Conventional Non-destructive Testing Research Articles

Evaluation of typical rail defects by induction thermography: experimental results and procedure for data analysis during high-speed laboratory testing

ABSTRACT Rail inspection via non-destructive testing (NDT) techniques is a critical area of research in the railway industry, significantly affecting transport safety and security. Conventional NDT methods face limitations in on-site applications, with emerging techniques improving defect detectability and inspection speed. Recent advances have highlighted infrared thermography, particularly induction thermography, as a promising alternative due to its non-contact, full-field capabilities for detecting both surface and subsurface rail defects. This study explores induction thermography in detecting key defects such as transversal cracks and head checks in different rail tracks. Novel approaches and procedure for data reconstruction that enhance the thermographic inspection results and allow for dynamic testing conditions are proposed. Additionally, the potential for high-speed on-site applications was investigated, utilizing infrared mirrors and optimally shaped coils. Various test parameters, including geometrical resolution, excitation power, and inspection speed up to 20 km/h, were systematically examined.

Preliminary study on terahertz non-destructive testing for defect detection in hot melt joints of polyethylene pipes

With the rapid development of the economy and society, non-metallic pipelines are more and more widely used in oil and gas fields, water supply projects, urban gas systems, and so on. Among the conventional nondestructive testing (NDT) methods, only ultrasonic, radiographic, and penetration testing tools are applicable to non-metallic pipe examination from a theoretical perspective, while their actual efficiency is quite low. In this work, terahertz (THz) non-destructive detection of typical welding defects in polyethylene-pipe hot melt joints is carried out. The characteristic THz wave signal is obtained, and the relationship between the THz wave and the defect type is established. Finally, a THz signal processing model for defect detection in polyethylene-pipe hot melt joints is established. Thus, this research lays a foundation for the application of the THz non-destructive testing technology to polyethylene pipeline engineering.

Research Status and Progress on Non-Destructive Testing Methods for Defect Inspection of Micro-Electronic Packaging

Abstract In a highly competitive and demanding micro-electronics market, non-destructive testing (NDT) technology has been widely applied to defect detection and evaluation of micro-electronic packaging. However, the trend of micro-electronic packaging toward miniaturization, high-density, ultrathin, ultralight, and with small chip footprint, poses an urgent demand for novel NDT methods with high-resolution and large penetration depth, which is utilized for internal defect detection and identification of advanced complicated packages. The conventional NDT methods for micro-electronic packaging mainly include optical visual inspection, X-ray inspection, active infrared thermography, scanning acoustic microscopy (SAM), atomic force microscopy (AFM), laser Doppler vibration measuring technique, scanning SQUID microscopy (SSM), electrical impedance spectroscopy, scanning electron microscope (SEM), and so on. This paper aims to provide a review of addressing their basic principles, advantages, limitations, and application researches in the field of defect inspection of micro-electronic packaging. Moreover, in order to overcome the shortcomings of the existing NDT methods, this paper emphasizes a novel NDT approach, called hybrid ultrasonic-laser digital holographic microscopy (DHM) imaging inspection method, and discusses its basic principle, merits, key techniques, system construction, and experimental results in detail. When some key technical problems can be solved in further research, this method will become a potentially promising technique for defect detection and evaluation of advanced complicated packages.

Damage Detection in FRP-Reinforced Concrete Elements.

