Automated bounding box annotation for NDT ultrasound defect detection

The growing interest in applying Machine Learning (ML) techniques in Non-Destructive Testing (NDT) to assist expert detection and analysis is facing many unique challenges. This research seeks to create an object detection network that would automatically generate bounding boxes around various defects found in Carbon Fibre Reinforced Polymers (CFRPs) through which the quantitative defect size information can be inferred. CFRPs are structurally anisotropic resulting in complex physical interactions between the emitted acoustic waves and the material structure when Ultrasonic Testing (UT) is deployed. Therefore, the structural noise makes the detection of various types of defects, such as porosities, delaminations and inclusions, that are frequently observed in CFRPs [1] even a more challenging task. In order to take a supervised learning approach in the detection of defects, a training dataset must be produced and labelled. Extensive automatic methods for data collection exist, however, in many cases labelling is done manually, which requires extensive use of expert time. Therefore, a method for automatically labelling simple defects could potentially be useful for accelerating the ground truth creation and allowing experts to focus on the detection of more complex defects

Application of eddy currents for inspection of carbon fibre composites

Carbon Fibre Reinforced Plastics (CFRP) have diverse industrial applications due to their unique mechanical and structural properties. The manufacturing cycle of CFRP can be summarised into three stages: Preforming, moulding and post cure. During the preforming stage of the composites where there is cutting, handling and layup of carbon fibre fabrics, defects such as fibre waviness, missing bundles and in-plane waviness can occur. These defects are usually detected when the component is inspected after the post cure stage. Hence there is a need to inspect these components before the resin is infused into the dry layup. Currently there is no standardised NDE protocols for the inspection of these dry fabrics and preforms in the aerospace manufacturing industry. This study investigates the inspection of Dry Carbon Fabrics (DCF) for fibre orientation, density, and defects such as missing fibre bundles, in and out of plane fibre waviness, before the resin infusion manufacturing stage, using Eddy Current Testing (ECT). Initial experiments were conducted to test the penetration depth of eddy currents in DCF. A sample was built using biaxial fibre cloth with fibre orientation at 0° and 90°. Six layers were used where layers 2,3,4 and 5 had a strip of aluminium foil to detect the penetration depth of eddy currents through the sample. A total of four stripes were used within the sample. The inspection was carried out at frequencies of 500 and 800 kHz using an eddy current array probe attached to a KUKA robotic arm. Data was gathered in absolute mode for pairs of transmit-receive coils in two transversal and axial topologies. The scans displayed all four stripes, indicating that the eddy current had penetrated through all six layers at both test frequencies. To identify the sensitivity to internal defects, a second experiment was conducted. The inspection sample was made by stacking 10 sheets of DCF with a piece of preformed carbon fibre to induce fibre waviness. Initial results show that the waviness can be detected at 500 kHz with a strong accuracy in every repetition of the scans. Orientation of the fibres could not be detected at this frequency. To conclude, initial experiments were conducted on dry carbon fibre fabrics using eddy current testing to detect fibre waviness and penetration depth of eddy currents. The results show an indication of fibre waviness in a 10-layer sample at 500 KHz in every repetition of the scans. Although the orientation of the fibres could not be detected at this frequency

3-Dimensional residual neural architecture search for ultrasonic defect detection

This study presents a deep learning methodology using 3-dimensional (3D) convolutional neural networks to detect defects in carbon fiber reinforced polymer composites through volumetric ultrasonic testing data. Acquiring large amounts of ultrasonic training data experimentally is expensive and time-consuming. To address this issue, a synthetic data generation method was extended to incorporate volumetric data. By preserving the complete volumetric data, complex preprocessing is reduced, and the model can utilize spatial and temporal information that is lost during imaging. This enables the model to utilize important features that might be overlooked otherwise. The performance of three architectures were compared. The first architecture is prevalent in the literature for the classification of volumetric datasets. The second demonstrated a hand-designed approach to architecture design, with modifications to the first architecture to address the challenges of this specific task. A key modification was the use of cuboidal kernels to account for the large aspect ratios seen in ultrasonic data. The third architecture was discovered through neural architecture search from a modified 3D Residual Neural Network (ResNet) search space. Additionally, domain-specific augmentation methods were incorporated during training, resulting in significant improvements in model performance, with a mean accuracy improvement of 22.4% on the discovered architecture. The discovered architecture demonstrated the best performance with a mean accuracy increase of 7.9% over the second best model. It was able to consistently detect all defects whilst maintaining a model size smaller than most 2-dimensional (2D) ResNets. Each model had an inference time of less than 0.5 seconds, making them efficient for the interpretation of large amounts of data

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

Automated deep learning for defect detection in carbon fibre reinforced plastic composites

