Nondestructive Evaluation Methods Research Articles

Accurate acoustic classification research of visually similar monochrome porcelain fragments

AbstractExploring a non-destructive and rapid evaluation method for precious ancient ceramic relics is of significant importance. Currently, there are countless monochrome porcelain fragments awaiting measurement and categorization. Various instruments such as XRF, XRD, SEM, OM, and thermoluminescence dating have been extensively utilized by numerous researchers to study ancient ceramics. However, these techniques pose challenges in reliably identifying monochrome porcelain fragments from the same kiln with similar appearance, content, and microstructure due to their limitations. To address this issue, this study presents an acoustic measurement system that utilizes audible frequencies to non-destructively evaluate monochrome porcelain fragments. The proposed method enables the extraction of parameters related to time domain analysis (e.g., group delay), frequency domain analysis (e.g., resonance), and sound loss characteristics of these fragments. This non-destructive and efficient technology for detecting acoustic characteristics of monochrome porcelain fragments presented in this work clarifies the fundamental principles governing the interaction between sound waves and ancient ceramic fragments while providing a completely non-destructive and highly efficient method for classifying and restoring valuable solid cultural heritages like stone, jade, bronze etc. Moreover, this approach can also be applied for non-destructive testing of elastic modulus in advanced ceramic devices including detecting small cracks, deterioration effects due to aging as well as other defects.

Line Scanning Thermography for Rail Base Defect Detection

Rail base defects present significant maintenance challenges within the railway industry, as they are difficult to detect before they lead to failure. While various in-motion nondestructive evaluation (NDE) methods exist for inspecting rail, none of the existing NDE methods can inspect the rail base area reliably and efficiently. This paper presents finite element analysis (FEA) and experimental results applying a line scanning thermography (LST) approach developed for rail base area inspection. This work demonstrates the LST approach for dynamic inspection of thick steel components containing surface and subsurface anomalies. Initially, FEA was used to help design the experimental setup, then to compare trends in surface temperature variations with those from experimental trials. Through motorized testing, test specimens were moved through a region heated by three stationary line heaters, utilizing LST to capture temperature variations on the surface of each sample. The results have shown that subsurface features 6.3 mm in diameter and with aspect ratios greater than 1.5 were detectable in all specimens. This is a promising development that warrants further study.

Influence of a suboptimal environment and sintering temperature on the mechanical properties of fused filament fabricated copper

Metal injection molding (MIM) processes are generally more cost-effective for the generation of metallic AM components. However, the thermal processing required to remove the polymer and sinter the metal powder is not well understood in terms of resulting mechanical response and damage evolution, especially in ambient atmospheres where contamination is present. This study aims to provide a range of achievable mechanical properties of copper produced using a MIM-based method called fused filament fabrication (FFF) that is post-processed in a nonideal environment. These results showed direct correlations between sintering temperature to multiple aspects of material behavior. In addition, Nondestructive Evaluation (NDE) methods are leveraged to understand the variation in damage evolution that results from the processing, and it is shown that the higher sintering temperatures provided more desirable tensile properties for strength-based applications. Moreover, these results demonstrate a potential to tailor mechanical properties of FFF manufactured copper for a specific application.

Characterization of adhesion within structural materials by means of guided elastic waves

Assembly methods in industry have improved considerably during last decades and mechanical fastening is increasingly abandoned in favor of structural bonding and/or welding methods. The need for improved nondestructive testing and evaluation (NDT&E) methods becomes therefore important particularly for thermoplastic composites welding. Indeed, the latter welding process generates subtle defects (Kissing Bonds) that can hardly be detected, hence the need to develop specific well-calibrated techniques. In addition to bonding testing which is based on the existing contrast between high and weak bonded areas, it remains necessary to determine the adhesion quality, particularly in the case of multi-layered materials. In this contribution a nondestructive quantification method of the adhesion strength corresponding to welded assemblies is presented. The work is based on the use of an ultrasonic contactless experimental device based on air-coupled ultrasound propagating within welded composites. The work takes advantage of the existing interaction between the generated guided waves and the composite structure along the propagating distance in order to locate defects and evaluate the adhesion of the tested thermoplastic welded assemblies. This rely on the basis of an inverse method based on the dispersion curves of Lamb waves, which revealed to be sensitive to the deterioration in adhesion quality.

Ultrasonic Rough Crack Characterization Using Time-of-Flight Diffraction With Self-Attention Neural Network.

