A Model-Assisted Probability of Detection (MAPOD) Approach for Enhancing the Reliability of Ultrasonic Inspection in Adhesive Bonded Joints

Abstract

This study presents a model-assisted technique to investigate the probability of detection (POD) for ultrasonic inspection of adhesive joints with defects (brass inclusions and delamination). Numerical simulations using CIVA software were conducted to predict how ultrasonic waves respond to flaws of varying sizes from 0.01 mm to 5 mm. A sensitivity analysis was performed to simplify the physics-based model by identifying the most influential parameter using a meta-model. The model was further improved and calibrated to generate a POD curve, enabling accurate determination of the smallest detectable crack size. Moreover, this study focused on the implementation of a customdeveloped Python script to integrate ultrasonic features (absolute time-of-flight difference, maximum amplitude, and absolute energy) for evaluating the POD curve, while also addressing statistical uncertainties arising from measurement noise, material variability, and discrepancies in simulation models. Confidence intervals were added to reflect uncertainties in the simulation parameters and to predict potential differences between both simulation and real adhesive-bonded structural integrity. The results of this study revealed that the potential of model-assisted probability of detection (MAPOD) analysis enhanced the detection and measurement of defects in adhesivebonded joints. Overall, these results make a valuable contribution to improving quality control, effective structural health monitoring and advancement in reliable predictive methods for non-destructive testing (NDT).