Traditional Nondestructive Testing Research Articles

Detecting Multi-Scale Defects in Material Extrusion Additive Manufacturing of Fiber-Reinforced Thermoplastic Composites: A Review of Challenges and Advanced Non-Destructive Testing Techniques

Additive manufacturing (AM) defects present significant challenges in fiber-reinforced thermoplastic composites (FRTPCs), directly impacting both their structural and non-structural performance. In structures produced through material extrusion-based AM, specifically fused filament fabrication (FFF), the layer-by-layer deposition can introduce defects such as porosity (up to 10–15% in some cases), delamination, voids, fiber misalignment, and incomplete fusion between layers. These defects compromise mechanical properties, leading to reduction of up to 30% in tensile strength and, in some cases, up to 20% in fatigue life, severely diminishing the composite’s overall performance and structural integrity. Conventional non-destructive testing (NDT) techniques often struggle to detect such multi-scale defects efficiently, especially when resolution, penetration depth, or material heterogeneity pose challenges. This review critically examines manufacturing defects in FRTPCs, classifying FFF-induced defects based on morphology, location, and size. Advanced NDT techniques, such as micro-computed tomography (micro-CT), which is capable of detecting voids smaller than 10 µm, and structural health monitoring (SHM) systems integrated with self-sensing fibers, are discussed. The role of machine-learning (ML) algorithms in enhancing the sensitivity and reliability of NDT methods is also highlighted, showing that ML integration can improve defect detection by up to 25–30% compared to traditional NDT techniques. Finally, the potential of self-reporting FRTPCs, equipped with continuous fibers for real-time defect detection and in situ SHM, is investigated. By integrating ML-enhanced NDT with self-reporting FRTPCs, the accuracy and efficiency of defect detection can be significantly improved, fostering broader adoption of AM in aerospace applications by enabling the production of more reliable, defect-minimized FRTPC components.

Fast concrete crack depth detection using low frequency ultrasound array SH waves data

Fast detection of the depths of surface-open cracks plays an important role in evaluating the damage conditions of concrete elements. The presence of surface-open cracks and other anomalies inside concrete complicates the ultrasonic wave field and thus severely undermines the precision of traditional nondestructive testing methods. This study introduces independently developed low-frequency ultrasonic array detection equipment. The detector adopts a doubled-ray coverage strategy to enhance the imaging stability under noisy conditions. Moreover, we propose an imaging method called the crack focusing-synthetic aperture focusing technique (CF-SAFT), through which both reflected and transmitted surface waves are removed so that only diffracted SH waves converge to their origins. An extra instantaneous phase analysis is supplemented to highlight the diffraction points. We test the effectiveness of our method through a multitude of numerical examples and a model experiment. Successful depth identification was obtained regardless of different geometries of the cracks or interference from the steel reinforcements. The superiority of our method is further verified through noisy ultrasonic data and complex scenarios.

Influence of sweep duration on debonding detection of honeycomb structures based on acoustic band gap

Abstract Honeycomb structure is widely used in many fields, and its bond quality needs to be tested during manufacture process and in service. However, the efficiency, sensitivity, and feasibility can hardly be achieved simultaneously for traditional non-destructive testing methods. In the previous work, we have proposed a new debonding detection method using the acoustic band gap of honeycomb structures. Nevertheless, the influence of the detection parameters on the testing result has not been thoroughly investigated. In this paper, the effect of duration variation of the sweep signal used for exciting on the results is studied. Experimental results demonstrate that the transmission frequency response (TFR) of both the intact and the debonding area almost keep unchanged as the sweep duration varies from 1 s to 0.001 s. Theoretical analysis shows that the upper limit of the sweep rate is about 500 MHz/s, i.e., the minimum sweep duration is about 1.2 × 10−5 s to detect a bandgap with a bandwidth of about 6 kHz. Therefore, a shorter sweep duration can be used to further improve the detection efficiency.

