Fast Inspection Research Articles

Gradient transformation-weighted centroid: enhancing peak localization precision in the line chromatic confocal sensing system

Line chromatic confocal imaging (LCI) is an advanced system used for quick and accurate three-dimensional surface imaging without vertical mechanical scanning. It is extensively employed for fast industrial inspection. For high-speed and accurate measurement characteristics of the LCI technique, a rapid and accurate peak localization method is important. In this paper, we propose a gradient transform weight centroid method (GTWC) to improve the accuracy of peak positioning, without compromising high-speed characteristics. This method helps reconstruct the 3D profile at a higher axial resolution by reducing the full width at half maximum (FWHM) without altering the original structure of the LCI system. Both simulations and experiments show the feasibility and good performance of this method.

Optimal Design of a Sensor Network for Guided Wave-Based Structural Health Monitoring Using Acoustically Coupled Optical Fibers.

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. Unfortunately, the FBG sensors and the equipment needed for interrogation is quite expensive, and hence their number is restricted. In the previous work by the authors, the number and location of the actuators was optimized for developing a SHM system with a single sensor and multiple actuators. But through the use of the phenomenon of acoustic coupling, multiple locations on the structure may be interrogated with a single FBG sensor. As a result, a sensor network with multiple sensing locations and a few actuators is feasible and cost effective. This paper develops a two-step methodology for the optimization of an actuator-sensor network harnessing the acoustic coupling ability of FBG sensors. In the first stage, the actuator-sensor network is optimized based on the application demands (coverage with at least three actuator-sensor pairs) and the cost of the instrumentation. In the second stage, an acoustic coupler network is designed to ensure high-fidelity measurements with minimal interference from other bond locations (overlap of measurements) as well as interference from features in the acoustically coupled circuit (fiber end, coupler, etc.). The non-sorting genetic algorithm (NSGA-II) is implemented for finding the optimal solution for both problems. The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly.

Spatial-frequency parallel subsampling for distributed compressive sensing in ultrasonic imaging inspection

To address the problem of the high hardware requirements and insufficient data storage capacity in current ultrasonic imaging testing, a novel approach is developed using a programmable device, which combines spatial-frequency parallel subsampling with the distributed compressive sensing simultaneous orthogonal matching pursuit (DCS-SOMP) algorithm to achieve fast and high-quality ultrasonic imaging inspection with a small amount of subsampled data. The spatial sparse measurement method was employed to achieve spatial subsampling and minimize the count of signals. Additionally, frequency subsampling was utilized to significantly reduce the data volume of time-domain signals while ensuring signal quality by truncating the primary testing frequency components. The subsampled data was then reconstructed using distributed compressive sensing (DCS) for multi-channel data reconstruction. The experiment of ultrasonic scanning imaging was conducted on a carbon steel specimen containing six transverse through-holes with a diameter of Ф1.5 mm at different depths. The ultrasonic signals were acquired using the spatial-frequency parallel subsampling method, and subsequently reconstructed using the DCS-SOMP algorithm. The results show that the proposed method achieves comparable image quality to that obtained with complete data, using only 1/8 of the complete data, while accurately locating and quantifying defects.

Development of a flexible arrayed eddy current sensor with three-phase excitation for inspection of rail rolling contact fatigue cracks

A Flexible Arrayed Eddy Current (FAEC) sensor can inspect a large area in a single probe pass, which is beneficial for customizing the profile of the inspected part. This paper presents the development of a flexible arrayed eddy current sensor with three-phase excitation for inspection of Rolling Contact Fatigue (RCF) cracks in rail. The excitation coils are driven by three-phase currents that are 120° apart in phase. The induced eddy current in the conductive rail sample shifts electrically along the phase variation direction of the excitation currents. The sensor measures a small background signal to determine if there is no defect in the rail sample. Therefore, the sensor has a high relative sensitivity to defects. We study the operating principle of the sensor using a 3D finite element method (FEM) model. The numerical results demonstrate the sensor's viability for fast inspection and characterization to detect RCF defects in conductive rail steel samples.

Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study

Given the current state of medical artificial intelligence (AI) and perceptions towards it, collaborative systems are becoming the preferred choice for clinical workflows. This work aims to address expert interaction with medical AI support systems to gain insight towards how these systems can be better designed with the user in mind. As eye tracking metrics have been shown to be robust indicators of usability, we employ them for evaluating the usability and user interaction with medical AI support systems. We use expert gaze to assess experts’ interaction with an AI software for caries detection in bitewing x-ray images. We compared standard viewing of bitewing images without AI support versus viewing where AI support could be freely toggled on and off. We found that experts turned the AI on for roughly 25% of the total inspection task, and generally turned it on halfway through the course of the inspection. Gaze behavior showed that when supported by AI, more attention was dedicated to user interface elements related to the AI support, with more frequent transitions from the image itself to these elements. When considering that expert visual strategy is already optimized for fast and effective image inspection, such interruptions in attention can lead to increased time needed for the overall assessment. Gaze analysis provided valuable insights into an AI’s usability for medical image inspection. Further analyses of these tools and how to delineate metrical measures of usability should be developed.

Fast inspection of flange faces of welded tubes using photogrammetry and auxiliary measuring tools

The attitudes (including positions and orientations) of flange faces on a multi-branch welded tube are critical to tube installation quality. However, it lacks appropriate approach for flange attitudes inspection. Given the inspection requirements, a novel Auxiliary Measuring Tool (AMT) is designed. A photogrammetry-based approach is put forward to inspect the attitude errors of the flange faces with the aid of the AMT. The specifically designed AMT can achieve flange face centering through a three-jaw chuck structure and establish in-plane rotation by employing a slider to locate the reference bolt hole. An ingenious calibration method is proposed to establish the AMT coordinate frame (ACF) and obtain the marker points’ coordinates in ACF. With the calibrated AMTs attached to each flange, the flange attitude error can be efficiently evaluated by simply capturing a set of images of the tube. Experiments on two welded tubes demonstrate the high performance of the proposed method.

Non-destructive evaluation and Structural Health Monitoring of impact induced damage in composite structures using ultrasonic approaches and shearography

Composite structures are massively used thanks to the high strength-to-weight ratio, making them ideal for lightweight and durable structures in aviation. However, Impact may arise at any time during lifetime on composite aerostructures and ensuring their structural integrity via fast inspection is essential to meet airworthiness criteria. Multimodal NDT (Non-Destructive Testing) inspection and characterization of composite structures is a critical process to verify the quality of the composite material and identify any defects. In this context, the paper shows a comparison of several techniques as nondestructive methods: shearography, laser ultrasonics and phased array ultrasonics. In particular, using shearography and laser ultrasonics as non-contact approaches can increase the inspection speed and reliability compared to the state of the art phased array ultrasonics, which is the standard approach used in the aerospace field. For this reason, a sample of interest to the aerospace industry is subject to different low velocity impacts which are characterized by different energies leading to barely visible damage. The results show the capabilities of both approaches in identifying small emerging defects along with the advantage and drawbacks of both methodologies through a critical review of performance thereof evaluating the parameters of their use. In addition, the same approaches are adopted on a stiffener composite plates and compared with continuous health monitoring approaches. In this regard, NDT approaches are demonstrated useful to enhance the SHM warnings.

Harnessing the power of thermal imagery and visual inspection- a mean for reliable damage detection of wind turbine rotor blades

Generation of green electricity as part of the energy transition is leading to a growing market in the wind energy sector all over the world. Maintenance and inspection are key to the reliability, safety and efficiency of wind turbines, the regular maintenance of rotor blades focuses on damage such as erosion on the leading edge of the profile, delamination and thermal cracks due to lightning strikes. To date, visual inspection by technicians (climbers) has been the state of the art and it is time consuming besides posing safety risk for themselves. Recently, drone-based inspections using visual cameras have become more common, enabling fast, reliable and cost-effective inspections. However, no internal damage to the rotor blades can be detected during such an inspection. Thermography is a recognised method for detecting damage beneath the surface of an object, which has been promoted and further developed at BAM for years. To enhance the accuracy and reliability of wind turbine blade inspection, the fusion of thermal and visual data has emerged as a promising technique. Here, a drone-based multisensory system is being presented that combines thermal imagery, high-resolution visual images and a three-dimensional image obtained from 3D Laser scanner to represent a comprehensive view of the blades' internal and external conditions. Besides this, flight data obtained from IMU, GPS and LiDAR sensors will be used for enhancing the inspection data. Thus, fusion of images enables a more detailed analysis of potential defects and this not only improves the overall effectiveness of the inspection process but also provides windmill operators a new dimensions for data analysis and decision making.

