NDT Technology

NDT Technology

Detection and Sizing of Fatigue Cracks in Metallic Structures from Eddy Current Probe Signals

Document Type : Original Article

Authors
Teymour Heidari
Seyed Hosein Sadeghi
Abstract
The use of Eddy Current (EC) technique has proved to be a viable means for crack detection and sizing in metals. In this technique, a crack is evaluated from the observation of probe impedance changes due to the interaction between induced current in a metal and the crack. Since the growth of fatigue crack in metals is a stochastic process, the cracks do not have a constant predetermined shape. Detection and Sizing of the cracks from probe signals witch called inverse problem is important for predicting the life of the workpiece. In this paper, an inverse algorithm based on the particle swarm optimization (PSO) technique is proposed for predicting the crack depth profile from EC probe signals. The validity of the proposed algorithm is demonstrated by comparing the actual and reconstructed depth profiles from measurement EC probe signals.
In this paper, an inverse algorithm based on the particle swarm optimization (PSO) technique is proposed for predicting the crack depth profile from EC probe signals. The validity of the proposed algorithm is demonstrated by comparing the actual and reconstructed depth profiles from measurement EC probe signals.
Keywords
Nondestructive testing
Eddy current
Inverse algorithm
Particle Swarm Optimization
1. R. I. Stephens, Metal Fatigue in Engineering, Wiley, New York, 2001.
2. Halmshaw, R. (1987). Non-Destructive Testing. Edward Arnold, 2th ed., London.
3. Burrows, M.L. (1964). Theory of eddy-current flaw detection. PH.D Dissertation University of Michigan.
4. Dodd, C. V. and Deeds, W.E. (1968). Analytical solutions to eddy-current prob-coil problems. J. Appl. Phys., 39, 2829-2838.
5. Mirshekar-Syahkal, D., Mostafavi, R.F. (1997). Analysis Technique for Interaction of High-Frequency Rhombic Inducer Field with Cracks in Metals. IEEE Trans. Magn, 33, 2291-2298.
6. Xiaoyunl, S., Donghui, L., Kai2 G., Liweil, Z., Ran, Z., and Jianye, L. (2004). Neural Network with Adaptive Genetic Algorithm for Eddy Current Nondestructive Testing. In: IEEE Proc. of the 5th World Congress on Intelligent Control and Automation. (pp. 2034-2037), Hangzhou, China.
7. Chady, T., Enkizono, M., Sikora, R. (2000). Neural Network Models of Eddy Current Multifrequency System for Nondestructive Testing. IEEE Trans. Magn., 36, 1724-1727.
8. Preda, G., Popa, R. C., Demachi, K., and Miya, K. (1999). Neural Network for Inverse Mapping in Eddy Current Testing. IEEE International joint Conf. on Neural Networks, 6, 4033-4036.
9. Li, Y., Udpa, L., and Udpa, S. S. (2004). Three-Dimensional Defect Reconstruction from Eddy-Current NDE Signals Using a Genetic Local Search Algorithm. IEEE Trans. Magn., 40, 410-417.
10. Badies, Z., Pavo, J., Komatsu, H., Kojima, S., Matsumoto, Y., and Aoki, K. (1998). Fast Flaw Reconstruction from 3D Eddy Current Data. IEEE Trans. Magn., 34, 2823–2828.  
11. Bowler, J.R. (1994). Eddy-current interaction with an ideal crack. J. Appl. Phys.75, 8128-8137.
12. Kennedy, J., and Eberhart, R.C. (1995). Particle swarm optimization”, IEEE International Conference on Neural Networks, 4, (pp. 1942-1948), Perth, WA, Australia.
13. Eberhart, R.C., and Shi, Y. (2001). Particle swarm optimization: Developments, applications and resources. IEEE Int. Conf. Evolutionary Computation, 1. Seoul, South Korea.

  • Receive Date 13 May 2018
  • Accept Date 14 June 2018