Defect Detection in XLPE Material Using Terahertz Wave-based Non-destructive Testing

2021 ◽  
Vol 71 (3) ◽  
pp. 305-309
Author(s):  
Min-Gyu BAE ◽  
In-Sung LEE ◽  
Joong Wook LEE
Keyword(s):  
Defect Detection ◽  
Terahertz Wave ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

A REVIEW ON EDDY CURRENT THERMOGRAPHY TECHNIQUE FOR NON-DESTRUCTIVE TESTING APPLICATION

2016 ◽  
Vol 78 (11) ◽  
Author(s):  
N. S. Rusli ◽  
I. Z. Abidin ◽  
S. A. Aziz
Keyword(s):  
Defect Detection ◽  
Eddy Current ◽  
Infrared Camera ◽  
Heat Diffusion ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Quantitative Characterisation ◽  
Optimum Heating ◽  
Non Destructive ◽  
Eddy Current Heating

Eddy current thermography is one of the non-destructive testing techniques that provide advantages over other active thermography techniques in defect detection and analysis. The method of defect detection in eddy current thermography has become reliable due to its mode of interactions i.e. eddy current heating and heat diffusion, acquired via an infrared camera. Such ability has given the technique the advantages for non-destructive testing applications. The experimental parameters and settings which contribute towards optimum heating and defect detection capability have always been the focus of research associated with the technique. In addition, the knowledge and understanding of the characteristics heat distribution surrounding a defect is an important factor for successful inspection results. Thus, the quantitative characterisation of defect by this technique is possible compared to the conventional non-destructive which only acquired qualitative result. In this paper, a review of the eddy current thermography technique is presented which covers the physical principles of the technique, associated systems and its applications. Works on the application of the technique have been presented and discussed which demonstrates the ability of eddy current thermography for non-destructive testing of conductive materials.   

scholarly journals Benefit of Neural Network for the Optimization of Defect Detection on Composite Material Using Ultrasonic Non Destructive Testing

2021 ◽  
Author(s):  
P. Trouvé-Peloux ◽  
B. Abeloos ◽  
A. Ben Fekih ◽  
C. Trottier ◽  
J.-M. Roche
Keyword(s):  
Defect Detection ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Learning Approaches ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Experimental Database ◽  
Out Of Plane ◽  
Non Destructive

Abstract This paper is dedicated to out-of-plane waviness defect detection within composite materials by ultrasonic testing. We present here an in-house experimental database of ultrasonic data built on composite pieces with/without elaborated defects. Using this dataset, we have developed several defect detection methods using the C-scan representation, where the defect is clearly observable. We compare here the defect detection performance of unsupervised, classical machine learning methods and deep learning approaches. In particular, we have investigated the use of semantic segmentation networks that provides a classification of the data at the “pixel level”, hence at each C-scan measure. This technique is used to classify if a defect is detected, and to produce a precise localization of the defect within the material. The results we obtained with the various detection methods are compared, and we discuss the drawbacks and advantages of each method.

Research on Non-Destructive Testing for Defects of Steel Bar in Concrete

2013 ◽  
Vol 351-352 ◽  
pp. 143-147
Author(s):  
Jing Yang ◽  
Wei Heng Yuan ◽  
Jun Tan
Keyword(s):  
Defect Detection ◽  
Electromagnetic Induction ◽  
Experimental Result ◽  
Non Destructive Testing ◽  
Test Device ◽  
Destructive Testing ◽  
Structure Detection ◽  
Steel Bar ◽  
Non Destructive ◽  
Magnetic Scanner

Steel bar defect detection in concrete is an important content of civil engineering structure detection. Currently there are no effective methods for nondestructive testing of steel bar defects . This paper studies the application of electromagnetic induction technology for Steel bar defect detection. Firstly, the principle of electromagnetic induction technology to detect rebar are described. Secondly,an air dielectric test device was designed and Steel bar defect in the device was detected by magnetic scanner. Through analyzing we got the characteristics of scanning images from different Steel bar defects. Thirdly this experimental result was compared with detection result in concrete.Finally verify the accuracy and feasibility of this method.

Ultra-Compact Eddy Current Transducer for Corrosion Defect Search in Steel Pipes

2022 ◽  
Vol 1049 ◽  
pp. 282-288
Author(s):  
S.F. Dmitriev ◽  
Vladimir Malikov ◽  
Alexey Ishkov ◽  
Sergey Voinash ◽  
Marat Kalimullin ◽  
...  
Keyword(s):  
Defect Detection ◽  
Eddy Current ◽  
Detection System ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Steel Pipes ◽  
Operational Characteristics ◽  
Margin Of Safety ◽  
Design And Manufacture ◽  
Non Destructive

This research is devoted to the application of non-destructive testing methods for detecting defects of the internal structure of the material in steel pipelines. Despite the use of modern approaches to the design and manufacture of pipelines, which make it possible to lay a significant margin of safety in the created system, the task of developing new approaches to measuring the technical and operational characteristics and parameters of steel parts using software and hardware complexes for non-destructive testing does not lose its relevance. The paper presents the results of the development of defect detection system aimed at detecting damage of the structure of the material with a diameter of 0.2 mm and located at a depth of up to 2 mm. The proposed system is based on the physical principles of the influence of the existing defect on the value of the transformer voltage, which is induced in the measurement circuit of the sensor built on eddy current effects. The focus of the research is the relationship between the linear dimensions of the defect, its location and the generated voltage indications of the developed sensor. Also, within the framework of the study, the results of processing and analysis of the data collected by the defect detection system are presented, the result of which was the determination of the parameters of the detected defects.

scholarly journals Characterization and Comparison of Defects Detection Limits of Ultrasonic Non Destructive Techniques

2012 ◽  
Vol 498 ◽  
pp. 79-88 ◽  
Author(s):  
Elodie Péronnet ◽  
Florent Eyma ◽  
Hélène Welemane ◽  
Sébastien Mistou
Keyword(s):  
Defect Detection ◽  
Composite Structures ◽  
Aviation Industry ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Critical Defect ◽  
Critical Defect Size ◽  
Ultrasonic Techniques ◽  
Liquid Resin Infusion ◽  
Non Destructive

This work deals with the Liquid Resin Infusion (LRI) process developed within the research program “FUSelage COMPosite” of DAHER SOCATA. This manufacturing process enables the realization of complex composite structures or fuselage elements in a single phase (mono-material), which considerably reduce connections and relative difficulties. The concern here is the investigation of non destructive testing (NDT) methods that can be applied to LRI-structures in order to define their capacities for defect detection, and especially their associated critical defect size. In aviation industry, the AITM standards require the ultrasonic testing as NDT for composite materials. Therefore the aim of this work is to characterize and compare three different and complementary ultrasonic techniques on composite specimens. Such analysis allows to define the NDT application field of each method in term of defect detection.

scholarly journals Automatic defect detection from thermographic non destructive testing

2019 ◽  
Vol 1249 ◽  
pp. 012010
Author(s):  
G. Dinardo ◽  
L. Fabbiano ◽  
R. Tamborrino ◽  
G. Vacca
Keyword(s):  
Defect Detection ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive
Sign in / Sign up

Export Citation Format

Share Document