Nondestructive Inspection System Research Articles

Gauging and Imaging of Pipes using Water-Immersible Ultrasonic Instrumentation System

Abstract The purpose of this research work is to establish the functionality of the novel ultrasonic non-destructive inspection system and accurate gauging of pipes and to locate and visualise flaws in the form of B-Scan cross-sectional view (front-view) of the pipe under test. In this paper, presents a custom-made perspex inspection head assembly integrated with a stand-alone, Li-ion battery-powered and IP67-grade water-immersible ultrasonic instrumentation and gauging system, which enables an efficient assessment of the condition and health of pipes in stringent environments. Extensive inspection was carried out on six samples of 12” Inner Diameter (ID) type carbon steel (CS) pipes with length of 500 mm and having machined wall thickness to simulate loss of wall-thicknesses from 10 % over a length 150 mm of pipe, using 5 MHz spherically focused transducers. Further inspection were carried out on a 12” CS pipe with four notches and four flat bottom holes (FBH) machined on the OD side. Identical flaws were also machined onto 12” CS pipe of total length of 700 mm containing water inside the pipe in flowing condition with water flow rate of 100 litres per minute (LPM). The test results demonstrate the effectiveness of the developed IP67 grade water-immersible ultrasonic pipe inspection and gauging instrumentation system for assessing the condition and health of long-length carbon steel pipes operating in harsh environments.

Image up-sampling method for dual-radiation nondestructive cargo inspection system

In this paper, an image up-sampling method for a dual-radiation nondestructive cargo inspection system, that uses x-ray and neutron, is proposed. The inspection system is assumed to have a larger neutron detector pixel size than that of x-ray detector pixel, which is usually the case to address the lower neutron source flux and the lower detector efficiency than those in the x-ray imaging system. The proposed technique adopts a cluster map of an x-ray projection image with high spatial resolution to up-sample the corresponding neutron projection image with low spatial resolution. The cluster map is acquired by use of a modified k-means clustering algorithm. The up-sampling process is formulated by a linear least-square problem of mapping the neutron projection image onto the cluster map. Consequently, the neutron projection image would have the same dimension with the x-ray projection image array allowing R-value based material decomposition. We have conducted a numerical study using GATE simulation to demonstrate the efficacy of the proposed method. The proposed method was compared with a linear interpolation method and also with an up-sampling technique that does not use the cluster map. The outperformance of the proposed method over those reference techniques has been successfully presented.

Inspection of antimicrobial particles in milk using RGB-laser scattering imaging combined with chemometric procedures

The immobilisation of phytochemicals on inert particles has demonstrated high potential to develop new formulations of natural antimicrobials for preserving liquid foods such as milk. This work aimed to test the capability of the laser scattering imaging technique combined with chemometric procedures to inspect antimicrobial particles in milk matrices non-destructively. The RGB laser's interactions with the milk matrix were captured as diffraction patterns in digital images, from which data were extracted after imaging processing and modelled by chemometric procedures. The antimicrobials were based on solid inorganic particles (silica) functionalised with a plant essential oil (eugenol). The effect of the natural variability from five types of milk was studied (cow 0, 1.5 and 3.5% lipids, goat and sheep). The inspection functions focused on predicting the type of milk, presence of antimicrobial particles, functionalisation of particles and type of inorganic structure. PLS-DA was applied to perform classification models for each property prediction, combined with pre-processing and variable selection procedures to isolate the variability of the property of interest. The results revealed a high classification capacity for milk types. The properties of antimicrobials were also successfully predicted. In this case, data pre-processing and variable selection procedures had to be applied to reduce structural noise and improve the models from raw data. Combining imaging and chemometrics techniques could capture and model the variability generated by antimicrobial particles independently of the nature of milk. It established the basis for developing a possible non-destructive inspection system to control liquid food containing these new antimicrobials.

