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.