Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization

Fayad, S. S.; Seidl, Daniel; Reu, Phillip L.

doi:10.1007/s11340-019-00553-9

Cited by 29 publications

(29 citation statements)

References 12 publications

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“…Spurious random fluctuations decorrelated with the displacement encoded in the deformed image are clearly visible on the left-hand side of Figure 13-a and -c. These random fluctuations correspond to the pattern-induced bias described in [44,46], and modeled in detail in [47]. In [45,46,47], it is shown that this bias is much more pronounced when random speckles instead of regular patterns are used, which is confirmed here by comparing Figure 13-a and -c on the one hand, and Figure 13-b and -d on the other hand. It is worth emphasizing that these spurious random fluctuations are still visible on the right-hand side of the vertical blue line whatever the degree of the matching function (see Figure 13-a and -c).…”

Section: Typical Maps Without Deconvolutionsupporting

confidence: 74%

“…This effect can be quantified by calculating the standard deviation of these quantities column-wise in these maps. • apart from for the highest frequencies (on the left in the figure), second-degree matching functions lead to a lower standard deviation than first-degree ones in the case of DIC, which is logical, undermatching being one of the main causes of pattern-induced bias, [46,47];…”

Section: Influence Of the Frequencymentioning

confidence: 99%

“…For instance, it is well-known that interpolation induces an error in DIC calculations [4]. Pattern itself also causes some spatial spurious fluctuations called pattern-induced bias to appear in the displacement fields, but this phenomenon has only been recently addressed in the DIC literature [44,45,46,47]. Finally, it is also worth remembering that the true signal is quantized.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Optimal Pattern for Displacement Field Measurement: Random Speckle and DIC, or Checkerboard and LSA?

2020

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This paper deals with the optimal pattern that can be used to retrieve displacement fields by minimizing the optical residual calculated over small regions of contrasted images. This minimization is generally performed in the spatial domain by processing speckle patterns with DIC. Another option is also considered here. It consists in switching this minimization to the Fourier domain. The benefit is that periodic patterns can be processed, which is generally not possible with DIC. It turns out that the optimal pattern in terms of sensor noise propagation is theoretically the checkerboard if it is correctly sampled, and this pattern is periodic. The reason why checkerboard is optimal is that the image gradient is maximum in this case. In addition, the minimization of the image residual in this case has a quasi-direct solution, which considerably speeds up the calculations. We first recall the basics of the different techniques used in the paper, namely classic subset-based DIC, and a spectral method called Localized Spectrum Analysis (LSA). A recent deconvolution procedure introduced to enhance

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Section: Typical Maps Without Deconvolutionsupporting

confidence: 74%

Section: Influence Of the Frequencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Optimal Pattern for Displacement Field Measurement: Random Speckle and DIC, or Checkerboard and LSA?

2020

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“…It is shown in [14] that while DIC consists in minimizing the optical residual over small zones in the spatial domain, LSA minimizes the same optical residual in the Fourier domain. The advantage is threefold: i-this minimization is quasi-direct, which leads to much reduced computing times compared to DIC, ii-the effect of sensor noise in the resulting displacement and strain maps is generally lower than with DIC, and iiithe pattern-induced bias recently introduced in [15,16] is lower with periodic patterns than with the random ones classically used with DIC [16,17]. The drawback of this technique is that a periodic pattern has to be deposited onto the sample, which is much less convenient than spraying random patterns used with DIC, and that only flat specimens can be used.…”

Section: Team 6: Institut Pascal Of the Clermont-auvergne Universitymentioning

confidence: 99%

Multi-partner benchmark experiment of fatigue crack growth measurements

Langlois

Cusset

Hosdez

et al. 2020

Engineering Fracture Mechanics

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The design of reliable structures and the estimation of the residual fatigue life of industrial parts containing flaws or cracks rely on our ability to predict the propagation of fatigue cracks. Whereas in industrial component cracks might have a complex path due to geometry and loading, lab experiments used for identifying crack propagation law are often in pure mode I. The paper presents a synthesis of an experimental benchmark performed in the

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“…This conclusion is drawn if the nearest neighbor forward difference scheme (and not the central difference scheme) is used to numerically estimate the gradient [3,6]. In [7], it is also shown that checkerboards, as other periodic patterns [8], induce lower pattern-induced bias than random speckles usually employed with DIC. Checkerboards are however quasi-periodic patterns and as such, they cannot be processed by DIC, except if the iterative minimization starts close to the solution to avoid convergence toward a local minimum [6], or if displacement continuity is enforced from a given point where the displacement can be considered as reliable.…”

Section: Introductionmentioning

confidence: 98%

Comparing several spectral methods used to extract displacement fields from checkerboard images

Grédiac

Blaysat

Sur

2020

Optics and Lasers in Engineering

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Checkerboard represents the best pattern for in-plane displacement measurement in terms of sensor noise propagation because this pattern maximizes image gradient. It also exhibits other interesting properties in terms of pattern-induced bias for instance. Digital Image Correlation (DIC) is not the best option to extract displacement fields from such periodic patterns, and spectral methods should be used instead. In this paper, it is shown that three different spectral techniques initially developed for classic bidimensional grids can be adapted to process checkerboard images. These three techniques are the Geometric Phase Analysis (GPA), the windowed version of the Geometric Phase Analysis (WGPA), and the Localized Spectrum Analysis (LSA), which can be regarded as the ultimate version of WGPA. The main features of these three techniques as well as the link between them are given in this paper. Their metrological performance are compared in terms of displacement resolution, spatial resolution and bias. Synthetic checkerboard images deformed with a suitable reference displacement field are considered for this purpose. It is shown that GPA is the fastest method. According to the metric used in this paper, the best metrological performance is obtained with WGPA with suitable settings. LSA followed by a deconvolution algorithm is just behind, but the calculation time is approximately 10 times lower than that of WGPA for the examples considered in this paper, which makes it a reasonable choice for the determination of in-plane displacement fields from checkerboard images.

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Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization

Cited by 29 publications

References 12 publications

On the Optimal Pattern for Displacement Field Measurement: Random Speckle and DIC, or Checkerboard and LSA?

On the Optimal Pattern for Displacement Field Measurement: Random Speckle and DIC, or Checkerboard and LSA?

Multi-partner benchmark experiment of fatigue crack growth measurements

Comparing several spectral methods used to extract displacement fields from checkerboard images

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