Development of Optimal Multiscale Patterns for Digital Image Correlation via Local Grayscale Variation

In many applications of digital image correlation (DIC), it is advantageous to have measurements at multiple scales. Because it is rare to have natural features that can be used for DIC at multiple magnifications, an appropriately multiscale DIC pattern is needed. This work develops a multiscale DIC pattern that (1) contains features appropriate for both high and low magnification, (2) does not need to know the location of high magnification a priori, and (3) does not require specialized DIC equipment beyond what is necessary to achieve the two magnifications. The pattern is developed based on an optimization framework that minimizes expected DIC error while constraining sub-regions of the pattern to biased average grayscale values. The inclusion of local grayscale biases in the pattern has the effect of introducing resolvable features at a length scale much larger than the speckles of which the pattern is composed. Numerical and physical experiments were performed to illustrate the functionality and utility of the designed patterns. Notable among the findings is the trade off between DIC accuracy at the two scales and how it is controlled by grayscale bias.     

基于数字图像体相关的物体内部三维位移场分析

在二维数字图像相关算法的基础上,推导了三维数字图像体相关算法,并应用于物体内部的三维位移场分析。用计算机模拟方法对SR-CT重建的物体内部三维图像施加已知变形,对变形前后三维数字图像进行体相关运算,获得三维位移场。在计算亚像素位移时,本文提出了一种三维的基于灰度梯度的算法,给出了梯度函数,阐述了数字模拟三维体相关的计算过程,通过数字模拟实验证明了算法的正确性和可靠性。     

Leveraging Full-Field Measurement from 3D Digital Image Correlation for Structural Identification

Within the domain of structural health monitoring (SHM) measurement techniques have primarily relied on discrete sensing strategies using sensors physically attached to the structural system of interest. These sensors have proven effective in describing both global and local phenomena, but are limited to providing discrete response measurements of these systems. With the introduction of novel imaging tools and image analysis techniques, such as digital image correlation (DIC), the ability to measure the full-field response of these systems provides a novel approach to refining structural identification (St-ID) approaches used in SHM. This paper explores this proposed concept through a case study on a series of structural test specimens analyzed using 3D digital image correlation (3D-DIC) for St-ID. Finite element model updating (FEMU) was used as the technique for the structural identification. For the identification process, ABAQUS was interfaced with MATLAB to converge on the optimal unknown/uncertain system parameters of the experimental setup. 3D-DIC results provided a rich full-field dataset for the identification process, which was compared against measurements derived from traditional physical in-place sensors typically used in SHM. In this work a Hybrid Genetic Algorithm (HGA), which combines the genetic algorithm as a global optimization and a gradient-based method as a local optimization, was used for the FEMU based on 3D-DIC results of structural specimen subjected to variable loading. To minimize the error between the full field 3D-DIC measurements and FEA model updating results, an objective function was introduced that included the full-field contributions of strains and deformation response. The evolution of this objective function illustrated satisfactory convergence of the identified parameters and the excellent agreement of the experimental and numerical strain and displacement responses after the model updating process confirmed the success of the proposed approach. The results of this study highlight the advantage of this hybrid approach and provide the foundation for effective deployment of the proposed strategy for large-scale structural systems.     

A Comparison of 3 Digital Image Correlation Techniques on Necessarily Suboptimal Random Patterns Recorded By X-Ray

Dynamic x-rays have been used to follow the deformation ahead of a steel ball fired into a mock-up of a generic cylindrical rocket motor. The impact was arranged to intersect a sparse lead powder layer within the mock explosive that created a random speckle pattern on x-ray film. Three different digital image correlation programs are compared to examine any sensitivity to the sub-optimal speckle pattern produced by the lead powder. An identical output data reduction method was used in all cases to aid comparison. All three correlation methods were able analyze the deformation, but all had intricacies that would require more detailed optimization of the data reduction in order to fully exploit the technique. Quantitative analysis showed that the three methods agreed closely in estimations of rigid body displacements between a pair of representative x-ray images. It was discovered that the deformation caused by the ball impact was highly localized and the useful data available about the deformation pattern was sparse. This limits the applicability of this technique to this specific application. Extensive cracking was not observed that would have aided the development of computer-based models for prediction of such impact events. The x-ray technique was however excellent for determining the ball position as a function of time after impact.     

Development of Patterns for Digital Image Correlation Measurements at Reduced Length Scales

Methods for patterning metal thin films at the microscale and nanoscale by applying the patterns to metallic and polymeric materials for use in shape and deformation measurements in a scanning electron microsope (SEM) or other high magnification imaging system are described. In one approach, thin films of metallic materials (e.g., Au, Ag, Cu, and Cr) are applied to a variety of substrates. The coated samples are then placed into a reaction vessel, where the specimens are heated and exposed to a nitrogen atmosphere saturated with selected volatile chemicals. This process results in nano-scale remodeling of the metallic films, thereby affording high contrast random patterns with different morphologies. In a second approach, thin films of metallic materials, including gold and silver, also have been applied using a simplified UV photolithographic method requiring a minimum amount of laboratory preparation. Using selected substrates, both methods have been used successfully to transfer patterns onto polymeric and metallic materials ranging from 50–500 nanometers with chemical vapor rearrangement and 2 to 20 microns with UV photolithography, providing a pattern that can be used with digital image correlation to quantify both the surface profile and also surface deformations at reduced length scales.     

