Feature Extraction of Welding Defects using Artificial Neural Network |
Author(s): |
| Amandeep Kaur , Ganga Institute of technology & Management, Kablana; Puneet Garg, Ganga Institute of technology & Management, Kablana |
Keywords: |
| Electron beam welding, Artificial neural network, Signal processing, Defect detection. Test blanket Module, Ultrasonic testing |
Abstract |
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The detection and classification of the defects in the welded components are very important in order to ensure the structural integrity of the fabricated components of the test blanket module (TBM). RAFM steel is used as structural material for the TBM, therefore ultrasonic based technique are the most suitable for high sensitive defect detection. In this work ultrasonic pulse echo technique is used to perform the experiment and the ANNs (artificial neural networks) technique is used to detection and classification of the defects in the welded region. For this study, artificial defect (Side drilled hole, notch and flat bottom hole) are fabricated in the welded region. In this paper this data acquisition from different type of defects and extraction of feature from these signal are discussed. |
Other Details |
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Paper ID: IJSRDV5I21411 Published in: Volume : 5, Issue : 2 Publication Date: 01/05/2017 Page(s): 1543-1545 |
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