Sim-to-Real: Employing ultrasonic guided wave digital surrogates and transfer learning for damage visualization

Wavefield imaging is a powerful visualization tool in nondestructive evaluation for studying ultrasonic wave propagation and its interactions with damage. To isolate and study damage scattering, damage-free baseline data is often subtracted from a wavefield. This is often necessary because the damage wavefield can be orders of magnitude weaker than the incident waves. Yet, baselines are not always accessible. When the baselines are accessible, the experimental conditions for the baseline and test data must be extremely similar. Researchers have created several baseline-free approaches for isolating damage wavefields, but these often rely on specific experimental setups. In this paper, we discuss a flexible approach based on ultrasonic guided wave digital surrogates (i.e., numerical simulations of incident waves) and transfer learning. We demonstrate this approach with two setups. We first isolate reflections from a circular, 2 mm diameter half-thickness hole on a 10 × 10 cm steel plate. We then isolate 8 circular, half-thickness holes of various diameters from 1 mm to 40 mm on a 60 × 60 cm steel plate. The second plate has a non-square geometry and the data has multi-path reflections. With both data sets, we isolate damage reflections without explicit experimental baselines. We also briefly illustrate the comparison of our dictionary learning method with wavenumber filtering technique which is often used to enhance the defect wavefields.