Non-destructive Testing Techniques Research Articles

A Physics-informed Neural Network for Pulsed Thermography- Based Defect Detection

The non-destructive testing technique of pulsed thermography has been widely used in materials to detect defects due to its low cost, large detection area, and rapid implementation. After conducting experiments, data processing is often necessary to reduce the influences of noise and non-uniform backgrounds and highlight defect information. However, during the analysis, physical information is usually ignored. In addition to thermographic data analysis, numerical simulations are also popular for analytical studies based on physical information, but they don’t fully utilize the experimental data. To address this, a new method was proposed using a physics-informed neural network (PINN) for thermographic data processing. PINN combines the prediction capabilities of deep neural networks with physical laws presented as partial differential equations and boundary conditions, allowing for both experimental data and physics information to be utilized in modelling. In pulsed thermography, the heat transfer is governed by Fourier's law of heat conduction in a three-dimensional system. However, there is a lack of temperature measurements in the depth direction. The proposed method solves this problem by using collocation points generated from Latin hypercube sampling. The PINN model provides a good estimation of the backgrounds in the thermograms, and the features of surface/subsurface defects are highlighted by subtracting the estimated backgrounds from the original thermograms. In the case study, the performance of the proposed method was found to be effective.

Development of Non-Destructive Dynamic Characterization Technique for MMCs: Predictions of Mechanical Properties for Al@Al2O3 Composites

In the past several decades, many destructive and non-destructive testing techniques have been developed to evaluate the characteristics of metal matrix composites (MMCs). This research aims to calculate the mechanical properties of the Al@Al2O3 composites by varying alumina nanoparticles (Al2O3 NPs) content using a non-invasive, position sensing detector (PSD) unit-based optical method. The composite was prepared by a powder metallurgy technique, and its characterization was conducted using SEM and XRD to understand its surface morphology and microstructure. The natural frequency and Young’s modulus of the composite were estimated experimentally. Young’s modulus was calculated using this natural frequency. The proposed study shows that Young’s modulus of the composite increases with an increase in Al2O3 NPs content in the composition, irrespective of the testing method. Along with this, natural frequency also increases with the increase in the Al2O3 NPs content. Evaluated properties were compared with the numerical modeling using COMSOL Multiphysics. The experimental and numerical results are equivalent and within the margin of error. This study illustrates the development of an experimental approach for evaluating the mechanical properties of a composite material. This experimental approach can be used whenever sample dimension and space are constrained to evaluate the mechanical behavior of nanomaterials and nanocomposites.

On monitoring fretting fatigue damage in solid railway axles by acoustic emission with unsupervised machine learning and comparison to non-destructive testing techniques

Railway axles are safety-critical components of the rolling stock and the consequences of possible in-service failures can have dramatic effects. Although this element is traditionally designed against such failures, the initiation and propagation of service cracks are still occasionally observed, requiring an effective application of non-destructive testing and structural health monitoring approaches. This paper investigates the application of structural health monitoring by acoustic emission to the case of solid railway axles subject to fretting fatigue damage. A full-scale test was performed on a specimen in which artificial notches were suitably manufactured in order to cause the initiation and evolution of fretting fatigue damage up to the stage of relevant propagating fatigue cracks. During the test, both periodical phased array ultrasonic inspections and continuous acquisition of acoustic emission data have been carried out. Moreover, at the end of the test, the specimen was inspected, analyzed and evaluated by visual inspection and magnetic particles testing, while acoustic emission raw data were post-processed by a special unsupervised machine learning algorithm based on an Artificial Neural Network. It is demonstrated that the proposed methodology is very effective to detect the onset of crack initiation in a non-invasive and safe way.

Technique for measuring the thickness of a non-conductive coatings on a non-magnetic electrically conductive base metals with automatic take account of the influence of the specific electrical conductivity of the base metal

The results of measurements of the thickness of a non-conductive coating on an electrically conductive non-magnetic base metal using the existing amplitude technique of eddy current non-destructive testing are strongly influenced by the specific electrical conductivity of the base metal. To eliminate this problem, it is proposed to use an eddy current probe with amplitude-phase signal processing. Its graduation is carried out using several base metals with different specific electrical conductivity using a coating thickness simulator, the number of graduation points is comparable to the resolution of the thickness gauge. To calculate the thickness of the coating, taking into account the specific electrical conductivity of the base, an algorithm is used to determine the involvement of a point in a polygon.

Physical Coupling Fusion Sensing of MFL-EMAT for Synchronous Surface and Internal Defects Inspection

Non-destructive testing (NDT) techniques are widely used for inspection and evaluation of conductive materials. However, a single physical mechanism sensing cannot simultaneously satisfy multiple inspection requirements. In this paper, we propose a hybrid transducer based on magnetic flux leakage (MFL) and electromagnetic acoustic transducer (EMAT) for synchronous detection of surface and internal defects. In the proposed hybrid transducer, both MFL and EMAT share a common magnetic field. The magnetic circuit characteristics of the MFL and EMAT principles are fully exploited, and the magnetic field provided by a single permanent magnet is used to excite ultrasonic waves that can detect internal defects within the sample. Iron is used to introduce magnetic field into the sample and provide a horizontal magnetic field to detect discontinuities on the near-surface of the sample. The huge frequency difference between the MFL signal and the EMAT signal effectively suppresses interference between the two signals. Simulations and experiments have been undertaken to show that the proposed transducer can overcome the detection limitations associated with the MFL and the dead detection zone of the EMAT, while simultaneously detecting the surface defects, internal defects and bottom thinning defects in the specimen.

