Non-destructive evaluation of induced defects in aerospace-grade CFRP composites using immersion ultrasonic C-Scan

• Artificial defects (inclusion, delamination, porosity) were embedded in CFRP laminates and inspected using immersion ultrasonic C-Scan. • Scan signals were analysed to distinguish defect types by amplitude and position, including those in the dead zone. • Higher probe frequencies (up to 20 MHz) improved resolution without losing defect detection in 3–6 mm laminates. • Custom calibration blocks enhanced inspection accuracy and supported reliable signal interpretation for composite materials. Non-destructive inspection methods are employed to assess the quality of both metallic and non-metallic materials without causing deformation. These techniques are widely applied across various industries. Ultrasonic testing represents one of the most prevalent non-destructive inspection techniques. The calibration of fibre-reinforced polymer composites using artificial defects presents significant challenges due to the unique internal properties of each structure, such as fibre type, orientation, layup, and fibre-to-resin ratio. This study characterises immersion ultrasonic C-Scan performance using artificially induced defects in aerospace-grade carbon fibre-reinforced polymer (CFRP) composites with varying thicknesses approximately 3 and 6mm and layup configurations. CFRP composites with [0] 48 and [-45/30/45/60] 3s layups were fabricated using an autoclave process. During preform preparation, six defect types were introduced by inserting materials including aluminium, release film (fluorinated ethylene propylene), Teflon (two and four layers), and Guyson Honite (one and four layers). The samples were inspected using immersion ultrasonic probes at frequencies of 5 MHz, 10 MHz, and 20 MHz. Analysis of the received signals enabled classification of defects as delamination, inclusion, layer porosity, or volume porosity. The dead zone, a primary limitation in ultrasonic testing, was also investigated. Defects were positioned near the top surface within the dead zone to evaluate detection capability. All defects located in the dead zone were successfully identified, although some challenges were encountered during the process. The received signals were subsequently classified according to the type of defect.