A Structural Health Monitoring System for Bond Line Flaws Detection on a Full-Scale Wingbox Section Demonstrator

In recent years, there has been a significant increase in the use of structural health monitoring (SHM) technologies as systems for monitoring the integrity of aircraft’s structures. The use of compact and embeddable sensor networks, like the ones based on fibre optics (FO), is particularly attractive from the perspective of releasing an integrated structural system with intrinsic sensing capacity. Usually, an SHM system architecture is completed by a dedicated algorithm that processes the data gathered from the sensors to elaborate on the level of damage currently suffered by the structure, with the further possibility of providing information to the relevant specialists involved with its supervision. One of the main SHM applications that is attracting major interest is related to the inspection and detection of anomalies in bonded joints, which is extremely relevant in many composite realizations. Aeronautical regulations allow the use of bonded joints on an aircraft’s primary structure but require the implementation of a means to ensure their absolute safety, such as the introduction of further mechanical links aimed at stopping the propagation of a possible flaw or the availability of Non-Destructive Inspection (NDI) systems to prove the absence of relevant damaged areas. Generally, the main typical defects occurring during the manufacturing of bonded joints include adhesive curing, kissing bonds, poor porosity, and poor surface preparation. The current NDI systems more widely used and available to detect defects are still inaccurate due to the lack of standard procedures for the creation of representative defects in a controlled manner, which would allow for the development of reliable methodologies and tools able to ensure the safety of a bonded joint, as required by safety regulations. This paper shows the results relative to the implementation of an SHM system developed by the Italian Aerospace Research Centre (CIRA) aimed at monitoring the bonding lines between spar caps and panels of a typical composite wingbox section and detecting faults in location and length. The work was performed during typical ground static tests by using a fibre optical sensing network embedded within relevant adhesive paste layers during the manufacturing process of the structure. In the reported investigation, the SHM system assumed the function of an NDI system tool. The results show that the developed SHM system has good reliability for the detection of both the position and size of damage areas that were artificially inserted within the test article during the bonding phase, showing its potential as a candidate to be used as a tool to verify the conditions of a bonded joint, as required by aviation authorities’ regulations.

Quality control in manufacturing through temperature profile analysis of metal bars: A steel parts use case

Non-uniform heating during metal bar hot forming may impact its straightness. In this study, an infrared non-destructive inspection system is proposed to acquire steel temperature profiles in runtime which should correlate to straightness deviations. Additionally, a machine learning algorithm detects outliers to identify oxides on the metal, which in turn is correlated to process parameters. This allows for proactive temperature adjustment to mitigate the risk based on historical profiles. The proposed approach has been tested in a use case coming from the steel industry.

X-ray multi-scale microfabrication system and x-ray imaging evaluation system all in one beamline

We have completed a system that can achieve both deep x-ray lithography and submicron x-ray lithography with a single beamline by introducing the combination of x-ray plane and cylindrical mirrors. This x-ray lithography system can provide a large-scale microfabrication processing with 210 × 300 mm2 (A4 size). To exploit multiscale lithography, the beamline has a beam transport vacuum duct with a two-stage stacked structure and a 5-axis stage. This two-stage stacked structure allows us to fabricate both micron scale structures with high aspect ratios and submicron scale structures using the same beamline. In addition, x-ray imaging and computer tomography (CT) system are connected to the x-ray lithography system for nondestructive inspection and evaluation of the fabricated microstructures. The x-ray imaging system constructed this study has a relatively low energy range of x-ray energy in the beamline, which is in the range of 2–15 keV or less. Therefore, relatively good absorption contrast can be obtained for plastic materials, biomaterials, and the like. Since nondestructive imaging of the processed shape by x-ray lithography is possible, it is a very useful system in processing and evaluation can be performed simultaneously. This system also enables us to obtain the live images with keeping the creature alive in liquid using an indirect x-ray imaging system which converts x-ray images to visible light images through the fluorescent plate.