Time-of-flight diffraction (ToFD) is a widely used ultrasonic nondestructive evaluation (NDE) method for locating and characterizing rough defects, with high accuracy in sizing smooth cracks. However, naturally grown defects often have irregular surfaces, complicating the received tip diffraction waves and affecting the accuracy of defect characterization. This article proposes a self-attention (SA) deep learning method to interpret the ToFD A-scan signals for sizing rough defects. A high-fidelity finite-element (FE) simulation software Pogo is used to generate the synthetic datasets for training and testing the deep learning model. Besides, the transfer learning (TL) method is used to fine-tune the deep learning model trained by the Gaussian rough defects to boost the performance of characterizing realistic thermal fatigue rough defects. An ultrasonic experiment using 2-D rough crack samples made by additive manufacturing is conducted to validate the performance of the developed deep learning model. To demonstrate the accuracy of the proposed method, the crack characterization results are compared with those obtained using the conventional Hilbert peak-to-peak sizing method. The results indicate that the deep learning method achieves significantly reduced uncertainty and error in rough defect characterization, in comparison with traditional sizing approaches used in ToFD measurements.

Array infrared thermography for visualization of defects in bonded fiber reinforced polymer joints

Fiber reinforced polymer (FRP) is widely used in new and existing structures, however, interfacial defects in the bonded joints pose a significant threat to structural integrity. Therefore, detection of interfacial defects is imperative for ensuring structural safety. This study proposes array infrared thermography (IRT) as a novel non-destructive evaluation method to visualize interfacial defects. Array IRT provides uniform heat excitation within the spatial domain, which overcomes the problem of heat concentration by conventional IRT. Forty-five bonded FRP plate specimens were tested using array IRT, results of which show that interfacial defects can be accurately detected within (8h+8) s (where h is the thickness of the upper layer of FRP in mm). Array IRT achieves high accuracy in detecting shapes, particularly sharp corners of defects. A pre-processing method was proposed to eliminate the twisted angles of thermal camera and to more clearly show the defects in the thermograms. A database containing tested thermograms and the corresponding predefined defects was established. Intelligent algorithms - UNet, Deeplabv3, and YOLOv8 - were used to segment the defected regions for array IRT analysis, results of which show a precision of 95.8%, 94.4%, and 94.1%, respectively.

Non-destructive ultrasonic testing for identifying concealed weapons and analyzing internal chemical substances

The management of unknown weapons obtained from warfare, military operations, and terrorist situations requires a series of nondestructive testing processes. This management process, which is aimed at collecting information on the nature of the weapons, encompasses identification, processing, transportation, and storage of the obtained weapons. A lack of identification marks in acquired ammunition may indicate the potential presence of a chemical weapon. Therefore, nondestructive evaluation methods have been employed to identify unknown weapons. However, these methods rely on neutron- and radiation-based analyses, which require radiation-handling experts and specific protective facilities. Consequently, their application in this field is challenging. Herein, a nondestructive ultrasonic testing method, which can identify substances inside unknown weapons and optimize the verifiability of the state of the ammunition, was employed as an alternative method to overcome these limitations. The experimental system enabled quantitative and qualitative analyses of the internal chemicals by analyzing the time difference in acquiring the signals, particularly when the results indicate that the internal substance is a liquid. The results of this study can serve as a basis for advancing the application of ultrasonic testing as a viable and efficient approach for managing unknown weapons.

A novel stone fruit quality non-destructive evaluation method based on PVR by LF-NMRI

For stone fruits, the pulp ratio, indicating the proportion of pulp to the total fruit, serves as a vital metric in assessing stone fruit quality. Traditionally, this ratio is computed by measuring the mass of each component through destructive sampling, and acquired results remain controversial. In order to obtain this ratio non-destructively and more accurately, a novel evaluation method for stone fruit quality based on pulp volume ratio (PVR) by low-field nuclear magnetic resonance imaging (LF-NMRI) is proposed. This approach integrates the SwinUnet segmentation network to differentiating the fruit pulp and core structure, thereby precisely computing the volume proportion of pulp. Experimental results reveal that SwinUnet exhibits better accuracy and robustness in the LF-NMRI data compared to the other networks. Based on the segmentation results, the disparities between the PVR values computed using the ellipse fitting method and the integral method, compared to the ground truth (GT), are less than 1% and 0.5% respectively. This paper provides a new reference for selecting premium stone fruits and enriches the fruit quality evaluation system.

