DIC Displacement Measurement Method Based on Improved White Shark Optimizer

TL;DR

This paper introduces an improved white shark optimizer combined with a surface fitting method to enhance the efficiency and accuracy of digital image correlation displacement measurements, validated through simulations and steel tensile tests.

Contribution

It proposes novel improvements to the white shark optimizer and surface fitting technique for more efficient and accurate displacement measurement in digital image correlation.

Findings

Computational efficiency comparable to particle swarm optimization.

Search success rate reaches 100%.

Accuracy similar to Newton-Raphson with higher efficiency.

Abstract

The traditional integer-pixel displacement search algorithm of digital image correlation method has low computational efficiency and has been gradually eliminated, and some intelligent optimization algorithms have their own strengths and weaknesses. The white shark optimizer has excellent global search capabilities. However, its calculation is cumbersome, programming complex and inefficient. In order to improve the computational efficiency of the white shark optimizer, it is improved by using the Tent map, introducing the dynamic nonlinear time factor, setting the automatic termination condition and adding the three-step search method. The improved white shark optimizer is applied to the integer-pixel displacement search. In order to improve the accuracy and efficiency of sub-pixel displacement calculation, an improved surface fitting method is proposed by combining bicubic interpolation, improved white shark optimizer and surface fitting method. Through grayscale interpolation, the distance between the fitting points is reduced, and the accuracy of search of the surface fitting method is further improved. The performance of the improved white shark optimizer and the improved surface fitting method is tested by simulated speckle pattern. Test results revealed that the computational efficiency of the improved white shark optimizer is comparable to that of the particle swarm optimization, and the search success rate is as high as 100%. The calculation accuracy of the improved surface fitting method is comparable to that of the Newton-Raphson algorithm, but the computational efficiency is much higher than that of the Newton-Raphson algorithm. Finally, the tensile experiment of low-carbon steel is used to verify the feasibility of the improved surface fitting method in actual measurement.