Image analysis as a geometry- and integrity-independent tool for predicting strength of cemented tailings backfill using slag-based binder

Compressive strength is a crucial indicator for assessing the stability of the cemented tailings backfill (CTB) matrix. The stringent requirements of traditional non-destructive testing (NDT) methods (e.g., ultrasonic pulse velocity and electrical resistivity) regarding the geometry, section flatness, and integrity of the core sample severely limit their use in the in-situ measurement of CTB strength. Therefore, this study proposes a novel NDT technique for assessing the in-situ strength of CTB made from ground granulated blast furnace slag (slag) by means of image analysis. Strength tests and image analysis are carried out in 258 specimens maintained at different solid concentrations (71−79 %), binder contents (6−14 %), activator ratios (from 28:2–20:10) and curing temperatures (5−40℃). In addition, microscopic investigations are carried out to clarify the underlying mechanisms contributing to the macroscopic features. Increasing solid content, desulfurized gypsum to calcium carbide slag (DG/CS) ratio, and curing temperature results in increases in UCS of 87.9−199.9 %, 171.3−421.2 %, and 27.5−173.8 %, respectively. The strength-enhancing effect of increasing the curing temperature diminishes with time, from 173.8 % at 3 days to 27.5 % at 28 days. Strength initially rises and then abruptly decreases as binder content rises. The grayscale value generally has an opposite trend to strength, but the former is less sensitive to changes in influencing factors. An obvious exponential relationship is found between strength and grayscale for all specimens (R2 = 0.92). Compared with traditional NDT methods, the application of image analysis is not restricted to the shape, size, and integrity of CTB and therefore provides a faster, easier, and less costly tool for the quality evaluation of field CTB matrix.

The Method and Experiment of Detecting the Strength of Structural Components Utilizing the Distributed Strain of Sensing Optical Fibers Demodulated by OFDR.

Defects occurring during the welding process of metal structural components directly affect their overall strength, which is crucial to the load-bearing capacity and durability of the components. This signifies the importance of accurate measurement and assessment of weld strength. However, traditional non-destructive testing methods such as ultrasonic and non-contact camera inspection have certain technical limitations. In response to these issues, this paper analyzes the detection principle of weld strength, revealing that weld defects reduce the effective area of the structural bearing section and cause stress concentration around them. Through repeated experimental data analysis of samples, strain distribution data along the one-dimensional direction caused by defects such as slag inclusion and porosity were obtained. Experimental results show that this method can identify defect types in welds, including slag inclusion, porosity, and unevenness, and accurately measure the location and size of defects with a precision of 0.64 mm, achieving qualitative analysis of weld defects. Additionally, by deploying distributed optical fiber sensors (DOFS) at different vertical distances along the weld direction, the propagation law of stress induced by different types of weld defects on samples was thoroughly analyzed. This further validates the advantages of this method in weld strength detection, including high spatial resolution, high sensitivity, and non-destructive measurement.

Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford its versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Tree internal defects detection method based on ResNet improved subspace optimization algorithm

The erosion behavior of trunk borers leads to the destruction of trunk structure and the formation of internal defects, which significantly impacts the ecological and economic value of trees. Traditional non-destructive testing (NDT) methods are costly and have low resolution, whereas electromagnetic NDT methods are more suitable for high-resolution detection and imaging. However, solving the highly nonlinear electromagnetic inverse scattering problems (ISPs) for small-sized defects with high contrast is challenging. Therefore, this paper proposes an improved subspace optimization algorithm based on a ResNet network called SOM-ResNet. SOM-ResNet incorporates physical principles into deep learning networks by simulating the iterative process of induced current and contrast, thereby enhancing its ability to accurately detect small objects with high contrast. Experimental results demonstrate that SOM-ResNet outperforms single inversion algorithms in detecting complex scatterers with small to medium-sized targets, validating its excellent performance.

