Weld Flaw Detection Based on Likelihood Estimation and Wavelet Transform
Mohanasundari L1, Sivakumar P2
1Mohanasundari L, Assistant Professor, Department of Electronics and Communication Engineering, Kingston Engineering College, Vellore (Tamil Nadu), India.
2Sivakumar P, Professor, Department of Electronics and Communication Engineering, Dr. N.G.P. Institute of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 30 September 2019 | Revised Manuscript received on 12 November 2019 | Manuscript Published on 22 November 2019 | PP: 1734-1738 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F13260986S319/19©BEIESP | DOI: 10.35940/ijeat.F1326.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The metals are fused at high temperature using various welding methods to form mechanical structures. The structural stability is achieved in metals by the proper welding process utilized. The failure may result in disaster and huge investment has to be incurred towards building the flawless structures. It is always preferred to check the quality of the weld before the final welded structure is used for its actual application. Though visual inspections could solve problems tentatively valid for low production rates, there are scenarios where visual inspection fails and needs high end methods to analyze the quality of welded joints. Several measurement techniques have evolved and help the user community. The objective of this paper is reviewing such earlier methods in the relevant domain of research and tabulates the various merits and demerits so as to find a method to overcome earlier drawbacks. Further, edge detection is done based on adaptive thresholding method to find the weld flaws.
Keywords: Radiographic Image, Classification, Segmentation, Enhancement, Genetic Algorithm.
Scope of the Article: Aggregation, Integration, and Transformation