Non-destructive Testing Techniques Research Articles

Grouting inspection of PC bridge girder using Ultrasonic Pulse Echo Tomography and multiparameter evaluation procedure

Assessing the condition of grouting in post-tensioned tendons within Prestressed Concrete (PC) structures stands as a challenge in the field of civil engineering infrastructure maintenance. Current non-destructive testing (NDT) techniques employed for grouting assessment have certain constraints. For instance, the Impact-Echo method exhibits relatively low accuracy, while the X-Ray method proves to be costly and demands significant time and labor resources. Simultaneously, there exist promising alternative NDT techniques that offer potential for grouting inspection. In this study the authors used commercially available Ultrasonic Pulse Echo Tomography device with an array of dry-contact sensors and multiparameter method of evaluation. The evaluation method uses amplitude of ultrasonic pulse reflected from PC sheath, backwall and phase of reflected ultrasonic pulse. The experimental study on large-scale specimens confirmed that the evaluation method allows to achieve higher accuracy, as compared to existing methods of evaluation. The developed procedure was applied for inspection of PC bridge girder on an existing road bridge. The inspection was carried out on one of the spans of the bridge, which had 4 I- shaped PC girders. The results of Ultrasonic Pulse Echo Tomography were confirmed by means of drilling and borescope inspection method The results of both methods showed good correlation.

A Deep Learning-Based Ultrasonic Diffraction Data Analysis Method for Accurate Automatic Crack Sizing

The purpose of this paper is to automate the interpretation of data during ultrasonic diffraction using a non-destructive testing (NDT) technique to accurately size defects for assisting in decision-making. A convolutional neural network (CNN) architecture was developed to automatically measure the length of the defect. Using the architecture, the population of A-scan signals in the scanning path was classified. The defect region was extracted and its size in the scanning direction was obtained by the connected region solution algorithm based on the classification results. The arrival time of diffraction waves was accurately identified by the intelligent denoising framework proposed, combined with Hilbert transform, and then the height of defects was calculated by corresponding geometric relations. The estimation results demonstrate that the measurement method can be considered as a useful technique for crack sizing in industrial structures, even in the case of complex noise.

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

AbstractAcoustic emission is a nondestructive testing (NDT) technique, widely used to monitor the condition of structures for safety reasons especially in real time. The method utilizes the electrical signals generated by the elastic waves in a material under load to detect and locate damage in structures. However, identifying the sources of AE signals in concrete or composite materials can be challenging due to the anisotropic properties of materials and interpreting a large amount of AE data, leading to data misinterpretation and inaccurate detection of damage. Hence, the need for filtering out noise-induced signals from recorded data and emphasizing the actual AE source is crucial for monitoring and source localization of damage in real time. This study proposed a one-dimensional convolutional neural network (1D-CNN) deep learning approach to filter around 22,000 AE data in a reinforced concrete (RC) beam. The model utilizes significant AE parameters identified through neighborhood component analysis (NCA) to classify true AE signals from noise-induced signals. By using the optimized network parameters, a high classification accuracy of 97% and 96.29% was achieved during the training and testing phases, respectively. To check the reliability of the proposed AE filtering model in the real world, it was evaluated and verified using source location AE activities collected during a four-point bending test on a shear-deficient beam. The outcomes suggest that the proposed AE filtration model has the potential to accurately classify AE signals with an accuracy of 92.8% and proved that the filtration model provides accurate and valuable insight into source location determination.

Terahertz Nondestructive Measurement of Heat Radiation Performance of Thermal Barrier Coatings Based on Hybrid Artificial Neural Network

