Abstract
A methodology is developed to detect defects in NDT of materials using an Artificial Neural Network and signal processing technique. This technique is proposed to improve the sensibility of flaw detection and to classify defects in Ultrasonic testing. Wavelet transform is used to derive a feature vector which contains two-dimensional information on various types of defects. These vectors are then classified using an ANN trained with the back propagation algorithm. The inputs of the ANN are the features extracted from each ultrasonic oscillogram. Four different types of defect are considered namely porosity, lack of fusion, and tungsten inclusion and non defect. The training of the ANN uses supervised learning mechanism and therefore each input has the respective desired output. The available dataset is randomly split into a training subset (to update the weight values) and a validation subset. With the wavelet features and ANN, good classification at the rate of 94% is obtained. According to the results, the algorithms developed and applied to ultrasonic signals are highly reliable and precise for online quality monitoring.
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References
Martın, O., Lopez, M., Martın, F.: Artificial neural networks for quality control by ultrasonic testing in resistance spot weldin. J. Mater. Process. Technol. 183, 226–233 (2007)
Cau, F., Fanni, A., Montisci, A., Testoni, P., Usai, M.: Signal-processing tool for non-destructive testing of inaccessible pipe. Eng. Appl. Artif. Intell. 19, 753–760 (2006)
Veiga, J.L.B.C., de Carvalho, A.A., da Silva, I.C., Rebello, J.M.A.: The use of artificial neural network in the classification of pulse-echo and TOFD ultra-sonic signals. J. Braz. Soc. Mech. Sci. Eng. 27(4), 394–398 (2005)
Bettayeb, F., Rachedi, T., Benbartaoui, H.: An improved automated ultrasonic NDE system by wavelet and neuron networks. Ultrasonics 43, 853–858 (2004)
Moura, E.P., Silva, R.R., Siqueira, M.H.S., Rebello, J.M.A.: Pattern recognition of weld defects in preprocessed TOFD signals using linear classifiers. J. Nondestruct. Eval. 23(4), 163–172 (2004)
Drai, R., Khelil, M., Benchaala, A.: Time frequency and wavelet transform applied to selected problems in ultrasonics NDE. NDT&E Int. 35, 567–572 (2002)
Obaidat, M.S., Suhail, M.A., Sadoun, B.: An intelligent simulation methodology to characterize defects in materials. Inf. Sci. 137, 33–41 (2001)
Margrave, F.W., Rigas, K., Bradley, D.A., Barrowcliffe, P.: The use of neural networks in ultrasonic flaw detection. Measurement 25, 143–154 (1999)
Graps, A.S.: An introduction to wavelets. IEEE Comput. Sci. Eng. 2(2), 50–61 (1995)
Burrus, C.S., Gopinath, R.A., Guo, H.: Introducing to Wavelets and Wavelet Transformation. Prentice-Hall, Englewood Cliffs (1998)
İşcan, Z., Kurnaz, M.N., Dokur, Z., Olmez, T.: Ultrasound image segmentation by using wavelet transform and self-organizing neural network. Neural Inf. Process. 10(89), 183–191 (2006)
Mallat, S.: A theory for multi resolution signal processing: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)
The Math Works, Inc. 1998: Neural Network Toolbox for use with Matlab 5.3/Version 2007, Natick, MA
Selvakumar, N., Radha, P., Narayanasamy, R., Joseph Davidson, M.: Prediction of deformation characteristics of sintered aluminium performs using neural networks. Model. Simul. Mater. Sci. Eng. 12(4), 611–620 (2004)
McBride, J., Malinov, S., Sha, W.: Modelling tensile properties of gamma based titanium aluminides using artificial neural network. Mater. Sci. Eng. A 384, 129–137 (2004)
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Sambath, S., Nagaraj, P. & Selvakumar, N. Automatic Defect Classification in Ultrasonic NDT Using Artificial Intelligence. J Nondestruct Eval 30, 20–28 (2011). https://doi.org/10.1007/s10921-010-0086-0
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DOI: https://doi.org/10.1007/s10921-010-0086-0