Automation Defect Detection Using High Resolution Ultrasound in Honeycomb Sandwich Panels

Abstract This study presents an automated ultrasonic inspection method for detecting and quantifying interface defects in honeycomb composite laminate sandwich panels. Using immersion-based pulse-echo ultrasonic testing with full RF-waveform capture, ultrasonic data was acquired from Carbon Fiber Reinforced Polymer (CFRP) panels with a Nomex honeycomb core. Typical interface defects are created, such as missing adhesive, embedded Kapton and polytetrafluorethylene (PTFE) films, and missing core sections, with defects ranging in size from 4.7 mm to 40.8 mm. Two automation strategies to quantify the planar dimensions of the defect were developed: the first using the energy of the interface reflection and the second utilizing an effective facesheet thickness measurement based on the front and back wall reflections. Defect areas were extracted through image thresholding, and their shape was extracted and quantified via a custom algorithm implemented in MATLAB. Results show that the interface energy based detection method (EBD) achieved a consistently higher accuracy, with defect sizing errors typically below 1 mm with a maximum of 1.9 mm over all specimens investigated. In contrast, the thickness-based detection method (TBM) provided excellent accuracy for Kapton inserts (errors less than 0.65 mm) but exhibited larger deviations, up to 4.2 mm, for holes and PTFE defects due to a poor backwall signal. Results are presented that suggest that the backwall echo can be used for defect identification type. The automated techniques for dimensionalization demonstrated good performance across a wide defect size range, with a reliable detection and quantification of features as small as 4.7 mm and provide a reliable alternative to manual signal analysis.