A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

Computer vision technology has attracted significant interest in the condition monitoring (CM) community due to its potential to automate visual inspection and analysis of structures and components. By facilitating the processing and interpretation of visual information, including images and video data, computer vision holds promise for CM applications. However, it is essential to distinguish computer vision from non-contact CM techniques regarding their underlying principles and methods. While computer vision enables non-contact, remote monitoring, and condition assessment with minimal disruption to daily operations, it is distinct from non-contact CM techniques, which utilize various sensors to assess the condition of assets without physical contact or interference. Building upon the potential of computer vision technology, this survey paper presents a comprehensive overview of the current state-of-the-art CM methods based on computer vision and deep learning (DL) techniques, focusing on their application in monitoring synthetic fiber ropes (SFRs). SFRs are a viable alternative to steel wire ropes for underwater equipment and cranes that handle heavy loads. This is due to their high resistance to frictional wear, high tensile strength, lightweight, and flexibility. New materials, technologies, and processes for CM are being developed to meet the growing demand for SFRs. The paper explores ongoing research in applications that monitor the wear and aging of materials, as well as estimate their remaining useful life. The survey briefly discusses the traditional non-destructive testing and machine learning (ML) methods for CM applications. More importantly, DL-based methods, including supervised, unsupervised, semi-supervised, and self-supervised methods, are discussed in detail, together with the use of deep generative models and the recently developed diffusion models in the generation of synthetic datasets. Furthermore, the paper addresses the difficulties present in DL-based CM applications, including the scarcity of labeled data and the complexity and variety of the models used. The article ends by discussing the benefits of employing DL-based visual methods to understand SFR degradation processes, particularly in monitoring and maintenance.

Real-Time Phased Array Ultrasonic Monitoring of Debonding Growth Under Cycling Loading in CFRP

Carbon Fibre Reinforced Polymer (CFRP) composites are widely employed in diverse industries due to their specific stiffness and strength, and corrosion resistance. However, over time, service-induced damage can initiate a failure that can threaten the operation’s safety and reliability, especially in critical industries like aerospace applications. The challenge lies in identifying and monitoring the defects before they escalate into serious issues. Non-Destructive Testing (NDT) methods play a pivotal role in this scenario, aiming to detect and evaluate defects without causing harm to the material. Detecting flaws in composite materials such as CFRP remains a complex task. Traditional NDT methods often struggle to effectively pinpoint and assess defects within these intricate structures. However, recent advancements in ultrasonic testing, especially Phased Array Ultrasonic Testing (PAUT), show promise in examining the internal composition of CFRP composites with higher accuracy and reliability. This study aims to demonstrate the applicability of PAUT for real-time monitoring of debond growth under dynamic loading. The loading was applied to the test specimens up to one million cycles, and 64 elements 2 MHz linear phased array probe was attached to the specimen. The acquired signals were analyzed and could also be recorded for offline processing or archiving. To validate the credibility of the findings, the performance of PAUT was compared to Acoustic Emission (AE), a well-established method for detecting and analysing high-frequency signals emitted by materials undergoing deformation or damage. Encouragingly, both methods exhibited similar trends in capturing the growth of debond. Furthermore, to verify the final defect lengths, post-test ultrasonic C-Scans utilizing the through transmission technique were conducted, complemented by visual inspections. These assessments served to confirm the accuracy and reliability of the results obtained through Phased Array Ultrasonic Testing. Real-time monitoring of composite structures using phased array technology comprises a valuable tool for real- time assessment of debonding in the composite. As industries continue to rely on Carbon Fibre Reinforced Polymer composites for multifaceted applications, the utilization of advanced Non-Destructive Testing methodologies like Phased Array Ultrasonic Testing emerges as a cornerstone in guaranteeing their continued integrity and performance.

