The use of Carbon Fibre Reinforced Plastic (CFRP) composite materials for critical components has significantly surged within the energy and aerospace industry. With this rapid increase in deployment, reliable post-manufacturing Non-Destructive Evaluation (NDE) is critical for verifying the mechanical integrity of manufactured components. To this end, an automated Ultrasonic Testing (UT) NDE process delivered by an industrial manipulator was developed, greatly increasing the measurement speed, repeatability, and locational precision, while increasing the throughput of data generated by the selected NDE modality. Data interpretation of UT signals presents a current bottleneck, as it is still predominantly performed manually in industrial settings. To reduce the interpretation time and minimise human error, this paper presents a two-stage automated NDE evaluation pipeline consisting of a) an intelligent gating process and b) an autoencoder (AE) defect detector. Both stages are based on an unsupervised method, leveraging density-based spatial clustering of applications with noise clustering method for robust automated gating and undefective UT data for the training of the AE architecture. The AE network trained on ultrasonic B-scan data was tested for performance on a set of reference CFRP samples with embedded and manufactured defects. The developed model is rapid during inference, processing over 2000 ultrasonic B-scans in 1.26 s with the area under the receiver operating characteristic curve of 0.922 in simple and 0.879 in complex geometry samples. The benefits and shortcomings of the presented methods are discussed, and uncertainties associated with the reported results are evaluated.

中文翻译：

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用于碳纤维增强塑料复合材料自动超声波检测中缺陷检测的无监督机器学习

在能源和航空航天工业中，碳纤维增强塑料 (CFRP) 复合材料在关键部件中的使用大幅增加。随着部署的迅速增加，可靠的制造后无损评估 (NDE) 对于验证制造组件的机械完整性至关重要。为此，开发了由工业机械手提供的自动化超声波测试 (UT) NDE 流程，大大提高了测量速度、重复性和定位精度，同时提高了所选 NDE 模式生成的数据吞吐量。 UT 信号的数据解释是当前的瓶颈，因为它仍然主要在工业环境中手动执行。为了减少解释时间并最大限度地减少人为错误，本文提出了一种两阶段自动化 NDE 评估流程，其中包括 a) 智能门控过程和 b) 自动编码器 (AE) 缺陷检测器。这两个阶段都基于无监督方法，利用基于密度的应用程序空间聚类和噪声聚类方法来实现稳健的自动门控和用于 AE 架构训练的无缺陷 UT 数据。使用超声波 B 扫描数据训练的 AE 网络在一组带有嵌入和制造缺陷的参考 CFRP 样品上进行了性能测试。开发的模型在推理过程中速度很快，在 1.26 秒内处理超过 2000 个超声波 B 扫描，简单几何样本中接收器工作特性曲线下面积为 0.922，复杂几何样本中为 0.879。讨论了所提出方法的优点和缺点，并评估了与报告结果相关的不确定性。