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Vaithiyanathan V.,SASTRA University | Venkataraman B.,Radiological Safety and Environmental Group | Anishin Raj M.M.,SASTRA University
Journal of Theoretical and Applied Information Technology | Year: 2011

X-ray radiography is commonly used in (NDT) Non-destructive Testing, for identifying defects in weld. When the X-ray is passed through the weld object, the area where the defects are occurred will be having different intensity profile, than the nearby pixels. Most of the X-ray radiographic images will be having some forms of noise components embedded in it. Median filter is applied for noise removal, followed by gamma correction for image enhancement which made the image more operative. For the segmentation of the weld defect, watershed method is performed. Through watershed segmentation process, the defective regions are segmented out, without oversegmentation problem. Standard derivation and mean of the Projection Profile of the radiographic image along with RST invariants features are used for feature extraction. In this work, we fed the feature extracted to a Learning Vector Quantization (LVQ) for training, with four different output classes, where each class corresponds to different classes or types of weld defects like Cluster Porosity, Slag inclusions, Lack of Penetration (LOP) and Burn-Through. The result shows that the proposed system is highly efficient in classifying different types of weld defects. © 2005 - 2011 JATIT & LLS. All rights reserved. Source


Vaithiyanathan V.,SASTRA University | Anishin Raj M.M.,SASTRA University | Venkataraman B.,Radiological Safety and Environmental Group
European Journal of Scientific Research | Year: 2011

Non destructive testing (NDT) is the technique of identifying the properties of material without making any damage. Due to errors in welding, weld defect arises, the process of identifying or detecting the weld defect is an important application in the field of Non-Destructive Testing (NDT).Analysis and comparison of various segmentation methods for weld defects identification such as Lack of Penetration, Porosity and Oxide Inclusion is presented in this paper with experimental results. Identifying weld defects using human eye is almost impossible, since the radio-graphic images will be very dark and low in contrast. Image segmentation is the process of identifying objects from the images using mathematical concepts which is a time consuming and tough task. Defects or discontinuation in welding occurs due to various reasons such as gas entrapment, lack of penetration, too much heat, oxide inclusion, etc. This paper, we performed Watershed, Hough Transform and Region Growing segmentation. The results are compared and conclusions are achieved. © EuroJournals Publishing, Inc. 2011. Source


Sudheera K.,Sathyabama University | Nandhitha N.M.,Sathyabama University | Nanekar P.,Bhabha Atomic Research Center | Venkatraman B.,Radiological Safety and Environmental Group | Rani B.S.,Sathyabama University
Proceedings of the 2013 International Conference on Advanced Electronic Systems, ICAES 2013 | Year: 2013

Ultrasonic Testing is a highly reliable Non-Destructive Testing Technique for weld defect characterization. Defects occur either high frequency components (Porosity, Sidewall crack) or as low frequency components (Root, Lack of Fusion, Lack of penetration, slag) in the UT signal. Manual interpretation of these signals is subjective in nature and is dependent on the expertise of the individual. Hence it is necessary to develop automated signal analysis system that classifies the defect. As defect classification is non-linear in nature, neural network based classification techniques are cited in literature. However neural network based techniques are computationally complex and has prediction error. Hence in this paper, an effective range based classification system using statistical moments is proposed. Performance of the proposed technique is measured in terms sensitivity and specificity. ©2013 IEEE. Source


Sudheera K.,Sathyabama University | Nandhitha N.M.,Sathyabama University | Ganesh N.V.S.L.,Sathyabama University | Nanekar P.,Bhabha Atomic Research Center | And 2 more authors.
International Conference on Communication and Signal Processing, ICCSP 2014 - Proceedings | Year: 2014

Ultrasonic Testing is the widely used NDT technique for flaw detection in thick walled weldments. It is an indirect technique and the signals are to be analyzed in order to characterize the flaw. Manual interpretation of these signals is subjective in nature and is dependent on the expertise of the individual. Hence the paradigm has shifted to automated signal analysis. In this paper a successful attempt has been made to develop a pattern among the flaws of same type without using Artificial Neural Networks. Here, the signals are analyzed with Stockwell transform and the pattern is determined. Also quantitative characterization is done with mean, standard deviation, root mean square value, peak to rms ratio. © 2014 IEEE. Source


Anishin Raj M.M.,SASTRA University | Venkataraman B.,Radiological Safety and Environmental Group | Vaithiyanathan V.,SASTRA University
European Journal of Scientific Research | Year: 2012

This paper presents comparative study and experimentation of simultaneous Reconstruction Technique (SART) and Ordered Subsets Expectation Maximization (OSEM). The SART and OSEM methods are used to reconstruct the object from the X-ray projection obtained on the detector. The process of creating back the object image from the Radon Transform of the object is known as Image Reconstruction. Image reconstruction is a famous and interesting field which comes under computed tomography. Computed Tomography is used in NDT for identifying the hidden or inner defects of objects by reconstructing the image through Filtered back projection or iterative reconstruction technique. In this paper Simultaneous Algebraic Reconstruction technique and Ordered Subsets Expectation Maximization methods are implemented and the experimented results are compared using performance parameters for various test cases and conclusion is achieved. © EuroJournals Publishing, Inc. 2012. Source

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