Thermal non‐destructive testing and evaluation for inspection of carbon fibre‐reinforced polymers

Thermal non‐destructive testing and evaluation (NDT&E) is crucial in ensuring the quality and safety of industrial materials, components and structures. It serves as a key tool for assessing their operational reliability, thus enhancing safety in a wide range of industries. There is a growing demand for dependable, swift, remote and secure inspection and assessment techniques to detect hidden flaws, especially for sustainable solutions, which prompts adjustments in design and manufacturing standards. Hidden defects often emerge during the service life of these materials and structures due to various stress factors, potentially resulting in catastrophic failures. This study delves into an optimal and dependable experimental method for conducting fast, remote and secure inspections and assessments of carbon fibre‐reinforced polymer (CFRP) materials using infrared imaging (IRI) as part of thermal non‐destructive testing and evaluation (TNDT&E). Additionally, it examines the post-processing approach associated with this technique. This perspective also sheds light on the current state-of-the-art of infrared imaging methods employed in TNDT&E, emphasising the strengths and weaknesses in their ability to detect subsurface defects present within the material. Most of the methods discussed in previous research primarily focus on the thermal differences in specific areas of a sample using processed thermal images, even though these images come from analysing a series of images captured over time. This study highlights the latest research in thermal/infrared non‐destructive testing and evaluation, along with related post-processing techniques. It not only aims to show hidden subsurface defects through thermal differences but also provides information about how these defects change over time.

FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN

Rail-fastening components are essential for ensuring the safety of urban rail systems by securing rails to sleepers. Traditional inspection methods rely heavily on manual labor and are inefficient. This paper introduces a novel approach to address these inefficiencies and the challenges faced by computer vision-based inspections, such as missed detections due to imbalanced samples and limitations in conventional image segmentation techniques. Our approach transitions the industry’s focus from qualitative to a more precise quantitative analysis of rail-fastening components. We propose Mask-FRCN, an advanced image segmentation network that incorporates three key technological enhancements: the fully refined convolutional network module (FRCN),which refines the segmentation boundaries for SFC-type fasteners; the Channel-WiseKnowledge Distillation (CWD) algorithm, which boosts the model’s inference efficiency; and the FCRM methodology, which enhances the extraction capabilities for features specific to SFC-type fasteners. Furthermore, we introduce a fastener system inspection and quantization method based on the Mask FRCN method (FIQ), a novel technique for quantifying the condition of components by using image features, template matching with random forests, and a clustering calculation method derived from segmentation results. Experimental results validate that our method significantly surpasses existing techniques in accuracy, thereby offering a more efficient solution for inspecting rail-fastening components. The enhanced Mask-FRCN achieves a segmentation accuracy of 96.01% and a reduced network size of 36.1 M. Additionally, the FIQ method improves fault detection accuracy for SFC-type fasteners to 95.13%, demonstrating the efficacy and efficiency of our innovative approach.

Alrecon: computed tomography reconstruction web application based on Solara

Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.

Rapid non-destructive inspection of sub-surface defects in 3D printed alumina through 30 layers with 7 μm depth resolution

The use of additive manufacturing (AM) processes has grown rapidly over the last ten years like fused deposition modelling and stereolithography techniques. 3D printing offers advantages in ceramic component production due to its flexibility. To enhance quality and reduce resource consumption in ceramics industry, fast, integrated, sub-surface and non-destructive inspection (NDI) with high resolution is needed. This study demonstrates sub-surface monitoring of 3D printed alumina parts to a depth of ∼0.7 mm in images of 400 × 2048 pixels with a lateral resolution of 30 μm and axial resolution of 7 μm, using mid-infrared optical coherence tomography (MIR OCT) based on a 4 μm center wavelength MIR supercontinuum laser. We detected individual printed ceramic layers and tracked predefined defects through all four processing steps and demonstrated how a defect in the green phase could affect the final product. This research sets the stage for NDI integration in AM.

Optimization of Yonsei Single-Photon Emission Computed Tomography (YSECT) Detector for Fast Inspection of Spent Nuclear Fuel in Water Storage

Background: The gamma emission tomography (GET) device has been reported a reliable technique to inspect partial defects within spent nuclear fuel (SNF) of pin-by-pin level. However, the existing GET devices have low accuracy owing to the high attenuation and scatter probability for SNF inspection condition. The purpose of this study is to design and optimize a Yonsei single-photon emission computed tomography version 2 (YSECT.v.2) for fast inspection of SNF in water storage by acquisition of high-quality tomographic images.Materials and Methods: Using Geant4 (Geant4 Collaboration) and DETECT-2000 (Glenn F. Knoll et al.) Monte Carlo simulation, the geometrical structure of the proposed device was determined and its performance was evaluated for the 137Cs source in water. In a Geant4-based assessment, proposed device was compared with the International Atomic Energy Agency (IAEA)-authenticated device for the quality of tomographic images obtained for 12 fuel sources in a 14 × 14 Westinghouse-type fuel assembly.Results and Discussion: According to the results, the length, slit width, and septal width of the collimator were determined to be 65, 2.1, and 1.5 mm, respectively, and the material and length of the trapezoidal-shaped scintillator were determined to be gadolinium aluminum gallium garnet and 45 mm, respectively. Based on the results of performance comparison between the YSECT.v.2 and IAEA’s device, the proposed device showed 200 times higher performance in gamma-detection sensitivity and similar source discrimination probability.Conclusion: In this study, we optimally designed the GET device for improving the SNF inspection accuracy and evaluated its performance. Our results show that the YSECT.v.2 device could be employed for SNF inspection.