Improving non-destructive testing methods for detecting cavity damage and internal defects in stone cultural relics: A focus on ultrasonic testing and acoustic tapping technology

This study employs advanced ultrasonic tomography and Grasshopper software to investigate the internal anomalies within stone materials. We aim to enhance the non-destructive testing (NDT) methods with potential applications extending to modern architecture and stone engineering. Cavity damage and internal defects threaten the structural stability of stone cultural relics. Traditional non-destructive testing methods, while practical, have limitations in assessing these relics comprehensively. This paper proposes an enhanced ultrasonic testing method based on mathematical analysis to accurately detect cavity damage in stone cultural relics. We also investigate imaging techniques for internal defects, combining ultrasonic testing with Grasshopper software for efficient and accurate defect characterization. Additionally, we introduce an acoustic tapping method for detecting internal defects in ancient building stones. Verified through field tests and data analysis, this method is non-invasive, accurate, and fast. The process of detecting stone defects using this technology, along with data analysis methods, experimental results, and application effects, are detailed. This research contributes to the improved preservation and restoration of ancient buildings and extends to other fields, such as modern architecture and stone engineering, highlighting its significance in cultural heritage conservation.

A review of the Research on Tunnel Lining Voiding Detection Based on Acoustic Vibration Method

In the study of nondestructive testing technology for tunnel lining voiding detection, the traditional nondestructive testing technology has many drawbacks such as poor detection accuracy, high subjectivity, and easy to be interfered with, etc., and the combination of acoustic vibration method and deep learning technology has the problems of uneven distribution of the data set, poor interpretability, and other problems that are difficult to be applied to industrial applications, which summarizes the current acoustic vibration testing technology and its application to the study of tunnel lining voiding. Firstly, the non-destructive testing techniques used in the current tunnel lining voiding detection research are sorted out, and then a variety of tunnel lining voiding detection research methods based on acoustic vibration method with potential applications in the industrial field are analyzed, followed by an overview of the current situation from the point of view of deep learning combined with the acoustic vibration method for the detection of tunnel lining voiding, and then the direction of the next step of the research is discussed and analyzed. The full paper is summarized, and future challenges are pointed out.

Non-contact detection of the interfacial microdefects in metal/CFRP hybrid composites using air-coupled laser ultrasound

Metal/CFRP (carbon fiber reinforced plastic) hybrid composites are crucial in aerospace applications, demanding non-contact, high-resolution inspection. Traditional non-destructive testing methods face daunting technical challenges due to varying densities and impedance between fiber and metal layers. Here, a hybrid air-coupled laser-ultrasound (ACLU) system was presented for non-contact detection of internal microdefects on the non-homogeneous metal/CFRP interface. The near-surface resolution of the system can reach as high as 80 µm on a long effective working distance of 50 mm. Three types of experiments, specifically ACLU, phased-array, and X-ray inspection, were performed in Al/CFRP composites with the interfacial microdefects located in Al, bonded, and CFRP layers respectively. Comparing with traditional methods, the proposed ACLU system can detect the microdefects of bonding failure, cohesive failure, and delamination at the Al/CFRP interface with higher contrast, resolution, and sensitivity. A significant advantage of the ACLU system is its ability to detect defects on curved surface structure materials, making it highly versatile for various composite configurations. Our eventual goal is to offer an affordable non-contact laser-ultrasound approach for quality and damage inspection of metal/CFRP hybrid composites.

RECONSTRUCTION OF BURIED THERMAL SOURCES FROM SURFACE TEMPERATURE FIELDS BY BAYESIAN INFERENCE

Early detection of defects (e.g., cracks, delamination in composites, defects in adhesive bonds) is critical to prevent potential accidents in industrial environments. Traditional nondestructive testing (NDT) methods, such as thermal techniques, can be used to detect and characterize defects buried in the material. In several of these methods, the material is mechanically excited in such a way that the defects behave like heat sources. By studying the resulting thermal fields, inverse methods have been developed to reconstruct these heat sources and, therefore, even more so defects. The reconstruction of volumetric heat sources buried in material is notably ill-posed and requires the use of specific tools, which makes it difficult to reconstruct both the position and, in particular, the source intensity value. In this paper, an algorithm based on a Bayesian approach is proposed. A prior modeling in the form of a mixture of distributions is considered, which promotes spatial sparsity and thereby regularizes the inference problem. This method enables the reconstruction of sources with high fidelity, including the estimation of intensities. Its performance is discussed in relation to noise, and it is compared to other methods, showing favorable potential for NDT applications.