Combining Image Compression with Digital Image Correlation

Experimental Mechanics - Digital image correlation (DIC) is a powerful experimental technique to determine displacement and strain fields. DIC methods usually require a large number of high...     

Augmented Lagrangian Digital Image Correlation

Experimental Mechanics - Digital image correlation (DIC) is a powerful experimental technique for measuring full-field displacement and strain. The basic idea of the method is to compare images of...     

Digital Image Correlation and Color Cameras

Digital Image Correlation (DIC) is a well-known experimental technique. It works by constructing a (surjective) mapping of pixel intensity from reference to target image, where the mapping parameters are identified using a Least Squares approach. Because it makes use of the luminance component of the image, Digital Image Correlation is usually implemented by assuming monochrome cameras. In this work, we will discuss its implementation when color cameras are used, focusing on pitfalls and potential advantages of this solution. Since most cameras implement color acquisition using a Color Filter Array (CFA), much of the article will focus on this technology. However, we will not limit ourselves to this aspect and will show that Three-CCD cameras can provide significant advantages over both CFA and monochrome cameras.     

Real-time 3D Digital Image Correlation for Large Deformation and Rotation Measurements Based on a Deformation Transfer Scheme

Experimental Mechanics - With the recent increase in digital image correlation (DIC) applications, it is important to improve the computational efficiency of DIC and realize the closed loop control...     

Recent Progress in Digital Image Correlation

In this paper, we report the following important progress recently made in the basic theory and practical implementation of digital image correlation (DIC) for deformation measurement. First, we answer a basic but confusing question to the users of DIC: what is a good speckle pattern for DIC? We present a simple, easy-to-compute yet effective global parameter, called mean intensity gradient, for quality assessment of the entire speckle pattern. Second, we provide an overview of various correlation criteria used in DIC for evaluating the similarity of the reference and deformed subsets, and demonstrate the equivalence of three robust and most widely used correlation criteria, i.e., a zero-mean normalized cross-correlation (ZNCC) criterion, a zero-mean normalized sum of squared difference (ZNSSD) criterion and a parametric zero-mean normalized sum of squared difference (PSSD_ab) criterion with two additional unknown parameters, which elegantly unifies these correlation criteria for subset-based pattern matching. Third, we describe an iterative least squares (ILS) algorithm for accurate subpixel motion detection, which is proved to be equivalent to the existing Newton–Raphson algorithm, but the principle and implementation of ILS algorithm is more straightforward and easier. Finally, to overcome the two limitations of existing subset-based DIC technique, we introduce a robust and generally applicable reliability-guided DIC technique, in which the calculation path is guided by the ZNCC coefficients of computed points, to determine the genuine full-field deformation of an object with complex shape.     

Micro- and Nanoscale Deformation Measurement of Surface and Internal Planes via Digital Image Correlation

The digital image correlation (DIC) technique is successfully applied across multiple length scales through the generation of a suitable speckle pattern at each size scale. For microscale measurements, a random speckle pattern of paint is created with a fine point airbrush. Nanoscale displacement resolution is achieved with a speckle pattern formed by solution deposition of fluorescent silica nanoparticles. When excited, the particles fluoresce and form a speckle pattern that can be imaged with an optical microscope. Displacements are measured on the surface and on an interior plane of transparent polymer samples with the different speckle patterns. Rigid body translation calibrations and uniaxial tension experiments establish a surface displacement resolution of 1 μm over a 5×6 mm scale field of view for the airbrushed samples and 17 nm over a 100×100 μm scale field of view for samples with the fluorescent nanoparticle speckle. To demonstrate the capabilities of the method, we characterize the internal deformation fields generated around silica microspheres embedded in an elastomer under tensile loading. The DIC technique enables measurement of complex deformation fields with nanoscale precision over relatively large areas, making it of particular relevance to materials that possess multiple length scales.     

A 3D Displacement Control by Digital Image Correlation for the Multiaxial Testing of Materials with a Stewart Platform

A Digital Image Correlation technique is proposed to control a quasi-static 6 degrees of freedom testing machine. This machine is based on a hexapod architecture, allowing displacements of several tens of centimeters and degrees, and with force capacities of several tens of kN and kN.m. The control technique is based on the measurement of the set of actuator lengths, from images of the end-effector of the machine. A firstattempt is shown with a single camera. Thoughrelevant, the results present too high uncertainties for the aimed applications. Several cameras are then used to improve the 3D rigid body motion displacement measurement of the end-effector. Thecamera positions are free, with an automatic calibration method assessing the needed coefficients for the control. The set of actuator lengths is then searched by global minimization of the difference between the current image and the reference image of each camera (i.e., the minimization is not performed independently for each camera). The algorithm is implemented on Graphical Processing Units to achieve computation times lower than 50 ms. An in-depth experimental validation of the whole setup is performed. For an actuator length range of 200 μm, the actuator length uncertainties are around 0.4 μm with 3 cameras. The errors of displacement of the end-effector are less than 2 μm, partially due to the validation setup itself.     

