Paper Title:
A Neural Network Approach for Locating Multiple Defects
  Abstract

A method is presented to demonstrate the use of artificial neural networks (ANNs) in providing additional information regarding defects or flaws when used in conjunction with the ultrasonic A-scan method. ANNs were employed both as pattern classifiers and as function approximators to maximise the amount of data available from the temporal A-scan signal. A steel bar was modelled in 2D using ABAQUS finite element analysis (FEA) software. A single defect was introduced to the bar, modelled as a void, and parametric studies conducted to record data with the defect at various locations. An ultrasonic Lamb wave was introduced at the top of the bar. The longitudinal wave propagated along the length of the bar and was partially reflected by the defect. Multiple cases were simulated, modelling voids between 1mm and 6mm in width in various locations. Mean displacement of all the nodes at the top of the bar was recorded throughout the simulation, and features extracted from this waveform to create the data set for the ANNs. The ANNs were trained with a percentage of the data collected, selected at random, and assessed with the remaining data. The target data for the ANNs were the depth and size of the defect. The case of two separate defects was also investigated. The procedure was carried out in the same manner as for one defect, but in this case the target data for the ANNs were the depth of the first defect and the distance between the defects. A separate ANN was employed as a pattern classifier, to determine if the reflected A-scan signal represented one or two defects. The final system was tested using previously unseen data, and provided very good results both in determining the number of defects and the size and location of the defects, even with data to which noise had been added.

  Info
Periodical
Edited by
J.M. Dulieu-Barton, J.D. Lord and R.J. Greene
Pages
125-131
DOI
10.4028/www.scientific.net/AMM.13-14.125
Citation
S.J. Farley, J.F. Durodola, N.A. Fellows, L. H. Hernández-Gómez, "A Neural Network Approach for Locating Multiple Defects", Applied Mechanics and Materials, Vols. 13-14, pp. 125-131, 2008
Online since
July 2008
Export
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Jin Jun Rao, Tong Yue Gao, Zhen Jiang, Zhen Bang Gong
Abstract:In order to provide a general purpose method to search optimum solution for complex constrained engineering problems without explicit system...
1522
Authors: Xing Wei, Jun Li
Abstract:Artificial neural networks (ANNs) have been widely applied to many bridge engineering problems and have demonstrated some degree of success....
1984
Authors: Iman Kameli, Mahmoud Miri, Ali Raji
Abstract:In this paper, the application of artificial neural network (ANN) in predicting seismic response of reinforced concrete (RC) frames with...
2345