Akshatha Aravind1 and Arun VA2
1Department of Electronics and Communication, KR Gouri Amma College of
Engineering, Cherthala, Alappuzha, Kerala, India
2Department of QA and QC, Nesma and Partners, Damam, Saudi Arabia
*Corresponding Author: Akshatha Aravind, Department of Electronics and Communication, KR Gouri Amma College of Engineering, Cherthala, Alappuzha, Kerala, India.
Received: December 27, 2019; Published: December 31, 2019
Thermal imaging is simply the technique of using the heat given off by an object to produce an image of it or locate it. Several thermal imaging frameworks for the detection of defects in welded joints have been introduced in literature. Weld joints are the origin of structural weakness in maximum cases and must be routinely inspected to ensure structural integrity of the fabricated components. Hence the defect detection on welded joints has become a significant task, as it provides the fundamental information for semantic understanding of the scene. This research aims to develop a specialized algorithm that would use the defect’s heat signature for detection, density of defect and applications that takes benefit of this technology. The algorithm is mainly divided into several segments which go like object capturing, Temperature decay variation, filtering, validation etc. It may embed a module for the automatic determination of the range of defect temperature variation using the learn and adapt technology of neurons. This technology surpasses previous approaches like Edge detectors, morphological operators, finding interest points and region of interest, features matching. Certainly, the research outperforms the result obtained by visible images with the boon of most advanced non visible spectrum thermal cameras.
Keywords: Thermal Image; Non - Destructive Evaluation; Infrared Thermography; Adaptive Neural Network
Citation: Akshatha Aravind and Arun VA. “Non Destructive Evaluation of Welded Joints in Hermal Bands Using Neural Networks”. Acta Scientific Computer Sciences 2.1 (2020): 23-27.
Copyright: © 2020 Akshatha Aravind and Arun VA. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.