Effective control of the micro- and nanostructure of thermal barrier coatings is essential to enhance the thermal radiation performance of the coating, which helps to determine the remaining service life of the coating. This paper proposed a method to measure the radiation properties of thermal barrier coatings by terahertz nondestructive testing technique, using APS-prepared thermal barrier coatings as the object of study. Radiative properties were a comprehensive set of properties characterized by the diffuse reflectance, transmittance, and absorptance of the thermal barrier coating. The coating data in actual service were obtained by scanning electron microscopy and metallographic experiments, and the data were used as the simulation model critical value. The terahertz time-domain simulation data of coatings with different microstructural features were obtained using the finite-different time-domain (FDTD) method. In simulating the real test signals, white noise with a signal-to-noise ratio of 20 dB was added, and fast Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet transform (WT) were used to reduce the noise and compare their noise reduction effects. Different machine learning methods were used to build the model, including support vector machine algorithm (SVM) and k-nearest neighbor algorithm (KNN). The principal component algorithm (PCA) was used to reduce the dimensionality of terahertz time-domain data, and the SVM algorithm and KNN algorithm were optimized using the particle swarm optimization algorithm (PSO) and the ant colony optimization algorithm (ACO), respectively, to improve the robustness of the system. The K-fold cross-validation method was used to construct the model to improve the adaptability of the model. It could be clearly seen that the novel hybrid PCA-ACO-SVM model had superior prediction performance. Finally, this work proposed a novel, convenient, nondestructive, online, safe and highly accurate method for measuring the radiation performance of thermal barrier coatings, which could be used for the judgment of the service life of thermal barrier coatings.

Barkhausen Noise as A Magnetic Nondestructive Testing Technique

Magnetic Barkhausen Noise (MBN) is a magnetic-based non-destructive electromagnetic testing method. Due to the electromagnetic working principle of MBN, it can be used for ferromagnetic materials, which consist of small magnetic fields discredited by domain walls and oriented in various directions. In an external magnetic field application, the fields turn to the magnetic direction, and the domain walls move and cause magnetic flux jumps. The jumps are named Barkhausen Noise (BN). The domain wall movements are sometimes pushed down by microstructure, composition, and defects. As the magnetic domain walls break away from the pinning sites produce MBN signal. MBN can be used for different material properties such as microstructure, composition, residual stress, and hardness. The paper's purpose is to analyze MBN as an improved NDT, clarify the relationship between material properties and MBN profile, and introduce MBN's applications and test equipment of MBN.

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review.

Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection.

Damage to monumental masonry buildings in Hatay and Osmaniye following the 2023 Turkey earthquake sequence: The role of wall geometry, construction quality, and material properties

Damage to monumental masonry buildings in Hatay and Osmaniye following the 2023 Turkey earthquake sequence: The role of wall geometry, construction quality, and material properties

Optimizing focal resolution imaging in Lamb wave time-reversal through periodic plate-column structures: Simulation and experimental study

Ultrasonic guided wave non-destructive testing technology, as one of the most widely used techniques in non-destructive testing, has encountered challenges in breaking the diffraction limit to achieve finer super-resolution imaging during its development. As a type of wave accompanying ultrasonic guided wave damage detection, the transient wave has had the potential to achieve super-resolution imaging in recent years due to its high wave number and sub-wavelength characteristics. In order to overcome its spatial attenuation characteristics, this study constructs a periodic plate-column structure metamaterial. By utilizing its resonance with sub-wavelength Bloch modes at specific frequencies and the interaction with transient waves carrying sub-wavelength information, the wave carrying sub-wavelength signals is amplified, transformed, and transmitted to the far field, thereby achieving super-resolution imaging. The dispersion characteristics of the periodic plate-column structure were simulated and experimentally investigated. The super-resolution imaging effect of the periodic plate-column structure metamaterial was simulated using the time-reversal imaging method and experimentally validated, providing a basis for achieving damage super-resolution imaging with periodic plate-column structures.

Rail Flaw Detection via Kolmogorov Entropy of Chaotic Oscillator Based on Ultrasonic Guided Waves.

Ultrasonic guided wave (UGW) inspection is an emerging non-destructive testing(NDT) technique for rail flaw detection, where weak UGW signals under strong noise backgrounds are difficult to detect. In this study, a UGW signal identification model based on a chaotic oscillator is established. The approach integrates the UGW response into the critical state of the Duffing system to serve as a disturbance control variable. By evaluating the system's motion state before and after introducing the UGW response, identification of UGW signals can be realized. Thus, the parameters defining the critical state of Duffing oscillators are determined by Ke. Moreover, an electromagnetic transducer was specifically devised to enable unidirectional excitation for UGWs targeted at both the rail base and rail head. Experimental studies showed that the proposed methodology effectively detected and located a 0.46 mm notch at the rail base and a 1.78 mm notch at the rail head. Furthermore, Ke was directly proportional to the notch size, which could be used as a quantitative index to characterize the rail flaw.

