The aim of this work is to achieve reliable nondestructive evaluation (NDE) of adhesively bonded aerospace components by developing novel multidimensional data fusion techniques, which would combine the information obtained by ultrasonic and X-ray NDE methods. Separately, both NDE techniques have their advantages and limitations. The integration of data obtained from pulse echo immersion ultrasound testing and radiography holds immense potential to help improve the reliability of non-destructive evaluation. In this study, distinctive features obtained from single techniques, traditional ultrasonic pulse echo testing, and radiography, as well as fused images, were investigated and the suitability of these distinctive features and fusion techniques for improving the probability of defect detection was evaluated. For this purpose, aluminum single lap joints with brass inclusions were analyzed using ultrasound pulse echo and radiography techniques. The distinctive features were extracted from the data obtained, and images of features obtained by both techniques were fused together. Different combinations of features and fusion algorithms were investigated, considering the desire to automate data evaluation in the future.

The current study focuses on the examination of adhesive-bonded materials, comprising different type of flaws like brass inclusions and delamination, through the application of ultrasound and X-ray non-destructive testing (NDT) techniques. The findings from both ultrasound and X-ray inspection were used to extract unique features, contributing to a more comprehensive understanding of the distinct characteristics demonstrated by each method. Several distinct features like absolute time of flight difference, peak-to-peak amplitude, variation coefficient in time and frequency domain, mean value of amplitude in frequency domain, and absolute energy were extracted from ultrasound testing results. Similarly, features like maximum amplitude, features from accelerated segment test, dilation, watershed segmentation, wiener deconvolution, and morphological gradient extracted from X-ray data underwent fusion. Different fusion techniques were applied to combine these features into a unified data set. A quantitative evaluation was performed for the individual features and their corresponding fused features from the ultrasound and X-ray results. A systematic analysis was conducted to quantify the improvement in defect sizing within the individual features and fused features from both the X-ray and ultrasonic investigations. The minimum absolute error of 0.02 mm was achieved with average fusion of absolute energy at 2nd interface and X-ray dilate features. This research not only delves into the diverse capabilities of ultrasonic and X-ray NDT methods in identifying flaws but also emphasizes the synergistic advantages arising from the integration of their distinct features. The qualitative study of defect estimation using the proposed fusion methods demonstrate that the distinctive fusion approaches significantly highlight the complimentary benefits of ultrasound and X-ray non-destructive testing methods, resulting in a quantifiable improvement in probability of defect detection.

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).