Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data.

In nondestructive evaluation (NDE), accurately characterizing defects within components relies on accurate sizing and localization to evaluate the severity or criticality of defects. This study presents for the first time a deep learning (DL) methodology using 3-D U-Net to localize and size defects in carbon fiber reinforced polymer (CFRP) composites through volumetric segmentation of ultrasonic testing (UT) data. Using a previously developed approach, synthetic training data, closely representative of experimental data, was used for the automatic generation of ground truth segmentation masks. The model's performance was compared to the conventional amplitude 6 dB drop analysis method used in the industry against ultrasonic defect responses from 40 defects fabricated in CFRP components. The results showed good agreement with the 6 dB drop method for in-plane localization and excellent through-thickness localization, with mean absolute errors (MAEs) of 0.57 and 0.08 mm, respectively. Initial sizing results consistently oversized defects with a 55% higher mean average error than the 6 dB drop method. However, when a correction factor was applied to account for variation between the experimental and synthetic domains, the final sizing accuracy resulted in a 35% reduction in MAE compared to the 6 dB drop technique. By working with volumetric ultrasonic data (as opposed to 2-D images), this approach reduces preprocessing (such as signal gating) and allows for the generation of 3-D defect masks which can be used for the generation of computer-aided design files; greatly reducing the qualification reporting burden of NDE operators.

Unsupervised machine learning for flaw detection in automated ultrasonic testing of carbon fibre reinforced plastic composites.

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.