Artificial intelligence based defect classification for weld joints
Artificial intelligence based defect classification for weld joints
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DOI：
10.1088/1757-899x/402/1/012159
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
S. E. Florence;V. Samsingh;Vimaleswar Babureddy
中科院分区：
文献类型：
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作者：
S. E. Florence;V. Samsingh;Vimaleswar Babureddy
本文主要论述了一个缺陷分类系统的开发，采用人工神经网络（ANN）分类的焊接缺陷的超声检测数据的基础上。该系统能够实时识别焊接缺陷，这在关键焊接应用的测试中得到应用，并且还减少了对熟练劳动力的依赖。研究主要包括三个部分-（i）焊接缺陷检测超声检测（UT）（ii）人工神经网络的实现（iii）缺陷分类。对焊接样品进行的超声波测试显示了有缺陷和无缺陷的焊缝以及缺陷之间的不同结果。将超声检测数据输入神经网络算法进行训练，以识别各种焊接缺陷。人工神经网络（ANN）是一种信息处理范式，它使用大量高度互连的处理元件（称为神经元），协调工作以解决特定问题。有两种类型的神经网络架构用于分类-反向传播网络（BPN）和概率神经网络（PNN）。反向传播网络已被用于本研究的目的。为了检验BP神经网络的性能，考虑了四类缺陷，即气孔、侧壁未熔合、未焊透和夹渣。
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.