Carbon Fibre Reinforced Polymers (CFRPs) are used extensively in the aerospace industry because of their unique physical properties and reduced weight that enables lower fuel consumption. This increase was especially rapid in the past decade, with CFRPs accounting for around 50% of the total material weight used in flagship models by Airbus and Boeing [1,2]. Before shipping, Non-Destructive Testing (NDT) methods are used to validate and control the quality of manufactured parts. Commonly used NDT technologies are radiographic testing, eddy current testing, and Ultrasonic Testing (UT). In the aerospace industry, UT is most prominent due to its flexibility and safety. However, when UT is done manually, reliability issues are often observed due to human inspector errors [3]. In addition to this, manufactured parts that need to be inspected are quite large (e.g., wing covers), resulting in slow inspection times. On the other hand, when NDT robotic inspection is deployed, large amounts of data can be captured in a short period of time. While this accelerates the acquisition of information, data interpretation is still done manually thus creating a bottleneck. Therefore, an automated data interpretation system would greatly improve the NDT process. To overcome these challenges, this project proposes a fully automated Deep Learning (DL) approach that leverages current technological advances in Machine Learning (ML) field for defect localization, sizing, and automatic report generation based on ultrasonic amplitude C-scans. Such an approach could decrease the processing time from approximately 6 hours for a 15-meter wing cover to just minutes, significantly benefiting the process throughput. In this research, a manually annotated semi-analytical simulated dataset in form of C-scans was used for training of "You Only Look Once" family of models for the detection and sizing of back-drilled holes and delamination defects in CFRPs. The purpose of using model-based simulations for training was the scarcity of real-world data, and a novel approach of image augmentation was introduced to ensure that the simulated scans closely mimic the experimental data. For NDT inspection, a force-torque-controlled 6-axis industrial robotic arm was used to deliver a phased array ultrasound roller probe to both defect-free and defective CFRP samples of varying thicknesses. The roller-probe array was connected to an array controller and water-coupled to the surface of the CFRPs. Raster scans were performed while the array was excited in linear-scan mode with a sub-aperture of 4 elements and an operating frequency of 5 MHz. Lastly, amplitude C-scan images of 64 x 64 resolution were extracted and used as an object detection validation dataset. These combined methods result in an accurate and precise deep learning network that enables rapid analysis of image data (with the possibility of real-time analysis)

A study of machine learning object detection performance for phased array ultrasonic testing of carbon fibre reinforced plastics

The growing adoption of Carbon Fibre Reinforced Plastics (CFRPs) in the aerospace industry has resulted in a significant reliance on Non-Destructive Evaluation (NDE) to ensure the quality and integrity of these materials. The interpretation of large amounts of data acquired from automated robotic ultrasonic scanning by expert operators is often time consuming, tedious, and prone to human error creating a bottleneck in the manufacturing process. However, with ever growing trend of computing power and digitally stored NDE data, intelligent Machine Learning (ML) algorithms have been gaining more traction than before for NDE data analysis. In this study, the performance of ML object detection models, statistical methods for defect detection, and traditional amplitude thresholding approaches for defect detection in CFRPs were compared. A novel augmentation technique was used to enhance synthetically generated datasets used for ML model training. All approaches were tested on real data obtained from an experimental setup mimicking industrial conditions, with ML models showing improvement over amplitude thresholding and statistical thresholding techniques. The advantages and limitations of all methods are reported and discussed

Advancing carbon fiber composite inspection : deep learning-enabled defect localization and sizing via 3-Dimensional U-Net segmentation of ultrasonic data

In Non-Destructive 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 methodology using 3-Dimensional (3D) U-Net to localize and size defects in Carbon Fibre Reinforced Polymer (CFRP) composites through volumetric segmentation of ultrasonic testing 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 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 (MAE) of 0.57 mm 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 2D images) this approach reduces pre-processing (such as signal gating) and allows for the generation of 3D defect masks which can be used for the generation of computer aided design files; greatly reducing the qualification reporting burden of NDE operators

Application of machine learning techniques for defect detection, localisation, and sizing in ultrasonic testing of carbon fibre reinforced polymers

The growing interest in applying Machine Learning (ML) techniques in Nondestructive Testing (NDT) to assist expert detection and analysis is facing many unique challenges, one of the most significant being a lack of experimental training data. This research aims to develop an object detection network that can automatically generate bounding boxes around various defects in Carbon Fibre Reinforced Polymers (CFRPs), allowing for the inference of quantitative defect size and other relevant information. The anisotropic nature of CFRPs results in complex interactions between emitted acoustic waves and the material structure during Ultrasonic Testing (UT), making the detection of defects such as porosities, delamination and inclusions particularly challenging. To address these challenges, a combination of advanced ML methods including object detection (You Only Look Once algorithms), synthetically generated datasets, Generative Adversarial Networks (GANs) and advanced statistical methods for data augmentation, and UNet segmentation networks were used. The combined outputs of these methods were evaluated on representative CFRP experimental data collected in-house using Phased Array Ultrasonic Transducer (PAUT) and a KUKA KR90 robotic arm. This presentation will provide insight into the state-of-the-art techniques and methods used in the field of NDT for CFRPs and their potential applications in the industry