This paper presents work on a natural crack identification problem from eddy current testing (ECT) signals. ECT is a widely used in-service Nondestructive testing (NDT) technique. A crucial problem in ECT is to inverse flaw profile from testing signals. Iterative inversion algorithms are commonly used to solve this problem. Typical iterative inversion approaches use a numerical forward model to predict the measurement signal from a given defect profile. But the use of numerical models is computationally intensive. In this study, the reconstruction of natural crack shapes from the ECT signals is realized by utilizing artificial neural networks as the forward solver and applying a metaheuristics-based optimization method. The crack is successfully reconstructed that verified both the efficiency of the artificial neural network forward scheme and the feasibility of the metaheuristics-based inversion method.

Intelligent optimization is a vibrant area of investigation, with some of the widely known and used approaches being Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization Algorithm. An inversion algorithm for the reconstruction of natural crack shape from eddy current testing signals is developed by using an artificial neural network based forward model and intelligent inversion algorithm. The true crack shapes and the measured eddy current testing signals are used to train the neural network. The parameters of the algorithms are modified, their effectiveness on crack shape inversion problem arc compared and discussed. The reconstructed results verified the efficiency of neural network based forward model and the promising of intelligent optimization algorithms in crack shape inversion.

No abstract