Non-destructive Inspection System for Self-piercing Riveting in CFRP-Metal Joining Product Using Stereoscopic Method with Robotic Manipulator

Non-destructive Inspection System for Self-piercing Riveting in CFRP-Metal Joining Product Using Stereoscopic Method with Robotic Manipulator

Non-destructive erosive wear monitoring of multi-layer coatings using AI-enabled differential split ring resonator based system

Unprotected surfaces where a coating has been removed due to erosive wear can catastrophically fail from corrosion, mechanical impingement, or chemical degradation, leading to major safety hazards, financial losses, and even fatalities. As a preventive measure, industries including aviation, marine and renewable energy are actively seeking solutions for the real-time and autonomous monitoring of coating health. This work presents a real-time, non-destructive inspection system for the erosive wear detection of coatings, by leveraging artificial intelligence enabled microwave differential split ring resonator sensors, integrated to a smart, embedded monitoring circuitry. The differential microwave system detects the erosion of coatings through the variations of resonant characteristics of the split ring resonators, located underneath the coating layer while compensating for the external noises. The system’s response and performance are validated through erosive wear tests on single- and multi-layer polymeric coatings up to a thickness of 2.5 mm. The system is capable of distinguishing which layer is being eroded (for multi-layer coatings) and estimating the wear depth and rate through its integration with a recurrent neural network-based predictive analytics model. The synergistic combination of artificial intelligence enabled microwave resonators and a smart monitoring system further demonstrates its practicality for real-world coating erosion applications.

Eddy Current Testing Based Non-Destructive Inspection for Metal 3D Additive Manufacturing Objects With HTS-SQUID

In this work, we applied eddy-current-testing-based HTS-SQUID non-destructive inspection (ECT-based HTS-SQUID NDI) system for inspection of metal 3D additive manufacturing (AM) objects, which were made of AlSi10Mg powder with powder bed fusion (PBF) method. The inspection system combines an ECT probe with an HTS-SQUID gradiometer as high sensitive readout magnetic sensor in low frequency range to overcome disadvantage of conventional ECT. We prepared 5 samples with different densities by changing parameters in the PBF process. Densities of the samples #1-#5 were estimated to be 2.65, 2.64, 2.62, 2.58, and 2.51 g/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , respectively. The samples were measured by the inspection system with an excitation current of 20 mA at 1.6 kHz, and lift-off of about 1 mm. In inspection results, anomalous magnetic patterns appeared on the sample #3, #4 and #5. The inspection results were verified with X-ray CT scanning. Comparison between the ECT inspection and the X-ray CT results reveals that the positions of the anomalous magnetic patterns show strong correlation with the positions of high porosity existing in the samples.

Anomaly Detection of Underground Transmission-Line through Multiscale Mask DCNN and Image Strengthening

This study proposes an integrated framework to automatically detect anomalies and faults in underground transmission-line connectors (UTLCs) with thermal images because anomaly detection of underground transmission-line connectors (UTLCs) plays a critical role in power line risk management. The proposed framework features three key characteristics. First, the measured thermal images were preprocessed through z-score normalization and image strengthening. Z-score normalization improves the robustness of feature extraction for UTLCs even though noise exists in a thermal image, and image strengthening improves the accuracy of segmentation for UTLCs. Second, a preprocessed thermal image is segmented to detect UTLCs by addressing a multiscale mask deep convolutional neural network (MS mask DCNN). The MS mask DCNN effectively detects UTLCs, enabling anomaly detection only for pixels of UTLCs. Specifically, the multiscale feature extraction module enables the extraction of distinct features of UTLCs and environments, and the skip-layer fusion module concatenates distinct features from the feature extraction module. Furthermore, a half tensor is used to reduce computational resources but maintain the same segmentation accuracy, enhancing the feasibility of the proposed framework in field applications. Third, anomaly detection is performed by addressing the contour method and unsupervised clustering method of DBSCAN. The contour method compensates for the limits of the MS mask DCNN for real-world applications because the neural networks cannot secure perfect accuracy of 100% owing to a lack of sufficient training images and low computational resources. DBSCAN improves the accuracy of diagnosis and ensures robustness to eliminate noise from thermal reflection caused by low-emissivity objects. Field experiments with high-voltage UTLCs demonstrated the effectiveness of the proposed framework. Ablation studies also confirmed that the methods addressed in this study outperform other methods. The proposed framework with a novel automatic non-destructive patrol inspection system would decrease the risks of human casualties during the periodic operation and maintenance of UTLCs, which are currently the most critical concerns.