Failure identification and location in asphalt materials using coplanar capacitance technology

Compared to traditional non-destructive testing (NDT) methods, coplanar capacitance technology has advantages in intuitive identification and accurate location for identifying and locating the various failures in asphalt materials. At present, research on coplanar capacitance technology for various failure detection and location in asphalt materials is missing. Therefore, this paper aims to develop a various failure detection and location method in asphalt materials using coplanar capacitance technology. Firstly, the capacitance distribution measurement on the single-pair electrode capacitance sensor with constant area (CSCS) is utilized to locate the various failures of asphalt materials. Then, the various failure depth inside asphalt materials can be predicted using a standard curve of the various failure depth-maximum change capacitance. Finally, based on the single-pair electrode capacitance sensor with variable area (VSCS), the spatially resolved capacitance measurement determines the various failure shape details of asphalt materials. It is found that the Ultrasonic Tomography Technology (UTT) method can only identify internal various failures in asphalt materials and is unable to determine the location of various failures. Nevertheless, the capacitance distribution measurement can identify air void and water intrusion various failures in asphalt materials. And the relative error of the measured location results inside asphalt materials is within 7. The predicted curves of various failure depths in asphalt materials have a coefficient of determination (R2) of greater than 0.984. The maximum change capacitance can be utilized to estimate the various failure depth in asphalt materials. The UTT method makes it difficult to determine the shapes and sizes of various failures in asphalt materials. But the spatially resolved capacitance measurement can determine the various failure shape details of asphalt materials. And the errors in the reconstructed results with failure shape details inside asphalt materials are less than 11%. The findings of this investigation can help engineers realize intuitive identification and precise location of invisible failures in shallow asphalt layers in bridge deck asphalt pavement using coplanar capacitance technology.

APPLICATION OF ACOUSTIC EMISSION METHOD FOR DETECTING DIFFUSION INTERLAYERS IN DIFFERENT WELDED JOINTS

The paper investigates the possibility of detecting specific structural defects of dissimilar welded joints by the method of acoustic emission (AE). Carbide and decarburized interlayers are formed in dissimilar welded joints of austenitic and pearlitic steels during welding and subsequent operation. Diffusion interlayers, as a structural defect, are usually small in size and cannot be detected by traditional non-destructive testing (NDT) methods, while the AE method is promising for detecting such defects. In the present work, we studied the AE data obtained during testing of defect-free specimens of a welded joint and specimens with diffusion interlayers. It has been established that the AE parameters for defective and defect-free welded joints have significant differences, which makes it possible to identify the presence of diffusion interlayers in dissimilar welded joints.

An Inspection Technique for Steel Pipes Wall Condition Using Ultrasonic Guided Helical Waves and a Limited Number of Transducers.

This research utilizes Ultrasonic Guided Waves (UGW) to inspect corrosion-type defects in steel pipe walls, providing a solution for hard-to-reach areas typically inaccessible by traditional non-destructive testing (NDT) methods. Fundamental helical UGW modes are used, allowing the detection of defects anywhere on the pipe's circumference using a limited number of transducers and measurements on the upper side of the pipe. Finite element (FE) modeling and experiments investigated generating and receiving UGW helical waves and their propagation through varying corrosion-type defects. Defect detection is based on phase delay differences in the helical wave's signal amplitude peaks between defective and defect-free regions. Phase delay variations were noted for the different depths and spatial dimensions of the defects. These results highlight the phase delay method's potential for NDT pipeline inspection.

