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.

Single-Bit Reception with Coded Excitation for Lightweight Advanced Ultrasonic Imaging Systems.

The demand for an efficient and reliable ultrasonic phased array imaging system is not unique to a single industry. Today's imaging systems can be enhanced in a number of areas including; improving scanning and processing times, reducing data storage requirements, simplifying hardware and prolonging probe lifespan. In this work, it is shown that by combining the use of Coded Excitation with single-bit data capture, a number of these areas can be improved. Despite using single-bit receive data, resolution can be recovered through the coded excitation pulse compression process, and shown to produce high Signal-to-Noise Ratio (SNR) images of Phase Coherence Imaging (PCI) and Total Focusing Method (TFM) of tip diffraction in a carbon steel sample. Comparison with conventional single-cycle transmission pulses has shown that little imaging performance degradation is seen despite a significant reduction in data resolution and size. This has also been shown to be effective at low excitation voltages with gain compensation due to the obsolescence of signal saturation concerns when considering single-bit receive data. The ability to compute high-resolution ultrasonic images from low-resolution input data at low transmission voltages has important implications for data compression, acquisition & imaging performance, operator safety and hardware simplification for ultrasonic imaging systems across industrial and medical fields.

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.

Three-Dimensional Residual Neural Architecture Search for Ultrasonic Defect Detection.

This study presents a deep-learning (DL) methodology using 3-D convolutional neural networks (CNNs) to detect defects in carbon fiber-reinforced polymer (CFRP) composites through volumetric ultrasonic testing (UT) 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 was 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 (NAS) from a modified 3-D residual neural network (ResNet) search space. In addition, 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 while maintaining a model size smaller than most 2-D ResNets. Each model had an inference time of less than 0.5 s, making them efficient for the interpretation of large amounts of data.

Transforming Industrial Manipulators via Kinesthetic Guidance for Automated Inspection of Complex Geometries.

The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilizing traditional programming approaches demand specialized robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process. Therefore, this body of work proposes a novel software system to realize kinesthetic guidance for path planning in real-time intervals at 250 Hz, utilizing an external off-the-shelf force-torque (FT) sensor. The proposed work is demonstrated on a 500 mm2 near-net-shaped Wire-Arc Additive Manufacturing (WAAM) complex component with embedded defects by teaching the inspection path for defect detection with a standard industrial robotic manipulator in a collaborative fashion and adaptively generating the kinematics resulting in the uniform coupling of ultrasound inspection. The utilized method proves superior in performance and speed, accelerating the programming time using online and offline approaches by an estimate of 88% to 98%. The proposed work is a unique development, retrofitting current industrial manipulators into collaborative entities, securing human job resources, and achieving flexible production.

Single-mode Lamb wave excitation at high-frequency-thickness products using a conventional linear array transducer.

Lamb wave excitation at high-frequency-thickness products offers a potential solution for high-resolution guided wave testing. The method is attractive for crack imaging and corrosion mapping, especially in hidden locations where direct access is limited. However, multiple modes may propagate, complicating signal interpretation, which is undesirable. In this work, a systematic approach is presented, in an effort to determine the influence of the key parameters related to single higher order Lamb wave mode excitation using a conventional linear array transducer. Specifically, a linear time delay law is used to enhance the targeted mode, while the array's length, pitch and apodisation profile remain to be optimally selected. First, an analytical solution is derived based on modal analysis. This provides a natural decomposition of the amplitude of a guided wave mode into the product of the response of a single element and the excitation spectrum. Then, a key observation is made, associating the excitation spectrum to the directivity function for bulk wave phased array steering. This allows the application of well-established phased array analysis tools to guided wave phased array excitation. In light of this fact, minimisation of the spectrum's bandwidth, elimination of the grating lobes and derivation of an apodisation profile are performed, to enhance the purity of the targeted mode. Finally, experiments conducted on an aluminium plate verify the above theoretical results. The Full Matrix is acquired, and all signals are reconstructed synthetically.

Automated Real-Time Eddy Current Array Inspection of Nuclear Assets.

