F (2019) Extracting displacement and strain fields from checkerboard images with the localized spectrum analysis. Exp Mech 59(2):207–218
16.
Hild F, Raka B, Baudequin M, Roux S, Cantelaube F (2002) Multiscale displacement field measurements of compressed mineral-wool samples by digital image correlation. Appl Opt 41(32):6815–6828
17.
Horn BK, Schunck BG (1981) Determining optical flow. Artif Intell 17(1):185–203
18.
Kammers A, Daly S (2011) Small-scale patterning methods for digital image correlation under scanning electron microscopy. Measure Sci Technol 22(12):125501
19.
Lavatelli A, Balcaen R, Zappa E, Debruyne D (2019) Closed-loop optimization of dic speckle patterns based on simulated experiments. IEEE Trans Instrum Meas 68(11):4376–4386
20.
Lecompte D, Smits A, Bossuyt S, Sol H, Vantomme J, Hemelrijck DV, Habraken A (2006) Quality assessment of speckle patterns for digital image correlation. Opt Lasers Eng 44(11):1132–1145
21.
LePage W, Shaw J, Daly S (2017) Optimum paint sequence for speckle patterns in digital image correlation. Exp Tech 41(5):557–563
22.
Lionello G, Cristofolini L (2014) A practical approach to optimizing the preparation of speckle patterns for digital-image correlation. Measure Sci Technol 25(10):107001
23.
Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th international joint conference on artificial intelligence - Volume 2, IJCAI’81. Morgan Kaufmann Publishers Inc., San Francisco, pp 674–679
24.
Mathew M, Ellenberg A, Esola S, McCarthy M, Bartoli I, Kontsos A (2018) Multiscale deformation measurements using multispectral optical metrology. Struct Control Health Monit 25:e2166
25.
Neggers J, Blaysat B, Hoefnagels JPM, Geers MGD (2016) On image gradients in digital image correlation. Int J Numer Methods Eng 105(4):243–260MathSciNet
26.
Pan B (2013) Bias error reduction of digital image correlation using gaussian pre-filtering. Opt Lasers Eng 51(10):1161–1167
27.
Pan B (2018) Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals. Measure Sci Technol 29(8):082001
28.
Pan B, Lu Z, Xie H (2010) Mean intensity gradient: an effective global parameter for quality assessment of the speckle patterns used in digital image correlation. Opt Lasers Eng 48(4):469–477
29.
Pan B, Xie H, Wang Z, Qian K, Wang Z (2008) Study on subset size selection in digital image correlation for speckle patterns. Opt Express 16(10):7037–7048
30.
31.
Passieux JC, Bouclier R (2019) Classic and inverse compositional gauss-newton in global dic. Int J Numer Methods Eng 119(6):453–468MathSciNet
32.
Passieux JC, Bugarin F, David C, Périé JNs, Robert L (2015) Multiscale displacement field measurement using digital image correlation: Application to the identification of elastic properties. Exp Mech 55(1):121–137
33.
Périé JN, Calloch S, Cluzel C, Hild F (2002) Analysis of a multiaxial test on a c/c composite by using digital image correlation and a damage model. Exp Mech 42(3):318–328
34.
Pierré JE, Passieux JC, Périé JN (2017) Finite element stereo digital image correlation: Framework and mechanical regularization. Exp Mech 57(3):443–456
35.
Reu P (2014) All about speckles: Aliasing. Exp Tech 38(5):1–3
36.
Reu P (2014) All about speckles: Speckle size measurement. Exp Tech 38(6):1–2
37.
Reu P (2015) All about speckles: Contrast. Exp Tech 39(1):1–2
38.
Reu P (2015) All about speckles: Edge sharpness. Exp Tech 39(2):1–2
39.
Reu P (2015) All about speckles: Speckle density. Exp Tech 39(3):1–2
40.
Roux S, Hild F (2006) Stress intensity factor measurements from digital image correlation: post-processing and integrated approaches. Int J Fract 140(1):141–157MATH
41.
