Use of deep learning and data augmentation by physics-based modelling for crack characterisation from multimodal ultrasonic TFM images

In this article, we study a machine learning-based approach for characterising crack-like defects in specimens with complex geometries using the multi-modal total focusing method (M-TFM) applied to ultrasonic full matrix capture (FMC) data. In particular, this work frames into a scientific machine learning perspective leveraging the use of physics-based numerical solvers as an efficient yet accurate data augmentation strategy. Toward this end, the numerical simulations have been tailored to built a suitable data set aiming at gathering the uncertainties in M-TFM images encountered when the reconstruction parameters are barely known. That is, the use of such a physical knowledge has been exploited to train a deep convolutional neural network designed for retrieving crack size and position based on multi-modal TFM images. Furthermore, for validating the learning proposed scheme, an experimental campaign has been also carried out accordingly to the most relevant factors that typically impact the quality of reconstructed M-TFM images. The developed approach has been tested on both synthetic and experimental data sets never shared in the training phase.