Digital Image Correlation Using Stochastic Parallel-Gradient-Descent Algorithm

This article proposes a digital image correlation (DIC) method based on the stochastic parallel gradient descent (SPGD) algorithm. Stochastic parallel perturbations are imposed on deformation parameters to make the correlation coefficients converge to a global extremum; thus, this allows the final measured values of the deformation parameters to be obtained and the DIC measurement to be made. Both simulated and real data processing, including rigid body and strain deformation, show that the proposed method can achieve nearly the same accuracy as the Newton–Raphson (NR) method in most cases and higher accuracy in some cases, such as the simulated experiments of rigid body translation with and without noise. It also has a good noise-robustness. Furthermore, a series of experiments have been designed to evaluate the convergence characteristics of the proposed method, and it has been proved able to process large displacement and have a stable convergence process, good robustness, and a high convergence speed when bilinear interpolation is adopted.     

Digital Image Correlation with Self-Adaptive Gaussian Windows

A novel subpixel registration algorithm with Gaussian windows is put forward for accurate deformation measurement in digital image correlation technique. Based on speckle image quality and potential deformation states, this algorithm can automatically minimize the influence of subset sizes by self-adaptively tuning the Gaussian window shapes with the aid of a so-called weighted sum-of-squared difference correlation criterion. Numerical results of synthetic speckle images undergoing in-plane sinusoidal displacement fields demonstrate that the proposed algorithm can significantly improve displacement and strain measurement accuracy especially in the case with relatively large deformation.     

Cross Polarization for Improved Digital Image Correlation

Digital image correlation (DIC) is a surface deformation measurement technique for which accuracy and precision are sensitive to image quality. This work presents cross polarization, the use of orthogonal linear polarizers on light source(s) and camera(s), as an effective method for improving optical DIC measurements. The benefits of cross polarization are characterized through quantitative and statistical comparisons from two experiments: rigid body translation of a flat sample and uniaxial tension of a superelastic shape-memory alloy (SMA). In both experiments, cross polarization eliminated saturated pixels that degrade DIC measurements, and increased image contrast, which enabled higher spatial precision by using smaller subsets. Subset sizes are usually optimized for correlation confidence interval (typically with subsets of 21×21 px or larger), but can be decreased to achieve the highest possible spatial precision at the expense of increased correlation confidence intervals. Smaller subset sizes (such as 9×9 px) require better images to maintain correlation within error thresholds. By comparing DIC results from a uniaxial SMA tension test with unpolarized and cross-polarized images, we show that for 9×9 px subsets, the loss of valid DIC data points was reduced almost ten-fold with cross polarization. The only disadvantage we see to cross polarization is the decrease in specimen illumination due to transmission losses through the polarizers, which can easily be accommodated with sufficiently intense light sources. With the installation of relatively inexpensive linear polarizing filters, an optimum optical DIC setup can provide even better DIC measurements by delivering images without saturated pixels and with higher contrast for increased DIC spatial precision.     

Fractal Pattern for Multiscale Digital Image Correlation

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.

     

Absolute Nodal Coordinates in Digital Image Correlation

A great deal of progress has been made in recent years in the field of global digital image correlation (DIC), where higher-order, element-based approaches were proposed to improve the interpolation performance and to better capture the displacement fields. In this research, another higher-order, element-based DIC procedure is introduced. Instead of the displacements, the elements’ global nodal positions and nodal position-vector gradients, defined according to the absolute nodal coordinate formulation, are used as the searched parameters of the Newton–Raphson iterative procedure. For the finite elements, the planar isoparametric plates with 24 nodal degrees of freedom are employed to ensure the gradients’ continuity among the elements. As such, the presented procedure imposes no linearization on the strain measure, and therefore indicates a natural consistency with the nonlinear continuum theory. To verify the new procedure and to show its advantages, a real large deformation experiment and several numerical tests on the computer-generated images are studied for the standard, low-order, element-based digital image correlation and the presented procedure. The results show that the proposed procedure proves to be accurate and reliable for describing the rigid-body movement and simple deformations, as well as for determining the continuous finite strain field of a real specimen.     

网格数字图像相关方法测量位移场的研究

本文将数字图像相关方法与古老的网格法相结合,给出了一种测量面内位移场的新方法——网格数字图像相关方法,实现了高精度的测量,精度可达0.02亚像素．在该方法中网格点采用圆标记点,使该方法特别适用于小线应变位移场的研测．本文运用Windows下的新的图像采集方法测量物体位移场,突破了Dos系统下对内存操作的限制,并使每一帧图像的像素数由512×512增至768×576,提高了图形的分辨率．