Fiber-Reinforced Polymer (FRP) composites have emerged as a promising alternative to conventional steel reinforcements in concrete structures owing to their benefits of corrosion resistance, higher strength-to-weight ratio, reduced maintenance cost, extended service life, and superior durability. However, there has been limited research on non-destructive testing (NDT) methods applicable for identifying damage in FRP-reinforced concrete (FRP-RC) elements. This knowledge gap has often limited its application in the construction industry. Engineers and owners often lack confidence in utilizing this relatively new construction material due to the challenge of assessing its condition. Thus, the main objective of this study is to determine the applicability of two of the most common NDT methods: the Ground-Penetrating Radar (GPR) and Phased Array Ultrasonic (PAU) methods for the detection of damage in FRP-RC elements. Three slab specimens with variations in FRP type (glass-, carbon- and basalt-FRP, i.e., GFRP, CFRP, and BFRP, respectively), bar diameter, bar depths, and defect types were investigated to determine the limitations and detection capabilities of these two NDT methods. The results show that GPR could detect damage in GFRP bars and CFRP strands, but PAU was limited to damage detection in CFRP strands. The findings of this study show the applicability of conventional NDT methods to FRP-RC and at the same time identify the areas with a need for further research.

A Review on Concrete Structural Properties and Damage Evolution Monitoring Techniques.

Concrete structures have emerged as some of the most extensively utilized materials in the construction industry due to their inherent plasticity and high-strength characteristics. However, due to the temperature fluctuations, humidity, and damage caused by human activities, challenges such as crack propagation and structural failures pose threats to the safety of people's lives and property. Meanwhile, conventional non-destructive testing methods are limited to defect detection and lack the capability to provide real-time monitoring and evaluating of concrete structural stability. Consequently, there is a growing emphasis on the development of effective techniques for monitoring the health of concrete structures, facilitating prompt repairs and mitigation of potential instabilities. This paper comprehensively presents traditional and novel methods for concrete structural properties and damage evolution monitoring, including emission techniques, electrical resistivity monitoring, electromagnetic radiation method, piezoelectric transducers, ultrasonic techniques, and the infrared thermography approach. Moreover, the fundamental principles, advantages, limitations, similarities and differences of each monitoring technique are extensively discussed, along with future research directions. Each method has its suitable monitoring scenarios, and in practical applications, several methods are often combined to achieve better monitoring results. The outcomes of this research provide valuable technical insights for future studies and advancements in the field of concrete structural health monitoring.

Nondestructive evaluation of aircraft stealth coating by Terahertz-time domain spectroscopy: experimental and numerical investigation

ABSTRACT Particle-reinforced polymer matrix composites (PMCs) are often used as radar-absorbing materials (RAM) in aircraft stealth applications. The thickness evaluation of such multilayered coatings with micrometre-level thickness is highly challenging from single-side access. In this study, we used Terahertz Time-domain Spectroscopy (THz-TDS) in the transmission mode to extract the refractive index of the dielectric coatings for measuring the thickness using the time-of-flight method. A numerical study based on the Finite Element method (FEM) has also been developed to validate the transmission experiments. A frequency-dependent complex dielectric parameters must be considered for coatings with high absorption in the THz regime. This was addressed by performing the finite element simulations in a frequency domain. The thickness of each layer of a multi-layered coating is estimated by carrying out the experiments in reflection mode. Since the interface echoes were overlapping, a deconvolution algorithm and frequency thresholding were employed to reconstruct the signals reflected from the different interfaces. Using this technique, the thickness of each layer of coating is estimated accurately in a single measurement, which was challenging to measure using other conventional non-destructive testing (NDT) methods.

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

On‐site inspection of invisible subsurface defects in multiscale structural materials by conventional nondestructive testing (NDT) methods, such as X‐ray and ultrasound, requires complex sample preparation and data acquisition processes. Moreover, the inspected area is very small. Herein, a simple, inexpensive, and ultrasensitive NDT method for identifying and classifying the geometries of subsurface defects using commercial cameras, digital image correlation software, and object detection (OD) algorithms is developed. Three OD algorithms—Faster region‐based convolutional neural network (Faster R‐CNN), Mask R‐CNN, and you‐only‐look‐once (YOLO)v3—are evaluated for their ability to locate defects and identify defect geometries. Specifically, bounding boxes of two sizes (large and small) are applied to the regions of defect‐induced perturbations in strain tensors, which serve as virtual representatives of invisible subsurface defects. The performance of the proposed approach is validated on test datasets of known and unknown defect types. The experimental results confirm that the proposed approach can effectively utilize the surface deformation field information to accurately and reliably locate and identify subsurface defects. The method is nondestructive and low cost, enables real‐time detection, is robust against noise‐dominated deformation fields, and can be applied to various structural deformations. The method is therefore suitable for multiscale structural health monitoring and characterization of internal defects in materials.

