Deconvolution of Ultrasonic Signals Using a Convolutional Neural Network

Deconvolution of Ultrasonic Signals Using a Convolutional Neural Network

Chapon, Arthur and Pereira, Daniel and Toews, Matthew and Bélanger, Pierre

Ultrasonics 2020

Abstract : \textlessp\textgreaterSuccessfully employing ultrasonic testing to distinguish a flaw in close proximity to another flaw or geometrical feature depends on the wavelength and the bandwidth of the ultrasonic transducer. This explains why the frequency is commonly increased in ultrasonic testing in order to improve the axial resolution. However, as the frequency increases, the penetration depth of the propagating ultrasonic waves are reduced due to an attendant increase in attenuation. The nondestructive testing research community is consequently very interested in finding methods that combine high penetration depth with high axial resolution. This work aims to improve the compromise between the penetration depth and the axial resolution by using a convolutional neural network to separate overlapping echoes in time traces in order to estimate thei time-of-flight and amplitude. The originality of the proposed framework consists in its training the neural network using data generated in simulations. The framework was validated experimentally to detect flat bottom holes in an aluminum block with a minimum depth corresponding to $łambda$/4.\textless/p\textgreater