Application of photothermal beam deflection spectrometry for non-destructive evaluation of advanced materials: a state-of-the-art review

Abstract The progress made in lasers and data acquisition systems has paved the way for innovative non-destructive evaluation methods based on the photothermal phenomenon. Beam deflection spectrometry (BDS) is a photothermal spectroscopic technique that offers ultra-sensitivity, high signal-to-noise ratios, and reduced sample preparation requirements. These advantages provide precise assessment of advanced material (AM) properties. This review presents a comprehensive in-depth analysis, thus helping researchers to understand the potential and future perspectives of BDS along with the theory, instrumentation, and application supported by the literature data. The objective of this review is also to present the possibilities of BDS in the characterization of AMs, including organic, inorganic, and hybrid organic–inorganic materials in the form of thin films, coatings, composites, and nanomaterials.

A state-of-the-art assessment in developing advanced concrete materials for airport pavements with improved performance and durability

Concrete materials, as the main component of airport pavement, provide essential attributes such as strength and toughness required by airport pavement. With the rapid development of air transportation, the compression and cracking resistance performances required by airport pavement are dramatically boosted with the ever-growing weight and the frequency of aircraft. In recent years, high-performance concrete materials with excellent properties have shown great potential in improving durability and extending the service life of pavement. This review presents a-state-of-the-art assessment guided by the pavement performance requirement, material design mechanism, application cases and future application guidance of advanced cementing materials in airport pavement improvement. Firstly, the functions of each structural layer of airport pavement and their effects on the surface layer are summarized. Then, the design mechanism and application cases of advanced concrete materials were summarized with the future application guidance to be given. Finally, the application scenarios, testing mechanism and testing effect of nondestructive testing methods for airport pavement performance evaluation are summarized. This review provides a comprehensive assessment of the development and applications of advanced concrete materials for airport pavement, paving avenue to achieve high performance and long service life of airport pavement.

Subsurface defect detection of high-temperatured components with laser induced surface wave

Abstract Subsurface defects are detrimental to the safety and integrity of critical components in various fields, particularly for those used in high-temperature environments. For this reason, a reliable non-destructive evaluation (NDE) method for in-situ inspection of subsurface defects of high-temperatured components is highly desired. Laser ultrasonic technique offers a promising potential to accommodate the demands in virtue of non-contact generation and detection of ultrasonic waves. In this work, laser ultrasosnic inspection of subsurface defects in high-temperatured components is proposed, which exploits the modal conversion of surface longitudinal wave to Rayleigh wave. The modal conversion was firstly investigated with finite element simulation, from which the phenomenon of modal conversion can be readily identified. Thereafter, an experimental setup is built to verify the feasibility and effectiveness of proposed method. Notably, a delay-and-sum method based on a scanning procedure is conducted to coherently increase the detection sensitivity of subsurface defects, since the surface longitudinal wave rapidly vanishes with propagation distance., and the temperature-dependant velocity variation can be adaptively compensated. This work provides a viable route for in-situ inspection of subsurface defects in high-temperatured components where conventional ultrasonic method fails, and it would find potential applications in broad fields, such as coating quality evaluation in fabrication, bearing assessment under load, and in-situ monitoring for additive manufacturing.

Development of a wireless multichannel miniature impedance measurement system and its application for bolt loosening detection

The piezoelectric impedance-based technique is always regarded as one of the most promising structural health monitoring and nondestructive evaluation methods. In recent years, impedance measurement chip AD5933 with the characteristics of high integration and cost-effectiveness makes it possible to address the huge and high-cost problems of the commercialized impedance measurement instrument during the process of structural health monitoring and defect identification. However, it still faces lots of challenges for the chips to be utilized in practical applications due to several limitations, such as short distance for data transmission, single measurement channel and artificial attendant requirement. In this paper, a wireless multichannel miniature impedance measurement system composed by the front-end measurement device and the remote measurement and control platform on the server, is firstly developed and presented with the functions of wireless data transmission, multi-channel acquisition and remote data post-processing. Subsequently, the design concept and composition of the system were introduced in detail. Then, a series of piezoelectric transducers related tests were conducted to validate its impedance measurement performance, especially when comparing with the ones measured by commercialized instruments. In addition, to verify its effectiveness and feasibility of the developed system for the structural damage detection, a bolt loosening detection experiment on the flange connection of a pipeline specimen was investigated for its damage localization and severity quantification. Finally, all the results demonstrated that the developed system provides a great possibility to be used as a convenient and portable impedance measurement tool for the civil structural health monitoring and damage identification in practical applications.