Detection of surface cracks on slanted surfaces and through thick irregular insulation layers using polarimetric microwave imaging

Reliable detection of cracks on slanted metal surfaces and through irregular insulation/coating is critical in many inspection applications. Microwave non-destructive testing (NDT) techniques have proven to be effective for crack detection and characterization. However, the utility of conventional microwave NDT techniques for challenging inspection scenarios which involve cracks on slanted surfaces, or cracks under relatively thick, and possibly, irregular insulation has been limited. In this paper, a polarimetric microwave imaging approach is employed for the detection of cracks in such inspection cases. The detection concept using interrogation with dual polarized wave to suppress the background is presented. It is established herein that the cross-polarization measurement is independent to irregular surfaces, and therefore, it constitutes an effective mean for reliable crack detection even if the background is not uniform or if the metal surface is slanted. This fact is further corroborated by numerical electromagnetic simulations and measurements. The efficacy of the proposed method is demonstrated using imaging results of various samples spanning the abovementioned scenarios.

Experimental investigation on interfacial defect detection for SCCS with conventional and novel contact NDT techniques

In order to ensure the mechanical performance and structural safety of steel–concrete composite structures (SCCS), advanced nondestructive testing (NDT) technique for bonding status at steel–concrete interfaces needs to be developed. In this study, the feasibility of multichannel analysis of surface waves (MASW)-based interfacial debonding detection is validated using the contact sensors array. Herein, the multichannel sensor arrays are composed of piezoceramic lead zirconate titanate patches and high-frequency acceleration meters, respectively. For comparison, the impact-response (IR) method and impact-transmission (IT) method are performed utilizing a force hammer and high-frequency acceleration meters, and corresponding damage imaging algorithms are developed. The applicability of ultrasonic computed tomography (CT) scanning test, scanning impact echo (IE) method, ultrasonic tomography (UT) technique on interfacial debonding detection is further discussed in depth. Research findings indicate that the developed contact MASW measurement can fully capture the variation of dispersion characteristics of surface waves induced by debonding defects in SCCS. The developed IR and IT methods are suitable for detecting interfacial debondings in different dimensions. Besides, the damage nephogram resolution of IR is higher than that of the IT method. In addition, the practicability of traditional ultrasonic CT scanning tests, scanning IE method, and UT techniques using commercial equipment is investigated. Experimental observations show that classical NDT testing techniques are incapable of effectively identifying the existence of interfacial damage and are unsuitable for NDT tests on SCCS. The research findings in this study clearly exhibit the precision and limitation of various contact NDT techniques and lay a solid foundation for interfacial debonding detection in practical SCCS.

Applications of Machine Learning to Predict the Flexural Bearing Capacity of Hollow Core Slabs After Fire Exposure

Conventional evaluation of the overall mechanical properties and ultimate flexural capacity of prestressed hollow core slabs after a fire exposure depends heavily on the inversion of fire scene temperature. To avoid this drawback, this paper presents a new methodology which combines a generalized regression neural network (GRNN) with conventional non-destructive testing technology. Thereby, a neural network model for predicting the material performance parameters after fire exposure is obtained based on conventional testing indices. A hollow core slab bridge is used as an example, and the applicability of the trained network model is confirmed using numerical simulation and a field failure test. Results show that the overall relative error of GRNN in predicting the key performance parameters of the bridge after fire exposure is less than 10%. Further, because of the good thermal inertia of the concrete, the relative error in predicting the material performance parameters of steel after a fire is less than 5%. Moreover, the ultimate flexural capacity of the prestressed hollow core slab after a fire can be accurately evaluated by feeding the material performance parameters predicted by GRNN neural network into the finite element (FE) model.