Eddy Current Testing Methods and Design for Pipeline Inspection System: A Review

Non-destructive testing and complex systems integration are commonly used in transportation, aircraft, automotive, manufacturing, petrochemical, and defence. Eddy current testing (ECT) is a common non-destructive evaluation technique, especially in the coating, nuclear, and oil and gas industries. However, the eddy current testing method must continue to focus on the probe’s construction and application. It ascribed noisy data to inadequate detection and a severe false alarm problem. Nonetheless, only few academics have elaborated on the intelligent ECT crack detection method. This study provides an overview of the eddy current testing technique and the probe structure design elements that influence fracture detection accuracy. The first section describes the Eddy current testing by encircling coil system for a differential probe (ECDP) and an encircling coil for an absolute probe design (ECDP). The explanations focus mostly on the probe’s creation and detection in Rotating electromagnetic field eddy current (RoFEC). The advantages and disadvantages of eddy current testing surrounding probes are analysed. In addition, a summary of prior studies investigating testing samples of ferromagnetic pipe, steel rod, and wire rope is presented. The second segment focuses on Eddy current testing employing a Giant Magnetoresistance (GMR) sensor system, with a particular emphasis on sensor arrays, GMR, and conventional eddy current coils for crack identification. Finally, a comprehensive overview of eddy current testing methods and design for non-destructive pipeline inspection systems is presented.

Development of fast scanning module with a novel bubble solution applied to scanning acoustic microscopy system for industrial nondestructive inspection

Scanning acoustic microscopy (SAM) system is a powerful nondestructive instrument owing to its capability to provide internal information and defect positions inside a material. In industrial applications of SAM, the scanning time plays an important role in enhancing efficient inspection of the product line. Several studies have used the traditional SAM system, which develops based on linear motor to inspect industrial samples, but the inspection time remained relatively long and cost-prohibitive. Herein, we reported a low-cost fast SAM (FSAM) system that provided the image results in a short time while maintaining their high resolution. In this design, FSAM fast scanning module (FSM) was developed by exploiting the slider-crank mechanism, which was optimized for a specific product to substantially reduce the inspection time. During the scanning process, bubbles were generated due to the rapid movement of transducer into the water, namely the bubble cavitation. The bubbles restricted the ultrasound wave propagation, which resulted in the bad image quality. The bubble cavitation phenomenon was examined using the incompressible flow theory to investigate the bubble inception, which was defined based on the pressure value around transducer. The bubbles appeared in areas where the pressure less than the vapor pressure. Using the OpenFOAM to analyze the pressure value, the bubble problem was solved by a novel solution that used a bubble reduction plate attaching to the transducer. The coin and aluminum samples with known dimensions and internal structure were prepared to scan using the FSAM system, which validated the simulation results and FSAM capabilities in providing good images with internal information and accurate measurements. Finally, FSAM system was designed and developed to successfully inspect industrial samples in a short time, such as an integrated circuit chip, printed circuit board, welded sheets, and silicon wafer. Using the FSAM system to scan welded sheets, the scanning time were reduced by approximately 77% compared to the traditional SAM system. These results demonstrate that the FSAM system is a potential instrument for industrial nondestructive inspection.

Non-destructive Inspection System Development for Secondary Battery Welding Part

In this paper, we develop a non-destructive inspection system for secondary battery welding. In a secondary battery, when the insulation or separator of each electrode is damaged by an impact from the outside depending on the degree of welding, an internal short circuit occurs during charging and fires even if it does not ignite at that time. To detect this, a non-destructive AOI (Automatic Optical Inspection) system is developed that compares the inspection target with the reference image to determine whether there is a proper indentation in the welding part. The system consists of a precision alignment stage on the lower part and imaging equipment that performs AOI, a non-destructive inspection on the upper part. And the appropriate exposure, i.e., the aperture setting of the used camera, was confirmed through the experiment according to the position of the pole .