Optimizing Building Rehabilitation through Nondestructive Evaluation of Fire-Damaged Steel-Fiber-Reinforced Concrete.

Fire incidents pose significant threats to the structural integrity of reinforced concrete buildings, often necessitating comprehensive rehabilitation to restore safety and functionality. Effective rehabilitation of fire-damaged structures relies heavily on accurate damage assessment, which can be challenging with traditional invasive methods. This paper explores the impact of severe damage due to fire exposure on the mechanical behavior of steel-fiber-reinforced concrete (SFRC) using nondestructive evaluation (NDE) techniques. After being exposed to direct fire, the SFRC specimens are subjected to fracture testing to assess their mechanical properties. NDE techniques, specifically acoustic emission (AE) and ultrasonic pulse velocity (UPV), are employed to assess fire-induced damage. The primary aim of this study is to reveal that AE parameters-such as amplitude, cumulative hits, and energy-are strongly correlated with mechanical properties and damage of SFRC due to fire. Additionally, AE monitoring is employed to assess structural integrity throughout the loading application. The distribution of AE hits and the changes in specific AE parameters throughout the loading can serve as valuable indicators for differentiating between healthy and thermally damaged concrete. Compared to the well-established relationship between UPV and strength in bending and compression, the sensitivity of AE to fracture events shows its potential for in situ application, providing new characterization capabilities for evaluating the post-fire mechanical performance of SFRC. The test results of this study reveal the ability of the examined NDE methods to establish the optimum rehabilitation procedure to restore the capacity of the fire-damaged SFRC structural members.

Combined non-destructive evaluation of the damage precursors in fused silica optics

Combined non-destructive evaluation methods for fused silica’s damage precursors including UV photothermal weak absorption, ultrafast fluorescence, steady-state fluorescence, Raman spectroscopy, and stress distribution, are proposed to explain the damage mechanism from multiple perspectives. The correlation between each non-destructive characterization method and damage behaviors is simultaneously verified, in general, the damage threshold test of R on 1. The results have shown that regions with high photothermal absorption are accompanied by low damage thresholds, because the photothermal conversion process is associated with laser-induced damage. The damage threshold is also lower at locations of bright ultrafast fluorescence signals with a lifetime ranging from 1.2∼3 ns, as ultrafast fluorescence is associated with high densities of defects. A complementary relationship between the distribution of ultrafast fluorescence and the distribution of photothermal absorption signals is constructed. This indicates that the steady-state fluorescence cannot accurately evaluate the precursor’s damage performance. Furthermore, Raman spectroscopy is utilized to identify the structural changes in bond lengths and angles, as well as locations of damages coinciding with stress distribution.

Compression–compression fatigue damage of wrinkled carbon/glass hybrid composite laminates

Compression–compression fatigue tests were carried out to study the compressive fatigue damage mechanisms of a carbon/glass hybrid composite laminate with a manufacturing-induced wrinkle defect. As reported in literature, thin wrinkled composite laminates could show sensibly different fatigue behaviours and damage mechanisms in the presence of tension and compression cyclic loadings. The sensitivities of composites to tension and compression cyclic loadings were not the same. In this study, the damage modes and cracking sequence of a thick wrinkled laminate were identified by the strain fields from digital image correlation (DIC). Acoustic emission (AE) and infrared (IR) thermography were used to detect the damages and explore the potential capabilities of non-destructive evaluation methods when applied to detection of fatigue induced damages in composite structures under operational cyclic loadings. Two primary damage modes were identified and detected before the final failure of the laminate i.e. debonding between the resin-rich layer and the laminate and interlaminar cracks. Debonding occurred earlier than interlaminar cracking. Out-of-plane stresses due to fibre waviness drove the initiation of these damages. Under the settings of AE in this work, AE captured early debonding activities but did not detect the micro damage accumulating before the formation of interlaminar crack. Interlaminar crack initiation was tracked by IR thermography at cross-section of the specimen based on the distinct temperature localisations that occur with this micro damage. No distinct temperature localisations occurred during the formation of debonding as it is mode-I driving under compression–compression cyclic loading, so IR thermography did not detect the debonding crack. The results of this study show the potential of using complementary non-destructive damage evaluation methods to inspect early damages in composite structures. The early detection of damages would give early warning signs before the damages become critical. It is found that AE and IR thermography should be used as complementary tools to detect different damage modes.

