Optimization of Nondestructive Evaluation Techniques for Bonded Components Through Model-Assisted POD Analysis

The reliability of nondestructive evaluation techniques is of utmost importance. The probability of detection (POD) is crucial for evaluating the performance of various NDT techniques, as it quantifies the ability to detect defects based on their type and size. In this study, the model-assisted probability of detection approach was used to evaluate the performance of various features for the determination of the defect sizes in adhesively bonded components. The analysis incorporated ultrasonic and radiographic inspection simulations, which were experimentally validated at our research facility. Unlike conventional approaches, which are limited to maximum amplitude-based evaluation, this approach incorporated various ultrasonic features, including peak-to-peak amplitude, absolute energy, mean value of amplitude in the frequency domain, and absolute time-of-flight. The Rose criterion was used for the estimation of the POD for the X-ray radiography. The key innovation of this study lies in the use of specific signal features—rather than the conventional maximum amplitude—to improve the reliability of defect sizing. To enable this, custom-developed ultrasonic and radiographic feature extraction modules were integrated into the CIVA simulation environment, thereby extending its standard capabilities beyond traditional amplitude-based POD analysis. The resulting a90|95 ​values, obtained from POD curves, demonstrate that the use of carefully selected signal features significantly enhances defect detection performance compared to conventional amplitude-based evaluation. The integration of custom feature extraction notably improves detection reliability, highlighting the advantage of feature-driven analysis in nondestructive testing.