Dataset · IEEE DataPort · March 14, 2026

"Real-time in-situ detection of buried defects for intelligent automated composite manufacturing using directional eddy current s"

Abstract

"a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; }This dataset supports an experimental study evaluating the effectiveness of Directional Eddy Current Testing (DECT) for identifying subsurface defects in carbon\u2011fibre\u2011reinforced polymer (CFRP) laminates manufactured using automated fibre placement (AFP). Two representative laminate specimens were produced: pristine material was first used to introduce controlled buried gaps and overlaps, after which the defective regions were manually reworked to replicate realistic manufacturing scenarios. A DECT scanning system was employed to assess the detectability of these internal features along the inspection direction. To obtain reference defect geometries, each scanned specimen was subsequently destructively sectioned following non\u2011destructive evaluation. Detection capability was quantified through the signal\u2011to\u2011noise ratio (SNR) derived from the eddy\u2011current response. The results show that DECT reliably identifies buried AFP\u2011related defects located at depths between 0.21 mm and 0.73 mm, with measured SNR values of 5.78\u20139.28, well above the commonly accepted detection threshold of SNR > 3."

Non-Destructive Testing TechniquesIndustrial Vision Systems and Defect DetectionUltrasonics and Acoustic Wave PropagationCFRP laminatesautomated fiber placementcomposite manufacturing defects

"Real-time in-situ detection of buried defects for intelligent automated composite manufacturing using directional eddy current s"

Abstract

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