Sparse-View Neutron Computed Tomography 3-D Reconstruction via the Fast Gradient Projection Algorithm

Neutron tomography is an efficient nondestructive testing technique. As a complement to X-ray computed tomography, it has been widely used in various fields. Due to the difficulty of obtaining complete neutron projection data in a high-radiation environment and the high noise characteristics of neutron images, it is difficult to reconstruct a high-quality image using the conventional filtered-back projection (FBP) algorithm. Therefore, research on sparse-view reconstruction algorithms in neutron tomography is needed. To improve the quality of neutron three-dimensional reconstructed images, this paper proposes an algorithm that combines the Simultaneous Algebraic Reconstruction Technique (SART) with Fast Gradient Projection (FGP), where the FGP is an algorithm for image denoising and deblurring based on the discrete total variation (TV) minimization model. The algorithm proposed in this paper is compared with other algorithms (FBP, SART, and SART-TV) by simulated experimental data and real neutron experimental data. The experimental results show that the novel algorithm outperforms the other three algorithms in terms of denoising and retaining detailed structural information.

Introducing Unmanned Aerial Vehicles to multi-criteria performance assessment of inspection techniques for port concrete infrastructure

Port infrastructure plays an important role in the economic and social activities of the regions they serve. Pressures induced by the corrosive marine environment and weather conditions, unexpected events associated with climate change, the usually high freight and vehicle loading conditions, the dynamic load of the equipment, and insufficient maintenance and rehabilitation measures that sometimes are not enough to repair an in-service facility, increase the susceptibility of port structures to rapid aging and deterioration. To ensure port infrastructure safety and reliability, and to maintain operational efficiency, port managers are tasked with forming successful inspection schemes for structural condition monitoring. Within this context, identifying the capabilities and prioritizing existing inspection techniques allow for optimizing resources (cost, time, personnel). The issue of performance assessment for commonly used Non-Destructive Testing techniques has received significant research interest. However, state-of-the-art Unmanned Aerial Vehicle techniques have not been previously involved in studies that assess the performance of various inspection techniques. Hence, the present research seeks to introduce Unmanned Aerial Vehicles techniques into multi-criteria performance assessment practices of inspection techniques for port concrete infrastructure. Three Non-Destructive Techniques: a) Infrared Thermography, b) Ground Penetrating Radar, and c) Impact-Echo, are compared to camera-based Unmanned Aerial Vehicle techniques linked to Close Range Photogrammetry applications. A qualitative approach, including a literature review and expert judgment, is applied to acquire data required for Multi-Criteria Decision Analysis practices with the Preference Ranking Organization METHod for Enrichment Evaluation methodology. The ranking output of the applied model is validated with other multi-criteria methods to ensure the robustness of the results. The obtained results for the specific set of criteria will assist decision-makers in selecting the most suitable technique for inspecting surface or sub-surface defects in port concrete infrastructure. Highlights The integration of UAV-driven techniques into current practices for assessing the performance of inspection techniques affects the ranking order The prioritization of the inspection techniques is conducted qualitatively (expert judgment and literature survey) by implementing the PROMETHEE method A camera-based UAV technique improves the practical part of inspections and provides flexibility A camera-based UAV technique is the optimal inspection technique for detecting surface defects, while Impact Echo is preferred for sub-surface defects

Experimental study of a thin thermally grown oxide layer in thermal barrier coatings based on the SWT-BP algorithm and terahertz technology

As a promising nondestructive testing (NDT) technique with a very adaptive physical modeling of wave transmission process, terahertz technology is used for the detection and characterization of nonpolar materials and the evaluation of layered and/or defective structures. THz-TDS can also be used to perform spectroscopic analysis and detect structural defects in thermal barrier coatings (TBCs) of aero-engines. Although it is generally difficult to measure the structure of the thin oxide layer of the thermal barrier coatings whose thickness is generally lower than 30 µm (the current axial resolution of the THz-TDS cannot exceed 30 µm). We were able to complete the detection of the oxide layer within 1–29 µm through simulation by using the SWT-BP algorithm. In this study, the analysis was performed on real-world samples, the fitting degree of the SWT-BP algorithm reached 0.77, and the minimum prediction error was less than 0.1 µm. The paper also put forward some improvement measures about the experimental results.