Applicability Evaluation of Surface and Sub-Surface Defects for Railway Wheel Material Using Induced Alternating Current Potential Drops.

The majority of catastrophic wheelset failures are caused by surface opening fatigue cracks in either the wheel tread or wheel inner. Since failures in railway wheelsets can cause disasters, regular inspections to check for defects in wheels and axles are mandatory. Currently, ultrasonic testing, acoustic emissions, and the eddy current testing method are regularly used to check railway wheelsets in service. Yet, in many cases, despite surface and subsurface defects of the railroad wheels developing, the defects are not clearly detected by the conventional non-destructive inspection system. In the present study, a new technique was applied to the detection of surface and subsurface defects in railway wheel material. The results indicate that the technique can detect surface and subsurface defects of railway wheel specimens using the distribution of the alternating current (AC) electromagnetic field. In the wheelset cases presented, surface cracks with depths of 0.5 mm could be detected using this method.

Nondestructive testing and 3D imaging of PE pipes using terahertz frequency-modulated continuous wave.

Polyethylene (PE) pipes are widely used as the main carrier for the transportation of natural gas, so nondestructive testing techniques for PE pipes are essential for the safety of natural gas transportation. In order to compensate for the shortcomings of conventional inspection methods, a terahertz (THz) three-dimensional imaging system for nondestructive inspection of PE pipes is designed. The system is based on frequency-modulated continuous-wave (FMCW) technology, with a THz source bandwidth of 0.225-0.330THz and an output power of over 5mW, which can achieve submillimeter spatial resolution in three dimensions. The system is used to scan PE pipes in three dimensions in a laboratory environment, and the results show that the system could achieve nondestructive testing and three-dimensional imaging of different defects in PE pipes. In addition, combined with the deep-learning-based THz transformer network, the intelligent identification of different defects is realized, and the accuracy rate can reach up to 88%. The above results provide technical guidance for the application of THz FMCW systems in the actual detection of PE pipes, and provide supplements and improvements for traditional detection methods.

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by general and standard steps, such as image acquisition, pre-processing and segmentation, extraction and classification of image characteristics. This paper describes the design and implementation of a real-time fresh fruit bunch (FFB) maturity classification system for palm oil based on unrestricted remote sensing (CCD camera sensor) and image processing techniques using five multivariate techniques (statistics, histograms, Gabor wavelets, GLCM and BGLAM) to extract fruit image characteristics and incorporate information on palm oil species classification FFB and maturity testing. To optimize the proposed solution in terms of performance reporting and processing time, supervised classifiers, such as support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN), were performed and evaluated via ROC and AUC measurements. The experimental results showed that the FFB classification system of non-destructive palm oil maturation in real time provided a significant result. Although the SVM classifier is generally a robust classifier, ANN has better performance due to the natural noise of the data. The highest precision was obtained on the basis of the ANN and BGLAM algorithms applied to the texture of the fruit. In particular, the robust image processing algorithm based on BGLAM feature extraction technology and the ANN classifier largely provided a high AUC test accuracy of over 93% and an image-processing time of 0,44 (s) for the detection of FFB palm oil species.

Phase estimation via riesz transform in laser speckle interferometry for large-area damage imaging