Inspection of components with surface discontinuities is an area that volumetric Non-Destructive Testing (NDT) methods, such as ultrasonic and radiographic, struggle in detection and characterisation. This coupled with the industrial desire to detect surface-breaking defects of components at the point of manufacture and/or maintenance, to increase design lifetime and further embed sustainability in their business models, is driving the increased adoption of Eddy Current Testing (ECT). Moreover, as businesses move toward Industry 4.0, demand for robotic delivery of NDT has grown. In this work, the authors present the novel implementation and use of a flexible robotic cell to deliver an eddy current array to inspect stress corrosion cracking on a nuclear canister made from 1.4404 stainless steel. Three 180-degree scans at different heights on one side of the canister were performed, and the acquired impedance data were vertically stitched together to show the full extent of the cracking. Axial and transversal datasets, corresponding to the transmit/receive coil configurations of the array elements, were simultaneously acquired at transmission frequencies 250, 300, 400, and 450 kHz and allowed for the generation of several impedance C-scan images. The variation in the lift-off of the eddy current array was innovatively minimised through the use of a force-torque sensor, a padded flexible ECT array and a PI control system. Through the use of bespoke software, the impedance data were logged in real-time (≤7 ms), displayed to the user, saved to a binary file, and flexibly post-processed via phase-rotation and mixing of the impedance data of different frequency and coil configuration channels. Phase rotation alone demonstrated an average increase in Signal to Noise Ratio (SNR) of 4.53 decibels across all datasets acquired, while a selective sum and average mixing technique was shown to increase the SNR by an average of 1.19 decibels. The results show how robotic delivery of eddy current arrays, and innovative post-processing, can allow for repeatable and flexible surface inspection, suitable for the challenges faced in many quality-focused industries.

Collaborative Robotic Wire + Arc Additive Manufacture and Sensor-Enabled In-Process Ultrasonic Non-Destructive Evaluation.

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part. This work presents a synchronized multi-robot WAAM and NDE cell aiming to achieve (1) defect detection in-process, (2) enable possible in-process repair and (3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, was performed on a titanium WAAM straight sample containing an intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data were acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient signal-to-noise ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair.

Sensor-Enabled Multi-Robot System for Automated Welding and In-Process Ultrasonic NDE.

The growth of the automated welding sector and emerging technological requirements of Industry 4.0 have driven demand and research into intelligent sensor-enabled robotic systems. The higher production rates of automated welding have increased the need for fast, robotically deployed Non-Destructive Evaluation (NDE), replacing current time-consuming manually deployed inspection. This paper presents the development and deployment of a novel multi-robot system for automated welding and in-process NDE. Full external positional control is achieved in real time allowing for on-the-fly motion correction, based on multi-sensory input. The inspection capabilities of the system are demonstrated at three different stages of the manufacturing process: after all welding passes are complete; between individual welding passes; and during live-arc welding deposition. The specific advantages and challenges of each approach are outlined, and the defect detection capability is demonstrated through inspection of artificially induced defects. The developed system offers an early defect detection opportunity compared to current inspection methods, drastically reducing the delay between defect formation and discovery. This approach would enable in-process weld repair, leading to higher production efficiency, reduced rework rates and lower production costs.

Clinical Monitoring of Tooth Wear Progression in Patients over a Period of One Year Using CAD/CAM.

PURPOSE: The aim of this study was to clinically monitor the progression of tooth wear over a period of 1 year in a cohort of referred tooth wear patients through the use of a computer-aided design/computer-assisted manufacture (CAD/CAM) scanner and a standardized scanning/assessment methodology. MATERIALS AND METHODS: Polyether impressions were made of 11 participants (130 teeth) at baseline and at 1 year. Impressions were poured in type IV dental stone and the anterior teeth were 3D scanned. A surface-matching software was used to compare 1-year and baseline scans and identify any dimensional differences. RESULTS: Parafunctional habits were reported by all patients. All participants exhibited tooth wear ≥ 140 µm in depth and extending to ≥ 280 µm in at least one tooth. Maxillary central incisors were the most commonly and severely affected teeth. CONCLUSION: The ability of the developed CAD/CAM scanning methodology in clinical monitoring of tooth wear was demonstrated. Further research is needed to assess its practicality in large-scale epidemiologic tooth wear studies.

A Proposed Methodology to Assess the Accuracy of 3D Scanners and Casts and Monitor Tooth Wear Progression in Patients.

PURPOSE: The aim of this study was to detail and assess the capability of a novel methodology to 3D-quantify tooth wear progression in a patient over a period of 12 months. MATERIALS AND METHODS: A calibrated stainless steel model was used to identify the accuracy of the scanning system by assessing the accuracy and precision of the contact scanner and the dimensional accuracy and stability of casts fabricated from three different types of impression materials. Thereafter, the overall accuracy of the 3D scanning system (scanner and casts) was ascertained. Clinically, polyether impressions were made of the patient's dentition at the initial examination and at the 12-month review, then poured in type IV dental stone to assess the tooth wear. The anterior teeth on the resultant casts were scanned, and images were analyzed using 3D matching software to detect dimensional variations between the patient's impressions. RESULTS: The accuracy of the 3D scanning system was established to be 33 µm. 3D clinical analysis demonstrated localized wear on the incisal and palatal surfaces of the patient's maxillary central incisors. The identified wear extended to a depth of 500 µm with a distribution of 4% to 7% of affected tooth surfaces. CONCLUSION: The newly developed 3D scanning methodology was found to be capable of assessing and accounting for the various factors affecting tooth wear scanning. Initial clinical evaluation of the methodology demonstrates successful monitoring of tooth wear progression. However, further clinical assessment is needed.