  Ultrasonic guided wave technology has played a significant role in the field
of non-destructive testing as it employs acoustic waves that have advantages of
high propagation efficiency and low energy consumption during the inspect
process. However, theoretical solutions to guided wave scattering problems
using assumptions such as Born approximation, have led to the poor quality of
the reconstructed results. To address this issue, a novel approach to
quantitative reconstruction of defects using the integration of data-driven
method with the guided wave scattering analysis has been proposed in this
paper. Based on the geometrical information of defects and initial results by
the theoretical analysis of defect reconstructions, a deep learning neural
network model is built to reveal the physical relationship between defects and
the received signals. This data-driven model is then applied to quantitatively
assess and characterize defect profiles in structures, reduce the inaccuracy of
the theoretical modelling and eliminate the impact of noise pollution in the
process of inspection. To demonstrate advantages of the developed approach to
reconstructions of defects with complex profiles, numerical examples including
basic defect profiles and a defect with the noisy fringe have been examined.
Results show that this approach has greater accuracy for reconstruction of
defects in structures as compared with the analytical method and provides a
valuable insight into the development of artificial intelligence-assisted
inspection systems with high accuracy and efficiency in the field of
non-destructive testing.