Investigation of Kissing Bonds in Adhesive Joints

Kissing bonds are critical defects in adhesively bonded parts. These defects result in a weak bonding in the interface region between the adherent and the adhesive. Unlike delaminations, kissing bonds induce a delicate change in contact properties. Thus, conventional non-destructive testing methods are not able to detect kissing bonds reliably. In this work various non-destructive testing techniques on kissing bonds were investigated. The manufactured kissing bond containing specimens were investigated with different non-destructive testing methods such as conventional ultrasound, aircoupled ultrasound, x-ray and shearography. Unfortunately, those non-destructive testing methods weren’t able to detect the kissing bonds dependably. On the contrary, the application of nonlinear ultrasound demonstrated the most reliable results in detecting kissing bonds. With this testing setup, the specimens were excited with a piezo shaker in a continuous wave mode and the vibration on the surface of the specimen was detected by laser Doppler vibrometry. After fast Fourier transform (FFT), a dramatic increase in the higher harmonic amplitudes were detected in an area with lower bonding force. The low adhesive forces lead to a higher nonlinearity of the kissing bond area, what results in substantial rise of the higher harmonic amplitudes. The measurements on the kissing bonds with their reduced adhesive force show a substantial rise of higher harmonic amplitudes, which makes it a suitable method for the detection of kissing bonds.

Feasibility of Conventional Non-Destructive Testing Methods in Detecting Embedded FRP Reinforcements

Fiber-Reinforced Polymer (FRP) bars/strands are the most promising alternative to their steel counterparts for reinforcing concrete elements due to their resistance to corrosion, lighter weight, higher strength and better durability. However, very limited research has been conducted in relation to non-destructive testing (NDT) methods that are applicable to damage detection in FRP bars or the detection of FRP reinforcements embedded in concrete. The ability to assess the condition of the relatively new and unique FRP reinforcements will increase the confidence of the construction industry in their use as a reliable substitute for steel reinforcements. This paper investigates the ability of two of the most commonly used NDT methods, Ground Penetrating Radar (GPR) and Phased Array Ultrasonic (PAU), in detecting FRP bars/strands embedded in concrete elements. GPR and PAU tests were performed on two slab specimens reinforced with GFRP (Glass-FRP) bars, the most commonly used FRP bar, with variations in their depth, size and configuration, and a slab specimen with different types of available FRP reinforcements. The results show that GPR devices can detect GFRP bars/strands and CFRP (Carbon-FRP) strands to some extent, and their detectability increases with the increase in their antenna center frequency. On the contrary, PAU is only capable of detecting GFRP and CFRP strands. The results of this paper also emphasize the need for further research and developments related to NDT applications to embedded FRP bars.

Nondestructive Evaluation of Fiber-Reinforced Polymer Using Microwave Techniques: A Review

Carbon-fiber-reinforced polymer (CFRP) is widely acknowledged as a leading advanced material structure, offering superior properties compared to traditional materials, and has found diverse applications in several industrial sectors, such as that of automobiles, aircrafts, and power plants. However, the production of CFRP composites is prone to fabrication problems, leading to structural defects arising from cycling and aging processes. Identifying these defects at an early stage is crucial to prevent service issues that could result in catastrophic failures. Hence, routine inspection and maintenance are crucial to prevent system collapse. To achieve this objective, conventional nondestructive testing (NDT) methods are utilized to inspect CFRP components. However, the restricted field penetration within the CFRP makes conventional NDT approaches ineffective. Recently, microwave techniques have been developed to address the challenges associated with CFRP inspection by providing better material penetration and more precise results. This paper offers a review of the primary NDT methods employed to inspect CFRP composites, emphasizing microwave-based NDT techniques and their key features.