Saupe D (1988) Algorithms for random fractals. Springer, New York, pp 71–136
42.
Scargle JD (1989) Studies in astronomical time series analysis. iii-fourier transforms, autocorrelation functions, and cross-correlation functions of unevenly spaced data. The Astrophysical Journal 343:874–887
43.
Serra J, Pierré J, Passieux J, Périé J, Bouvet C, Castanié B (2017) Validation and modeling of aeronautical composite structures subjected to combined loadings: The vertex project. part 1: Experimental setup, fe-dic instrumentation and procedures. Compos Struct 179:224–244
44.
Shepard MK, Brackett RA, Arvidson RE (1995) Self-affine (fractal) topography: Surface parameterization and radar scattering. Journal of Geophysical Research: Planets 100(E6):11709–11718
45.
Stoilov G, Kavardzhikov V, Pashkouleva D (2012) A comparative study of random patterns for digital image correlation. J Theor Appl Mech 42(2):55–66
46.
Sun Y, Pang JHL, Wong CK, Su F (2005) Finite element formulation for a digital image correlation method. Appl Opt 44(34):7357–7363
47.
Sutton M, Wolters W, Peters W, Ranson W, McNeill S (1983) Determination of displacements using an improved digital correlation method. Image and Vision Computing 1(3):133–139
48.
Tanaka Y, Naito K, Kishimoto S, Kagawa Y (2011) Development of a pattern to measure multiscale deformation and strain distribution via in situ fe-sem observations. Nanotechnology 22(11):115704
49.
Tong W (2005) An evaluation of digital image correlation criteria for strain mapping applications. Strain 41(4):167–175
50.
Triconnet K, Derrien K, Hild F, Baptiste D (2009) Parameter choice for optimized digital image correlation. Opt Lasers Eng 47(6):728–737
51.
Voss RF (1988) Fractals in nature: From characterization to simulation. Springer, New York, pp 21–70
52.
Voss RF (1991) Random fractals: characterization and measurement. Springer, Boston, pp 1–11
53.
Wagne B, Roux S, Hild F (2002) Spectral approach to displacement evaluation from image analysis. Eur Phys J AP 17(3):247–252
54.
Wang H, Xie H, Li Y, Zhu J (2012) Fabrication of micro-scale speckle pattern and its applications for deformation measurement. Measure Sci Technol 23(3):035402
55.
Wang YQ, Sutton MA, Bruck HA, Schreier HW (2009) Quantitative error assessment in pattern matching: Effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements. Strain 45(2):160–178
56.
Wang Z, Li H, Tong J, Shen M, Aymerich F, Priolo P (2008) Dual magnification digital image correlation based strain measurement in cfrp laminates with open hole. Compos Sci Technol 68 (9):1975–1980
57.
Winiarski B, Schajer G, Withers P (2012) Surface decoration for improving the accuracy of displacement measurements by digital image correlation in sem. Experimental Mechanics 52(7):793– 804
58.
Yaofeng S, Pang JH (2007) Study of optimal subset size in digital image correlation of speckle pattern images. Opt Lasers Eng 45(9):967–974
59.
Zhang J, Sweedy A, Gitzhofer F, Baroud G (2018) A novel method for repeatedly generating speckle patterns used in digital image correlation. Opt Lasers Eng 100:259–266
60.
Zhou Y, Sun C, Song Y, Chen J (2015) Image pre-filtering for measurement error reduction in digital image correlation. Opt Lasers Eng 65:46–56. Special Issue on Digital Image Correlation
Metadaten
Titel
Fractal Pattern for Multiscale Digital Image Correlation
verfasst von
Raphaël Fouque
Robin Bouclier
Jean-Charles Passieux
Jean-Noël Périé
Publikationsdatum
27.10.2020
Verlag
Springer US
Erschienen in
Experimental Mechanics / Ausgabe 3/2021
Print ISSN: 0014-4851
Elektronische ISSN: 1741-2765
DOI
https://doi.org/10.1007/s11340-020-00649-7

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