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

"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."

本数据集支撑一项实验研究，该研究旨在评估定向涡流检测（Directional Eddy Current Testing，DECT）对采用自动纤维铺放（automated fibre placement，AFP）工艺制备的碳纤维增强聚合物（carbon-fibre-reinforced polymer，CFRP）层合板亚表面缺陷的识别效果。
研究制备了两块代表性层合板试样：首先以原始无缺陷材料为基底，引入可控的埋藏间隙与搭接缺陷，随后对缺陷区域开展人工返修，以复现真实的制造场景。
采用DECT扫描系统沿检测方向评估这些内部特征的可检出性。
为获取缺陷几何形状的参考数据，每一块经扫描的试样在完成无损检测（non-destructive evaluation）后均进行了破坏性剖切。
检测能力通过涡流响应得到的信噪比（signal-to-noise ratio，SNR）进行量化表征。
结果显示，DECT能够可靠识别深度介于0.21 mm至0.73 mm之间的AFP相关埋藏缺陷，测得的SNR值为5.78~9.28，远高于业界普遍认可的SNR＞3的检测阈值。