Abstract
Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one from the undeformed reference states of the sample and the other from the deformed target state, the relative displacement between the two states is determined. DIC is well-known and often used for post-processing analysis of in-plane displacements and deformation of the specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and expand the scope of this method. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether the real-time analysis is possible with these methods. The effects of computing with different hardware settings were also analyzed and discussed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm, such that it becomes practically slower than a sub-optimal algorithm. The Newton–Raphson algorithm in combination with a modified particle swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss–Newton algorithm is superior. As expected, the brute force search algorithm is the least efficient method. We also found that the correct choice of parallelization tasks is critical in attaining improvements in computing speed. A poorly chosen parallelization approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode, the correct choice of combinations of integer-pixel and sub-pixel search algorithms is critical for efficient analysis. The real-time analysis using DIC will be difficult on computers with standard computing capabilities, even if parallelization is implemented, so the suggested solution would be to use graphics processing unit (GPU) acceleration.
Highlights
In a wide range of engineering applications, the measurement of displacements and deformations plays an important role [1]
The combination of the Inverse-Compositional Gauss–Newton (IC-GN) algorithm implemented by the author and the modified particle swarm optimization algorithm is even slower than a combination with the standard particle swarm optimization
The difference is small if the brute force search algorithm is used; the difference is larger if the particle swarm optimization or the modified particle swarm optimization are used
Summary
In a wide range of engineering applications, the measurement of displacements and deformations plays an important role [1]. Image Correlation (DIC) gained a lot of popularity, because of its simplicity and accuracy [2] and because of its reliability and wide range of applications [7] Since this method was first published by Peters and Ranson [8] in the early 1980s, the DIC method has been continuously improved by different researchers [6,9,10], while the basic principle remained the same. The main reason for choosing MATLAB for computing DIC algorithms is because MATLAB allows user to implement and test user-defined algorithms [15] It has a large database of built-in algorithms which user can inherit and use them based on the problem requirement [16]. A modular program was developed to investigate the advantages and disadvantages of different computational methods and their combinations These methods were tested, compared, and improved until the most efficient and optimized combination of methods was found. In addition to quantifying the impact of these approaches to high-level parallelization, the impact of hardware settings on the overall performance of DIC computations was evaluated
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