Performance Of Digital Image Correlation Research Articles

An analytical study on the low-pass filtering effect of digital image correlation caused by under-matched shape functions

The low-pass filtering effect, which stems from undermatching of shape function, sincerely affects the metrological performance of digital image correlation, especially for localized deformations. Here, we build a theoretical model characterizing the low-pass filtering effect, which explicitly expresses the measured displacement in terms of subset size, shape function order, and underlying deformation field. Based on the model, we derive formulae for transfer function of digital image correlation and study the attenuation of Gaussian displacement fields. These results provide a deep insight into shape function undermatching and open up new possibilities for compensating the low-pass filtering effect.

Optimization of speckle pattern based on integer programming method

The performance of digital image correlation (DIC) method heavily depends on the quality of the speckle pattern. Extensive studies have been conducted to evaluate the speckle pattern, in order to reduce the errors of DIC. It is generally understood that the sum of square of subset intensity gradients (SSSIG) is the most relevant metric to the accuracy of the DIC; and the higher SSSIG indicates the better quality of the speckle pattern. However, there are few works on the optimization of the speckle pattern. In this work, we propose an integer programming method (IPM) to obtain the subset having the highest SSSIG. The speckle pattern derived from the proposed IPM has a relatively high SSSIG and has a strong anti-noise capability. The regular patterns (e.g. checkerboard-type) can also be avoided by the proposed method. Both the simulation and experiment have been conducted to validate the effectiveness of the proposed method. The simulation results show that the standard deviation (STD) of the displacements obtained by using the proposed speckle pattern in this study is reduced by 25.2% and 10.7%, respectively, comparing with the results obtained by the conventional random speckle pattern and the optimized speckle pattern in our earlier work. In addition, the experimental results from a tensile testing also agreed to the results from the simulation.

Theoretical analysis on performance of digital speckle pattern: uniqueness, accuracy, precision, and spatial resolution.

The performance of digital image correlation is closely associated with the quality of speckle pattern. In this paper, the performance of digital speckle pattern is analyzed theoretically concerning four critical factors: uniqueness, accuracy, precision, and spatial resolution. Pattern uniqueness could be characterized by secondary autocorrelation peak height; based on a theoretical analysis on autocorrelation function of digital speckle pattern, analytical formulas are derived to estimate the secondary autocorrelation peak height. Measurement accuracy and precision are descriptions of systematic error and random error respectively; by deriving analytical expression for power spectrum of digital speckle pattern, theoretical models are built for analyzing both systematic errors and random errors. Spatial resolution characterizes the ability of a given technique to distinguish close features; empirical formulas are presented to describe the dependence of spatial resolution upon subset size and shape function order; besides, a rudimentary model is proposed, which provides conservative estimates for spatial resolution. Considering all these four factors, we make recommendations for selection of generation parameters of digital speckle pattern, which can eventually improve the measurement performance of digital image correlation technique.

Quality assessment of speckle patterns for DIC by consideration of both systematic errors and random errors

The performance of digital image correlation (DIC) is influenced by the quality of speckle patterns significantly. Thus, it is crucial to present a valid and practical method to assess the quality of speckle patterns. However, existing assessment methods either lack a solid theoretical foundation or fail to consider the errors due to interpolation. In this work, it is proposed to assess the quality of speckle patterns by estimating the root mean square error (RMSE) of DIC, which is the square root of the sum of square of systematic error and random error. Two performance evaluation parameters, respectively the maximum and the quadratic mean of RMSE, are proposed to characterize the total error. An efficient algorithm is developed to estimate these parameters, and the correctness of this algorithm is verified by numerical experiments for both 1 dimensional signal and actual speckle images. The influences of correlation criterion, shape function order, and sub-pixel registration algorithm are briefly discussed. Compared to existing methods, method presented by this paper is more valid due to the consideration of both measurement accuracy and precision.