Non-destructive inspection method for egg freshness evaluation via low-field nuclear magnetic resonance technology

Non-destructive inspection method for egg freshness evaluation via low-field nuclear magnetic resonance technology

FTnet: An integrated network for fusing multi-modal NDE data of lightning damage in aircraft composite materials

Lightning strikes pose a significant challenge for aircraft and wind turbine blades with Carbon Fiber Reinforced Polymer (CFRP) structures, requiring reliable damage detection techniques. Non-destructive evaluation (NDE) methods, including X-ray and Ultrasonic Testing, are effective in identifying material damage in aircraft. However, X-ray requires access to both sides of the structure, and UT requires a coupling medium between the transducer and the structure, as well as a relatively smooth surface, making both methods less feasible for routine aircraft maintenance. Other NDE techniques, such as eddy current testing and infrared thermography, can detect damage on the side struck by lightning but lack the precision needed for a comprehensive assessment. To address these challenges, this paper introduces a two-stage Fusion-Translation network (FTnet), which integrates NDE 4.0 innovations, including data fusion and advanced imaging algorithms, to optimize the NDE process. By integrating infrared and eddy current data, FTnet characterizes lightning-induced damage with enhanced depth and contour detail, demonstrating superior performance over existing methods in both qualitative and quantitative evaluations. The implementation of FTnet marks an advancement in NDE 4.0, potentially enhancing aircraft safety and streamline maintenance protocols by providing a more reliable and comprehensive assessment of lightning strike damage.

Revealing Subsurface Damage Morphology and Patterns in areal Ultrashort Pulse Laser Machining of Glass

Material removal rates as well as surface and subsurface quality are key aspects for the industrial application of ultrashort pulse (USP) laser machining. However, revealing so-called subsurface damage (SSD) is challenging. The presented study visualizes and quantifies subsurface damage patterns in areal USP laser ablation of fused silica (FS) and glass N-BK7 (BK). For the first time, using high-resolution optical coherence tomography (OCT) as non-destructive and three-dimensional (3D) evaluation method, SSD morphologies of areal laser machining induced damages are analysed. Influences of laser wavelength, beam geometry and processed material are investigated. Discovered differences of damage morphologies and depth in FS and BK point out the relevance of selecting suitable process parameters. Based on the evaluation of volumetric OCT data, the authors were able to quantify damage morphologies using the surface texture ratio as well as power spectral density functions. One important finding for the quantification and comparability of damage depths in USP laser processing is the influence of applicable evaluation thresholds. In comparison to area thresholds of 0.001% being applicable to OCT measurements, more lenient thresholds of e.g. 1% commonly applied in destructive SSD measurement methods in average result in a reduction of measured damage depths by a factor of ~ 2. This potentially leads to an underestimation of damage depths depending on methods on thresholds used. The presented measurement and evaluation methods as well as gained process insights are important assets for the future optimization of low-damage USP laser micromachining of brittle materials. Moreover, the general applicability and relevance of OCT-based morphological damage analysis in laser material processing is shown.

Towards 3D Pore Structure of Porous Gypsum Cement Pozzolan Ternary Binder by Micro-Computed Tomography

A sophisticated characterisation of a porous material structure has been challenging in material science. Three-dimensional (3D) structure analysis allows the evaluation of a material’s homogeneity, pore size distribution and pore wall properties. Micro-computed tomography (micro-CT) offers a non-destructive test method for material evaluation. This paper characterises a novel ternary binder’s porous structure using micro-CT. Gypsum–cement–pozzolan (GCP) ternary binders are low-carbon footprint binders. Both natural and industrial gypsum were evaluated as a major components of GCP binders. Porous GCP binder was obtained by a foaming admixture, and the bulk density of the material characterised ranged from 387 to 700 kg/m3. Micro-CT results indicate that pores in the range from 0.017 to 3.0 mm can be effectively detected and described for porous GCP binders. The GCP binder structure proved to be dominant by 0.1 to 0.2 mm micropores. For GCP binders produced with natural gypsum, macropores from 2.2 to 2.9 mm are formed, while GCP binders with phosphogypsum possess pores from 0.2 to 0.6 mm. Micro-CT proved to be an effective instrument for characterising the homogeneity and hierarchical pore structure of porous ternary binders.

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.