High frequency eddy current inspection of fibre placement in Carbon Fibre Reinforced Polymer Composites

This paper presents experimental and simulation studies of high frequency eddy current non-destructive testing (NDT) methods for inspection of carbon fibre reinforced polymer (CFRP) composites. It is well known that CFRP composite materials are widely used in industry as structural components, and products based on such materials are increasingly developed and introduced to market. In recent years, non-conventional eddy current method operating at high frequencies for NDT of composites begins to attract attention. It provides a non-contact, couplant-free option of inspection when compared with the other NDT methods currently used in industry. As eddy current relies on fibre fabrics in individual plies to form the current pathways, the eddy current paths in composite are unsurprisingly complex due to the structural features of composite laminates when compared with that in homogenous conducting metals for which conventional eddy current NDT is often used in industry. It will be noted that the knowledge is still limited so far about the eddy current distributions in composite laminates due to lack of effective theoretical and numerical models. This paper will focus on the development of a new numerical model that is suitable for use in simulations of high frequency eddy current inspection of fibre placements. An effective model is desirable for understanding the performance dependence of the eddy current testing method on the material properties, composite structural features, and other parameters involved. In this model, the fibre tow layout of individual plies is taken into consideration. The collection of the pickup signals by the sensor probe shows clear images of the fibre placement in different layers. The simulation results are compared with experimental results of fibre placement imaging. The model is useful for understanding the physical process for inspection of composites using high frequency eddy current methods, and for studying the influence of the controllable parameters involved.

Nondestructive Testing Technologies for Rail Inspection: A Review

Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the past. However, these methods have several challenges in terms of detection speed and accuracy. Combined inspection methods have emerged as a promising approach to overcome these limitations. Nondestructive testing (NDT) techniques in conjunction with artificial intelligence approaches have tremendous potential and viability because it is highly possible to improve the detection accuracy which has been proven in various conventional nondestructive testing techniques. With the development of information technology, communication technology, and sensor technology, rail health monitoring systems have been evolving, and have become equally significant and challenging because they can achieve real-time detection and give a risk warning forecast. This paper provides an in-depth review of traditional nondestructive techniques for rail inspection as well as the development of using machine learning approaches, combined nondestructive techniques, and rail health monitoring systems.

Real-time terahertz characterization for composite delamination using a lightweight CPU adaptive network

Delamination defects, as common damages occurring in fiber reinforced composite laminates, are often difficult to be detected due to their invisibility and concealment. Terahertz (THz) technique, as a promising alternative to conventional nondestructive testing (NDT) approaches, has emerged great potentials in quantitative characterization of delamination defects in composites. However, since THz signals are susceptible to confounding interference during THz inspection, such as the noise, interlayer reflection, dispersion and overlap, the accuracy and speed of defect localization and identification are usually contradictory. Generally, to improve the accuracy, many complex manual signal processing methods can be applied, which however will inevitably decrease the inference speed for delamination inspection and cannot fulfill the automatic and real-time analysis requirements in practical THz applications. Here, an intelligent THz 3D characterization system based on the lightweight deep learning (DL) model is proposed to realize the automatic and real-time characterization of hidden delamination defects in composites with high accuracy and low CPU latency. The core aims to specifically design a lightweight and end-to-end CPU adaptive network to obtain the optimal balance between the accuracy and speed. A series of experiments are implemented to validate the superior comprehensive performance of the system in terms of classification accuracy and inference speed on low-cost CPU-based platform. Compared with other methods, the proposed system can be not only able to obtain the superior accuracy, but also to obtain low CPU latency. Therefore, our work provides a novel and general paradigm for DL-based THz intelligent 3D characterization, which will promote the deployment of the system for the automatic and real-time THz characterization in industrial applications.

