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.

Baseline-free guided wave damage detection with surrogate data and dictionary learning.

In guided wave structural health monitoring, damage detection is often accomplished by comparing measurements before damage (i.e., baseline data) and after damage (i.e., test data). Yet, in practical scenarios, baseline data is often unavailable. Data from surrogate structures (structures similar to the test structure) could replace baseline data, but due to small differences in material properties, such as thickness, temperature, and other effects, this data is often unreliable. In this paper, a dictionary learning framework overcomes this challenge and detects damage with surrogate information. The framework combines wave propagation characteristics of a test structure with geometric information from surrogate structures to create a synthetic damage-free baseline. The test data is compared with the synthetic baseline to detect damage. The framework is evaluated with four 108 mm ×108 mm plates: two 1.6-mm thick aluminum plates, one 1.6-mm thick steel plate, and one 6.25 mm thick aluminum plate. The framework is applied to each test structure after learning from plates with different material properties and thicknesses. With full wavefield data, this paper successfully isolates reflections from a mass without using explicit baseline data. Furthermore, with sparse guided wave data, this paper shows that a drop in a correlation coefficient can detect a mass without using explicit baseline data.