Machine learning for Non-Destructive Evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This paper demonstrates how an efficient, hybrid finite element and ray-based simulation can be used to train a Convolutional Neural Network (CNN) to characterize real defects. To demonstrate this methodology, an inline-pipe inspection application is considered. This uses four plane wave images from two arrays, and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6 dB drop method, is used as a comparison point. For the 6 dB drop method the average absolute error in length and angle prediction is ±1.1 mm, ±8.6° while the CNN is almost four times more accurate at ±0.29 mm, ±2.9°. To demonstrate the adaptability of the deep-learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed the 6 dB drop method has an average error of ±1.5 mm, ±12° while the CNN has ±0.45 mm, ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing.
Plane Wave Imaging (PWI) is an ultrasonic array imaging technique used in non-destructive testing, that has been shown to yield high resolution with few transmissions. Only a few published examples are available of PWI of components with nonplanar surfaces in immersion. In these cases, inspections were performed by adapting the transmission delays in order to produce a plane wave inside the component. This adaptation requires prior knowledge of the component geometry and position relative to the array. The current paper proposes a new implementation, termed PWI Adapted in Post-Processing (PWAPP), which has no such requirement. In PWAPP the array emits a plane wave as in conventional PWI. The captured data is input into two post-processing stages. The first reconstructs the surface of the component, the latter images inside of it by adapting the delays to the distortion of the plane waves upon refraction at the reconstructed surface. Simulation and experimental data are produced from an immersed sample with a concave front surface and artificial defects. These are processed with conventional and surface corrected PWI. Both algorithms involving surface adaptation produced nearly equivalent results from the simulated data, and both outperform the non-adapted one. Experimentally, all defects are imaged with Signal-to-Noise Ratio (SNR) of at least 31.8 and 33.5 dB for respectively PWAPP and PWI adapted in transmission, but only 20.5 dB for conventional PWI. In the cases considered, reducing the number of transmissions below the number of array elements shows PWAPP maintains its high SNR performance down to number of firings equivalent to a quarter of the array elements. Finally, experimental data from a more complex surface specimen is processed with PWAPP resulting in detection of all scatterers and producing SNR comparable to that of the Total Focusing Method. Index Terms-ultrasound, non-destructive evaluation, plane wave imaging, immersion testing, signal processing, complex geometry.Rosen K. Rachev was born in Burgas, Bulgaria, in 1993. He received the M.Eng. degree in Mechanical Engineering from the University of Bristol, Bristol, U.K., in 2016. He is currently pursuing the D.Eng. degree in Nondestructive Evaluation in the Ultrasonics and Nondestructive Testing Research Group, University of Bristol. He is working on advanced ultrasonic data processing algorithms for pipeline in-line inspections, and his project is sponsored by Baker Hughes. His research interests include nondestructive evaluation for the oil and gas industry, ultrasonic phased array surface reconstruction, adaptive imaging, and defect characterisation.
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