A machine learning surrogate model for time of flight diffraction measurements of rough defects

Understanding the uncertainty in nondestructive evaluation measurements of rough defects requires a stochastic analysis due to the random variation between the morphology of different defects. In previous studies, large numbers of finite element models of randomly generated rough defects have been run in order to gain an insight into the statistics of the uncertain results. This approach is limited due to the time taken to run each individual model (typically of the order of minutes per model) and so the total number of models run is limited. In this paper, a surrogate model approach is proposed which produces close approximations to an original finite element model of a time-of-flight diffraction measurement, but in a small fraction of the time (<1 ms). The surrogate model is trained on pairs of input (rough defect) and output (A-scan) data from the finite element model; the surrogate then learns the relationship between inputs and outputs and is then able to generate new results. The architecture employed is a machine learning sequence-to-sequence approach similar to those used in natural language algorithms, based on the idea of 'translating' between defect to A-scan. In this study, a surrogate model was trained on 2160 finite element results which spanned across a range of ultrasonic incidence angles, rough defect correlation length and root-mean-square roughness. The surrogate model is shown to produce A-scans with close agreement to those produced by the original finite element model; taking approximately 0.425 ms instead of approximately 8 minutes. Millions of results are then possible in seconds, rather than the decades that would be required for finite element studies; more complete stochastic analysis are therefore now viable.