Assessment of the Structural State of Dissimilar Welded Joints by the Acoustic Emission Method

In this study, we investigated defect detection in dissimilar welded joints by the acoustic emission (AE) method. The study objects were carbide and decarburized interlayers, which are formed at the fusion boundary between austenitic and pearlitic steels. Diffusion interlayers, as a structural defect, usually have microscopic dimensions and cannot be detected using conventional non-destructive testing (NDT) methods. In this regard, the AE method is a promising approach to diagnose metal objects with a complex structure and to detect microscopic defects. In this paper, the AE signatures obtained from testing defect-free specimens and specimens with diffusion interlayers are analyzed. We found that the AE signature for defective and defect-free welded joints has significant differences, which makes it possible to identify descriptors corresponding to the presence of diffusion interlayers in dissimilar welded joints.

Wrinkle defects investigation on displacement response in fiber-reinforced composites using grating projection measurement

Wrinkle defects in fiber-reinforced polymer (FRP) composites are often unavoidable due to various causes during the production process, which are also difficult to be rapidly screened by conventional non-destructive testing (NDT) methods. In order to find defects in FRP within non-destructive status, a three-dimensional (3D) finite element analysis (FEA) containing micro mechanical model of wrinkle has been applied to predict the mechanical responses in elastic range. It is found that displacement responses under certain loads are noticeably distorted by wrinkle, especially out-of-plane displacement under tension, which provides a defect inspection guidance for FRP. Therefore, a novel wrinkle defect detection method for FRP based on grating projection measurement is proposed. Combined with the measured point clouds and surface reconstruction algorithm, the full-field out-of-plane displacement distortion of 10 μm measuring precision can be obtained. Tensile tests with a small force are carried out to ensure the wrinkled FRP specimens in the elastic range, the obtained out-of-plane displacement field satisfactorily reflect the wrinkle-induced disturbances. And the measured out-of-plane displacement using grating projection are in good agreement with the predicted responses from numerical approaches.

A magnetic permeability perturbation testing methodology and experimental research for deeply buried defect in ferromagnetic materials

DC magnetization is generally considered to suppress the usual local magnetic permeability variation of ferromagnetic materials but also causes neglected non-uniform magnetic characteristics. This paper proposes a new magnetic permeability perturbation testing (MPPT) method, which uses DC magnetization to excite surface permeability perturbation of deeply buried defects and uses an eddy current probe to sense this surface permeability perturbation. The mechanism of MPPT is analyzed in detail and verified by a series of experiments. The effects of the differential distance of probe, crack size, insulation thickness, and excitation frequency on MPPT are investigated experimentally. Finally, the comparison of the proposed MPPT method with traditional eddy current testing and magnetic flux leakage testing is discussed in detail. The proof-of-concept results indicated that the MPPT testing method can realize the detection of internal defects of a 25 mm thick steel plate. This method is completely feasible and has great practical value as a supplement to conventional nondestructive testing methods.

A Robotic Platform for Aircraft Composite Structure Inspection Using Thermography

Water ingression is a critical issue in honeycomb composite structures, which could result in catastrophic structural failure. In the aviation industry, they are widely used to manufacture critical aircraft structural components including fuselage, wings, and flight control surfaces. Catastrophic failure of these structures would be disastrous, thus identifying water accumulation in earlier stages of the defect is necessary. The conventional non-destructive testing method is thermography which is performed using handheld thermography cameras by manually accessing the specific areas. This method of inspection has been identified to be a risky, costly, time-consuming, and inspector-dependent technique. This paper describes using a wall-climbing robotic platform that can be controlled remotely to access and perform the inspection on a targeted structural area replacing the manual process. The designed wall-climbing inspection robot onboard a heat pump to stimulate the composite surface to an adequate temperature and, an infrared sensor to feed the real-time temperature data via Bluetooth serial communication to a remote computer system to be processed into a thermal image and evaluated to determine the presence of water. The results obtained from the thermographic sensor are validated with the comparison of the Fluke thermography camera.