Alrecon: computed tomography reconstruction web application based on Solara.

Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.

Machine learning based sinogram interpolation for X-ray computed tomography validated on experimental data

The data driven industry 4.0 and increasing mass-customization of additive manufacturing products require a flexible and high-throughput integration of a 100% quality inspection. While XCT has shown to be a viable non-destructive testing and measurement technique, the long acquisition times remain a stumbling block for a cost-efficient integration in production environments. To mitigate the reduced reconstruction quality from fast acquisition protocols a new sinogram interpolation algorithm is proposed. First, the method re-samples the acquired sinogram data to a new data representation which is called a spinogram. Second, the alternative data format is used to predict the missing X-ray projections using a deep learning network. We implemented this method for real XCT scans and validated on a dataset of five laser sintered objects. When applying the proposed method before the reconstruction step, less noise is present in the reconstructed and segmented volumes. This improves the feature analysis of the measured objects while preserving a lower acquisition time, hence extending the use of XCT towards fast quality inspections.

On the defect detection limits of flash thermography in reflection mode: A comprehensive parametric 3D FE study

Optical flash thermography is a promising non-destructive testing technique which offers a fast, full-field and non-contact inspection. This study provides a comprehensive study into the influence of different factors on the physical defect detection limits of hidden defects with flash thermography, which is a missing key piece in the present literature.Therefore, a large-scale parametric 3D finite element study is performed in order to explore the actual defect detection limits for a substantial range of inspection conditions:i.Defect parameters: type, size and depth.ii.Material parameters: anisotropic diffusivity and stacking sequence.iii.Excitation parameters: heating non-uniformity, excitation energy and temporal excitation profile.iv.Acquisition parameters: sampling frequency and measurement noise.The finite element model is carefully designed using the commercial Abaqus software in order to ensure both accuracy and computational efficiency, and its reliability in quantifying defect detectability is experimentally validated. Decisive defect detection thresholds are substantiated, through which the detection limits in several (fiber reinforced) materials are concisely visualized and critically compared for the raw thermographic sequence and well-known processing techniques for flash thermography.

Field Deployable Mirror Soiling Detection Based on Polarimetric Imaging

Heliostat mirror soiling is one of the significant factors for reduced optical efficiency of Concentrated Solar Power (CSP) fields. However, it remains challenging to determine the mirror soiling patterns across the large area of the CSP field. Here we propose a fast and field deployable inspection method to measure the heliostat mirror soiling levels based on polarization images in regular daylight settings without additional light sources. Under sunny and clear sky conditions, we have demonstrated accurate measurement of reflection efficiency (error ~1%) for mirrors with different soiling levels.

A railway fastener inspection method based on lightweight network

To ensure the safety of train operations, regular inspection and maintenance of railway fastener is crucial. Currently, computer vision-based fastener inspection has become the primary method. However, to improve detection accuracy, model complexity has been continuously increasing, leading to a decrease in detection efficiency and seriously affecting real-time fastener inspection task. To address this issue, we propose a fastener inspection method based on lightweight network. First, we introduce YOLOv5n-Faster, where this model selects YOLOv5n as the base network for fastener localization and uses partial convolution (PConv) from FasterNet for lightweight design, while using CoordConv to improve localization accuracy, thereby achieving efficient fastener localization task. Then, we perform lightweight design on the overall network structure and Block module of the ConvNeXt-T to propose the fastener state inspection model, Light ConvNeXt, for rapid fastener classification. Experimental results show that our proposed fastener localization model achieves a mean average precision (mAP) of 84.7%, a detection speed of 161 FPS. The fastener state inspection model achieves an accuracy of 99.7%.

Proposing an Accurate and Fast Optical Batch Inspection Method of Mini-/Micro-LEDs

Proposing an Accurate and Fast Optical Batch Inspection Method of Mini-/Micro-LEDs

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

A Railway Fastener Inspection Method Based on Abnormal Sample Generation