A direct phase estimation (DPE) via Riesz transform from a speckle pattern (images) for visualizing barely visible impact damage (BVID) occurring in layered composites is proposed in this paper. Rather than complex optical hardware setup for phase extraction in commercial digital speckle pattern interferometry (DSPI) or shearography (SG) systems, the proposed numerical (software) DPE implementation via Riesz transform with a log-Gabor filter enables a compact, portable, and cost-effective system for large-area real-time nondestructive inspection (NDI) due to its simplicity, stability, and efficiency.Using speckles in laser speckle interferometry (LSI) speckle pattern is intrinsically noisy; the correlation-based phase signatures derived from Riesz transform followed by the log-Gabor filter is implemented to supplant the conventional phase-difference algorithm to localize damage regions, especially in the case where the high-power diode laser (low coherence) can cause serious phase drift. By enveloping the monogenic signal through Riesz transform for direct phase retrieval is more intuitive than TPS or SPS. This yields visualization of the damage feature with higher fidelity. Consequently, a phase-correlation algorithm becomes a more robust operation to cope with speckle images from the interferometry.This full-field optical SG system was demonstrated and verified in an impacted damaged honeycomb composite panel using the proposed phase-correlation algorithm under thermal stress loading for large-area inspection in near real-time. The visualized damage images enhanced by principal component analysis (PCA) agreed with images from pulse laser/LDV and X-ray CT scan. The DPE algorithm has a promising potential to apply on any optical metrology for instantaneous phase estimation and can combine with selected imaging conditions for multi-damage detection.

Defect Shape Classification Using Transfer Learning in Deep Convolutional Neural Network on Magneto-Optical Nondestructive Inspection

To implement a magneto-optic (MO) nondestructive inspection (MONDI) system for robot-based nondestructive inspections, quantitative evaluations of the presence, locations, shapes, and sizes of defects are required. This capability is essential for training autonomous nondestructive testing (NDT) devices to track material defects and evaluate their severity. This study aimed to support robotic assessment using the MONDI system by providing a deep learning algorithm to classify defect shapes from MO images. A dataset from 11 specimens with 72 magnetizer directions and 6 current variations was examined. A total of 4752 phenomena were captured using an MO sensor with a 0.6 mT magnetic field saturation and a 2 MP CMOS camera as the imager. A transfer learning method for a deep convolutional neural network (CNN) was adapted to classify defect shapes using five pretrained architectures. A multiclassifier technique using an ensemble and majority voting model was also trained to provide predictions for comparison. The ensemble model achieves the highest testing accuracy of 98.21% with an area under the curve (AUC) of 99.08% and a weighted F1 score of 0.982. The defect extraction dataset also indicates auspicious results by increasing the training time by up to 21%, which is beneficial for actual industrial inspections when considering fast and complex engineering systems.

Autonomous dynamic line-scan continuous-wave terahertz non-destructive inspection system combined with unsupervised exposure fusion

The investigation of cultural heritage objects is commonly carried out by using non-destructive inspection techniques. In fact, due to the artistic peculiarities of cultural heritage objects, conventional contact-type inspection techniques such as ultrasonic scan cannot always be applied. The objective of this study is to develop a new autonomous dynamic line-scan continuous-wave terahertz (CW THz) non-destructive inspection system combined with long-wave infrared (LWIR) thermography. The newly developed system is aimed at producing clear external and internal maps for wooden objects inherent to the cultural patrimony. Additionally, a new unsupervised fusion algorithm is also proposed for multi-energy density data fusion to correct the inaccurate image acquisition caused by unbalanced line-scan exposure source. The algorithm is designed in an encoder-decoder deep learning structure with dense blocks. Finally, it is worth mentioning that the newly developed system has the capability of speedy detection (up to 49.2 mm/s) and fast in-line processing (of the order of seconds).

Surface defect characterization and depth identification of CFRP material by laser line scanning

The detection of defects on the surface of carbon fiber reinforced polymer has increasingly become the focus of modern NDT research. In this paper, the shape characterization and depth identification of surface defects of CFRP materials are investigated by establishing reflective and transmissive line laser infrared thermography nondestructive inspection systems. First, we verified the feasibility of the work by simulation. Then, the temperature variation of surface defects was analyzed by two experimental schemes, reflective mode and transmissive mode. To characterize the shape of the defects, we deduced the size of the detect from the scan of the line laser. The results show that the characterization accuracy of defect size is different for different scanning speeds, and finally the characterization error can be controlled within 2.2%. In order to achieve the defect depth classification, we used the grey wolf optimization algorithm to optimize the hyper-parameters in the support vector machine, which can finally achieve 97% depth classification accuracy in 0.56s.