Real-Time Defect Detection Scheme Based on Deep Learning for Laser Welding System

Laser welding, as an important material processing technology, has been widely used in various fields of industry. In most industrial welding production and processing, high precision is required for welding parameters and fixed work pieces. However, in the process of laser welding, serious heat transfer effect will bring unpredictable welding deviations, and even a small deviation will lead to serious welding defects, which will affect the quality of the welded products. Traditional non-destructive testing methods have been widely used, but they have been proved to have some limitations. Existing laser welding defect detection schemes are mainly focused on the detection of post-weld defects, which requires a large amount of data, and the real-time detection cannot be guaranteed. In this paper, we propose a data acquisition system for collecting changes in physical characteristics during laser welding with the aids of multiple sensors. Based on the data originating from sensors’ system, an efficient laser welding defect detection model has been designed and investigated based on the MSCNN (multi-scale convolutional neural network), BiLSTM (bidirectional long short-term memory), and AM (attention mechanism). The final proposed MSCNN-BiLSTM-AM fusion detection model can achieve 99.38% detection accuracy, which make the laser welding system more efficient and more suitable.

A thickness reduction testing method for ferromagnetic materials based on variable intensity DC magnetization

The assessments of internal thickness reduction of ferromagnetic components are extremely important. Traditional nondestructive testing (NDT) methods are usually non-linear and sometimes the electromagnetic parameters of components are required. In this paper, a thickness reduction testing method based on variable intensity DC magnetization (VIDC-MAG) is proposed. Both theoretical modeling analysis and simulation modeling analysis show that a pole point of magnetic permeability μm appears with the increase of DC magnetization intensity, which corresponds to an inflection point magnetizing current im. The im decreases with the increase of the defect depth of the wall thickness reduction and shows a linear relationship. The experimental results show that the method performs well for the inner wall thickness thinning and the relative error of the measurement results is small. Additionally, the measurement results are related to the width of the thickness reduction, the probe excitation frequency, the magnetic properties of ferromagnetic material and the lift-off distance of the magnetizer. The method is applicable to the thickness variation of the cover layer and does not need to know the electromagnetic parameters of the components in advance, which has greater practical application value.

Thermal nondestructive testing: traditional approaches and novel trends (review)

In the last years, thermal testing has shown up as a quickly growing nondestructive evaluation technique. This is explained by wide implementation of composite materials in many industrial spheres, first of all, aviation and aerospace. Inspection of composites by means of traditional nondestructive testing (NDT) methods encounters some difficulties, which can successfully be overcome by using thermal NDT. Moreover, an overwhelming progress in development and commercialization of infrared imagers also took place in the last two decades. On the basis of earlier reviews in the field of thermal NDT, the author has attempted to summarize recent achievements in this field, including inspection methodologies and data processing, as well as development of thermal NDT equipment and applications of this technique. The review describes both national achievements and international approaches, which have been lately proposed in thermal NDT thus making this technique a very promising in NDT of some classes of materials and constructions.

Evaluation of material surface properties using one-way collinear mixing Rayleigh waves

Nonlinear Rayleigh waves are useful for material surface damage detection. Traditional non-destructive testing (NDT) techniques based on higher harmonics of Rayleigh waves are inevitably interfered with by the nonlinearity of measurement systems. To overcome this disadvantage, an approach based on one-way collinear mixing Rayleigh waves (MRWs) is proposed to evaluate the material surface properties. A wedge with two different incidence angles for the excitations of transverse and longitudinal waves is designed to generate one-way collinear MRWs. The one-way collinear MRWs can be generated on the whole surface of the material, and propagate along the propagation way under one excitation. Numerical simulations of one-way collinear MRWs under different surface properties, which are characterized using different third-order elastic constants, are studied on a semi-infinite aluminum. Experiments are also conducted on the aluminum specimen, whose surface is corroded by hydrochloric acid so as to change the material surface properties. Both numerical and experimental results show that the one-way collinear MRWs are generated effectively, and the defined relative acoustic nonlinearity parameter increases with the propagation distance as well as the severity of the material surface damages. The results verify the effectiveness of the proposed MRW-based method and prove that it would be a promising NDT technique for the accurate evaluation of material surface properties.