Self-supervised learning-aided ultrasonic testing for overcoming long-tail problems in stress-strain curve prediction

Addressing the long-tail problem (LTP) is critical when applying deep learning (DL) to ultrasonic testing, as defective samples often lead to poor testing performance. This study addresses the LTP in stress-strain curve prediction using ultrasound by applying a Value Imputation and Mask Estimation (VIME)-based self-supervised learning (SSL) framework. Using 816 aluminum alloy samples, including low yield strength (YS) cases (100-200 MPa) that trigger LTP, the baseline model performed well overall but degraded sharply on LTP data (mean absolute percentage error (MAPE): 10% for non-LTP vs. 26% for LTP). VIME-SSL reduced the MAPE to 9.4% and 21%, respectively, with greater relative improvement for LTP cases. Notably, frequency-domain signals containing fundamental and second harmonic components were found to be especially effective for VIME-SSL in addressing the LTP. This finding was substantiated by separate ultrasonic measurements of attenuation and nonlinearity. Overall, this study demonstrates VIME-SSL as a promising approach for improving DL-based ultrasonic testing on rare or anomalous samples.