Artificial intelligence based defect classification for weld joints

This paper mainly deals with the development of a defect classification system that uses Artificial Neural Network (ANN) to classify weld defects based on ultrasonic test data. The system enables real-time identification of weld defects which finds application in testing of critical welding applicat...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2018-08, Vol.402 (1), p.12159
Main Authors: Esther Florence, S, Vimal Samsingh, R, Babureddy, Vimaleswar
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Back propagation
Back propagation networks
Classification
Computer architecture
Data processing
Defects
Neural networks
Propagation
Ultrasonic testing
Ultrasonic tests
Weld defects
Welded joints
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Summary:This paper mainly deals with the development of a defect classification system that uses Artificial Neural Network (ANN) to classify weld defects based on ultrasonic test data. The system enables real-time identification of weld defects which finds application in testing of critical welding applications and also reduces dependency on skilled workforce for the function. The study mainly consists of three parts- (i) Weld defect detection using Ultrasonic Testing (UT) (ii) Implementation of ANN (iii) Defect classification. An ultrasonic test performed on welded samples shows different results for welds with and without defects and further between defects as well. The ultrasonic test data is fed into the ANN algorithm to train it to identify the various weld defects. An Artificial Neural Network (ANN) is an information processing paradigm that uses a large number of highly interconnected processing elements called neurons, working in unison to solve the specific problems. There are two types of neural network architectures that are used for classification - a back propagation network (BPN) and a probabilistic neural network (PNN). Back propagation network has been used for the purpose of this study. In order to test the performance of the back propagation neural network, four classes of defect namely porosity, lack of side wall fusion, lack of penetration and slag inclusion are considered.
ISSN:1757-8981
1757-899X
1757-899X
DOI:10.1088/1757-899X/402/1/012159