Automated Data Analysis Algorithms for Ultrasonic Nondestructive Evaluation of Complex Composite Panels

Abstract

To address the data review burden and improve the reliability of the ultrasonic (UT) inspection of large composite structures, automated data analysis (ADA) algorithms have been developed to make calls on indications that satisfy the call criteria. Certain complex composite structures with varying shape, thickness transitions, front-wall and back-wall curvature, and the presence of bonds can greatly complicate this interpretation process and produce false calls. In this effort, enhancements to the automated data analysis algorithms are introduced to address these challenges for complex composite panels. First, the thickness of the part and presence of bonds is estimated by tracking potential backwall signals, detecting the presence of multiple signals and step changes which are indicators of bonded sections, and through the application of smart spatial filters for estimating the panel thickness and additional bonded sections with varying signal levels. Once part boundaries, thickness transitions and bonded regions are identified, feature extraction algorithms are applied to multiple sets of through-thickness and backwall C-scan images, for evaluation of both first layer through thickness and layers under bonds. To verify the algorithm performance, a set of test data was selected to challenge the ADA algorithms that includes a wide range of complex parts and artificial defects located both above and below bond lines. Results are presented for a variety of test specimens that include inserted materials and discontinuities produced under poor manufacturing conditions, demonstrating the desired detection capability while minimizing false indication calls to a manageable level.