Issue 28

B. Ye et alii, Frattura ed Integrità Strutturale, 28 (2014) 32-41 ; DOI: 10.3221/IGF-ESIS.28.04 40 Group 1 Group2 Group 3 Group 4 True Length (mm) 16 17 18 19 TrueHeight (mm) 0.8 1 0.8 1 TrueDepth (mm) 1.2 1.7 1.2 1.7 Estimated length (mm) 16.65 17.25 17.74 19.53 EstimatedHeight (mm) 0.91 0.94 0.83 1.08 EstimatedDepth (mm) 1.17 1.65 1.3 1.78 Length error (%) 4.06 1.47 -1.44 2.79 Height error (%) 13.75 -6 3.75 8 Depth error (%) -2.5 -2.94 8.33 4.71 Table 5 : Identificationof defect using the least squaremethod. Comparing Tab. 4, using IACA, and Tab. 5, using the least square method, the results show that the former has higher precision and robustness than the latter. C ONCLUSION n iterative improved ant colony algorithm has been implemented with FEM to determine the defect size in ECNDT. Defect size is automatically obtained and material parameters of the structure can be obtained more effectively than the existing ECNDT method. The main advantage of this method is its easy implementation. Particularly, the resolution of FEM has been pre-calculated. The only work is to write a few lines of code in order to define parameters to be optimized, the objective function and the constraints. The improved fast inversemethodwith the database computed in advance distinctly reduces the computing time of the whole optimization. The possibility of quantifying size of deep defects in multi-layered structures is demonstrated. A decrement in human errors and faster inspections canbe expectedby completely automatic inspection. A CKNOWLEDGMENT his work is supported by the National Natural Science Foundation of China Grant No. 51105183, the Research Fund for the Doctoral Program of Higher Education of China Grant No. 20115314120003, the Applied Basic Research Programs of Science and Technology Commission Foundation of Yunnan Province of ChinaGrant No. 2010ZC050, the Foundation of Yunnan Educational Committee Grant No. 2013Z121, the National College Student InnovationTraining ProgramFunded ProjectsGrant No. 201210674014, the Science and Technology Project of Yunnan PowerGridCorporationGrantNo. K-YN2013-110. R EFERENCES [1] Takagi, T., Huang, H., Fukutomi, H., Tani, J., Numerical evaluation of correlation between crack size and eddy current testing signal by a very fast simulator, IEEETrans.Magn., 34 (1998) 2581–2584. [2] Auld, B.A., Moulder, J.C., Review of advances in quantitative eddy current nondestructive evaluation, J. Nondestr. Eval., 18 (1999) 3-36. [3] Ye, B., Cai, J., Huang, P., Fan, M., Gong, X., Hou, D., Zhang, G., Zhou, Z., Automatic classification of eddy current signals based on kernelmethods, Nondestruct. Test. Eva., 24 (2009) 19-37. [4] Mandache, C., Khan, M., Fahr, A., Yanishevsky, M., Numerical modelling as a cost-reduction tool for probability of detectionof bolt hole eddy current testing,Nondestruct. Test. Eva., 26 (2011) 57-66. [5] Shubochkin, A.E., Eddy-current testing of the quality of railway wheels, Russ. J. Nondestruct. Test., 41 (2005) 189- 192. [6] Kojima, F., Fujioka, T., Quantitative evaluation of material degradation parameters using nonlinear magnetic inverse problems, Int. J. Appl. Electrom., 25 (2007) 57-163. A T