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.

Machine Learning approaches to damage detection in composite structures combining experimental and simulation domains

Composite materials are widely used across major industries such as the automotive, aerospace and wind power, due to their excellent mechanical properties. A strong effort is thus put into developing innovative damage detection methodologies, for which Non-Destructive Testing (NDT) techniques can play a vital role as advanced measurement methods. One such technique is Laser Doppler Vibrometry which allows to accurately measure high-frequency vibration behavior with dense grids of points, without mass loading the structure. For data analysis, Machine Learning (ML) techniques have achieved high success on a number of structural applications, and can be leveraged to build automated and reliable damage classifiers. In this work, three methodologies have been developed by combining Laser Doppler Vibrometer (LDV) measurements with ML approaches, for the task of detecting damages on a carbon-fiber reinforced polymer (CFRP) plate. Each damage detection methodology requires pre- and post-processing steps, which were optimized with Bayesian Optimization. Principal Component Analysis (PCA) was also explored to reduce the dimensionality of the data, before classification. Moreover, making use of Finite Element Analysis (FEA), simulation data was generated with the ability of characterizing the high-frequency dynamic behavior of ply-based composite plates. The simulation data enriched the damage detection methodologies in a Transfer Learning (TL) framework. Results are presented for each damage detection methodology, alongside with a comparative overview of the advantages and disadvantages of each method.

Analysis of structural integrity through the combination of non-destructive testing techniques in heritage inspections: The study case of San Segundo's hermitage (Ávila, Spain)

The establishment of proper conservation and preservation actions has become one of the major concerns for historic cities. Given that possible damage affecting these buildings can be fast-acting and irreversible, the evaluation of the damage origin must be faced from the application of different prospecting possibilities adapted to the specific conditions of the approach. In this regard, the application of non-destructive testing techniques has become one of the most common practices for the characterization of pathologies in cultural heritage. The present research focuses on the analysis of the structural integrity of the Hermitage of San Segundo (center of Spain) through the application of these prospective methodologies that do not pose any damage to the asset. More specifically, the work combines geophysical and optical techniques (ground-penetrating radar, terrestrial laser scanner and drone with RGB camera, and infrared thermography) to provide a complete characterization of the analyzed building. Results show that the combination of the three non-destructive techniques allows detecting defects, defining their possible causes and providing a solid basis for future decision-making regarding maintenance and restoration actions. Methodological integration has shown that certain pathologies can only be detectable with specific techniques while others have been corroborated through the results obtained with the three testing methodologies.

Nonlinear acoustic response in nanoparticle-dielectric systems and nondestructive assessment of particle agglomeration

Nanoparticle agglomeration reduces the modification effect of nanocomposites, and there is a need to investigate a technique for evaluating the overall particle dispersion within the material, which in turn ensures the modification effect of the material. In this paper, a non-destructive testing technique for nanoparticle dispersion based on nonlinear ultrasonic (NLUS) is proposed to rapidly assess the overall nanoparticle dispersion within a material. An interfacial mismatch model describing different degrees of nanoparticle agglomeration was firstly developed, and then the nonlinear acoustic response of the nanoparticle-dielectric system was quantitatively solved, and the theoretical model was validated in conjunction with NLUS tests. The experimental results show that agglomerated interfaces are the source of the nonlinear response of nanodielectrics and that the microscopically generated nonlinear effects can be equivalently accumulated to the mesoscopic domain, with the error in the theoretically calculated values of the two being less than 5%; The NLUS technique eliminates the need for cumbersome steps such as particle size counting, and its small number of rapid detection results are close to the statistical convergence of the particle size of the 250 particles accumulated in the imaging image of the scanning electron microscope, with an error of between 3% ∼ and 13%. With a single inspection area of 339 mm3, the NLUS technology significantly reduces evaluation time and cost while expanding the inspection area, offering the combined advantages of speed, wide field of view, and non-destructiveness.

