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Artificial Neural Network based Defect Detection of Welds in TOFD Technique

by S.lalithakumari, B.sheelarani, B.venkatraman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 20
Year of Publication: 2012
Authors: S.lalithakumari, B.sheelarani, B.venkatraman
10.5120/5808-8069

S.lalithakumari, B.sheelarani, B.venkatraman . Artificial Neural Network based Defect Detection of Welds in TOFD Technique. International Journal of Computer Applications. 41, 20 ( March 2012), 17-20. DOI=10.5120/5808-8069

@article{ 10.5120/5808-8069,
author = { S.lalithakumari, B.sheelarani, B.venkatraman },
title = { Artificial Neural Network based Defect Detection of Welds in TOFD Technique },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 20 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 17-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number20/5808-8069/ },
doi = { 10.5120/5808-8069 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:30:06.131642+05:30
%A S.lalithakumari
%A B.sheelarani
%A B.venkatraman
%T Artificial Neural Network based Defect Detection of Welds in TOFD Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 20
%P 17-20
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Time of Flight Diffraction Technique is one of the NDE methods, used in weld inspection to identify the weld defects. The classi?cation of defects using the TOFD technique depends on the knowledge and experience of the operator. The classi?cation reliability of defects detected by this technique can be improved by applying the Artificial Neural Network. In this work, four austenitic stainless steel weldments with defects viz, Lack of Fusion, Lack of Penetration, Slag, Porosity and one with out any Defect were fabricated. TOFD experiment is conducted on these weldments. Discrete wavelet transform based denoising methods were applied to denoise the resultant A scan signals. Time scale features are extracted from the denoised signals. A multi layer feed forward network with Resilient Back Propagation algorithm has been applied for classification of the signals. The number of hidden layers in the network are increased from 0 to 6. Various performance functions are also employed to achieve a better classification efficiency. The results are promising to proceed the automatic defect classification by TOFD technique.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Resilient Back Propagation Algorithm Time Scale Features Classification Accuracy

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