Influence of part temperature on in-situ monitoring of powder bed fusion of metals using eddy current testing

AbstractPowder bed fusion of metals (PBF-LB/M) is currently the most widely adopted additive manufacturing technology for the fabrication of metal parts. However, the inconsistent quality of PBF-LB/M-manufactured parts and high costs for part certification are impeding wider industrial adoption. In-situ monitoring technologies are expected to enable process control in order to ensure consistent quality, and to replace some of the post-process inspection steps, therefore, reducing part certification costs. Eddy current testing (ECT) is a standardized nondestructive testing technique, which can be used as an in-situ monitoring technology to measure the part quality during the PBF-LB/M build cycle. However, the process-induced complex temperature fields in PBF-LB/M parts during the build cycle are among the most relevant disturbances due to the temperature dependence of the electrical conductivity. This study investigates the process-induced temperature influence on in-situ monitoring of relative density using ECT. Parts made from AlSi10Mg were manufactured on a PBF-LB/M machine and the build cycle was monitored using ECT and an infrared camera, which was used to extract the part surface temperature right before the ECT measurement. The results demonstrate that the temperature increase of the parts during the build cycle decreases the electrical conductivity independently of the relative part density, which was measured via micro-computed tomography. Therefore, a temperature compensation method was proposed and applied demonstrating that a layer-to-layer difference of 0.15 % relative density can be detected via ECT. Consequently, it has been demonstrated that ECT is an effective in-situ monitoring technology for PBF-LB/M, even in the presence of temperature disparities within parts.

Single-frame temporal phase-shifting shearography for real-time nondestructive testing

Shearography, which is an effective nondestructive testing (NDT) tool, can detect internal defects of composites materials. Temporal phase-shifting (TPS) shearography has a simple setup and high accuracy. It is suitable for quasi-static detection because it requires the acquisition of a sequence of speckle patterns before and after specimen deformation. To achieve dynamic shearographic NDT, this study adopted high-frequency synchronous driving mode of camera and piezoelectric transducer (PZT) phase shifter to generate and acquire phase-shifted patterns. Subsequently, a single frame TPS shearography requiring one phase-shifted pattern in the deformed state was proposed. Finally, using parallel computing technology and efficient approximations of arctangent function, a real-time four-step TPS shearographic NDT technique with high-quality phase imaging was developed. The experimental results demonstrate the effectiveness and robustness of the proposed method. The results show that the proposed method could reach a rate of approximately 24 fps (frames per second) at the spatial resolution of megapixels for defect detecting.

Non-destructive inspection technologies for repair assessment in materials and structures

Aging infrastructure globally faces degradation, posing risks and requiring substantial repair investment. Strategic maintenance practices are crucial for evaluating structural conditions and ensuring sustainability. The growing demands on modern materials and structures necessitate enhanced health monitoring approaches. Shifting from reactive to proactive maintenance methodologies is paramount, due to lower investment while keeping the structural performance at acceptable standards. However, quantitative assurance of repair/reinforcement/retrofit programs or self-healing effect in structures is similarly crucial for the operation of the infrastructure. Non-destructive testing (NDT) techniques, such as ultrasound, acoustic emission, and optical methods, play a vital role in assessing structural health. Through real-world case studies, the effectiveness of repair in addition to damage assessment are evaluated, encouraging a more systematic approach to monitoring structural repair efficacy. The paper intends to address the research gap in monitoring the repair effectiveness in civil structures and materials and provides valuable insights to enhance repair strategies in civil engineering.

Creep strain prediction in power plant material via ANN modelling of nonlinear ultrasonic test results

ABSTRACT Reliable and accurate prediction of the creep life of power plant components is crucial for both economic and safety reasons. Existing prediction models, based on creep test data, can be complex and time-consuming. Nonlinear ultrasonic (NLU) is a widely-accepted non-destructive testing (NDT) technique for evaluating damage progression in crept specimens. The information from NLU measurements alone is insufficient to forecast the life of any component. In real-time applications, intelligent NDT protocols are needed to enable fast and accurate life prediction of such components. A methodology for creep life prediction using artificial neural networks (ANN) has been introduced based on NLU test results of crept P92 steel specimens. The technique involved creep tests of P92 specimens exposed to a temperature of 625⁰C with applied stress ranging from 120MPa to 160MPa, NLU measurements at each step load, and prediction of creep life of the material with a ANN trained with creep strain and NLU test data. The technique involves prediction from previously generated historical data, thus saving both cost and time of conducting continuous experiments. This approach for ANN modeling of NLU data can be considered a reliable, time-saving, and effective technique for assessing creep damage progression in power plant components.