Multi-objective optimization-based model calibration of masonry bridges

Multi-objective optimization-based model calibration can be an intermediate solution between the computationally expensive probabilistic approaches and the single-objective optimization strategies that do not allow uncertainty quantification of the obtained solutions. This work addresses the multi-objective model calibration of two historic stone arch bridges using high-fidelity computational FE models. To implement the methodology, a five-step approach is proposed: experimental characterization through non-destructive testing techniques, non-parametric as-built geometric modeling, macro-finite element modeling, sensitivity analysis, and multi-objective optimization. The preferred solution among the Pareto front solutions is selected based on two different classical criteria, and the set of optimal solutions is further statistically analyzed to assess the validity of the identification process. The results show an average frequency error of 0.97 % and 0.70 % and an average MAC of 0.97 and 0.96 for each case study, respectively, thus highlighting the adequacy of the proposed methodology.

Differentiate tensor low rank soft decomposition in thermography defect detection

Composites are prone to defects in manufacture，which are to be evaluated for safety through non-destructive testing (NDT) techniques. Thermal images are acquired for NDT by using Optical pulsed thermography. Defect detection can be performed by proposing defect detection algorithms. However, due to the low resolution of defect contrast, the detection performance of the existing algorithm is still sub-optimal. In this work, a decomposition algorithm by differentiating low-rank tensors is proposed to extract weak defect information from complex thermal pattern disturbances for surface and sub-surface defect detection. The algorithm mines deep insight into the information on the differentiation of different ranks between structures from the results of Tucker decomposition to extract defect features. In particular, a probabilistic tensor model is introduced to correct potential mismatch patterns enhance defect contrast, and suppress noise and light spot interference. To verify the effectiveness and robustness of the proposed algorithm, a variety of complex composite specimens have been used for validation. The experimental results show that the proposed algorithm achieves better performance compared to the state-of-the-art algorithms especially in enhancing the defect contrast and suppressing the light spot in seven common samples. In overall, it can provide on average of approximately 15% F-score and 3 dB SNR improvement for validation.

Generative Deep Learning-Based Thermographic Inspection of Artwork.

Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.

Dislocation detection of gas turbine materials using a nonlinear ultrasound modulation technique

Industrial gas turbines are used for generating electricity or driving other turbomachinery with the continuous development goal of further increasing machine efficiency. This is primarily achieved by raising the pressure ratio generated in the compressor and by increasing the turbine inlet temperature. Consequently, the hot gas components in gas turbines are subjected to extreme loads and the need for non-destructive testing and structural health monitoring techniques is becoming increasingly important to maintain these components. An important indicator for assessing the structural integrity is the determination of the initial plastic deformation.In this paper, a new method for the detection of plasticity was developed, which is based on a nonlinear ultrasonic two-frequency excitation. The one-dimensional wave equation was solved with a two-frequency excitation and combined with the expanded dislocation theory. As a result, various nonlinearity parameters were defined, showing a clear increasing or a decreasing behaviour with increasing plastic strain. This was experimentally proven with flat tensile specimen made of stainless steel and Inconel 718 (metal plates and additively manufactured). The new indicators allow the possibility to efficiently detect the initial plastic deformation in gas turbine components.

Estimation of the static bending modulus of elasticity in glulam elements by ultrasound and modal-updating NDT techniques

Glulam timber is gaining popularity as a structural element due to its cost-effectiveness, strength, and environmental benefits. However, being an organic material, it is susceptible to degradation. Therefore, nondestructive testing (NDT) methods are essential for assessing its structural health. In this study, two NDT approaches, ultrasound-based and modal updating, were used to predict the static bending modulus of elasticity (MOE) in Pinus Sylvestris and Spruce species. The results demonstrated that the modal updating and linear regression models outperformed the ultrasound technique in accurately determining the static MOE. Additionally, considering the influence of knots improved the accuracy of ultrasound predictions.

Topology optimisation for robust design of large composite structures

The manufacturing of composite structures can lead to material defects. Some of these defects, due to their small size, can go undetected by current non-destructive testing techniques; especially in very large and geometrically complex components. Therefore, it is of paramount importance to design composite components to resist defects by accounting for them as early as possible. Topology optimisation is a methodology whereby complex features, such as cracks and debonds, can be accounted for in the design. Therefore, this work proposes a new approach for the design of very large composite structures. This new approach is enabled by a solid shell element formulation with boundary tracking capabilities (through the floating node and the level-set methods) and optimisation capabilities (through design sensitivity analysis of an energy release rate objective function). Furthermore, this work proposes a new algorithm and modelling workflow that has the ability to handle large multi-scale models. The proposed methodology shows advantages as the ability to track the topology explicitly, and the ability to increase defect tolerance of the design. This works shows the new methodology is capable of handling complex structures, such as a multi-scale stringer run-out model from an aircraft wing-box, while a kissing bond defect is present.

Numerical analysis and experimental research on detection of welding defects in pipelines based on magnetic flux leakage

Regular inspection of long-distance oil and gas pipelines plays an important role in ensuring the safe transportation of oil and gas, and inspection on welding defects is an important part of the inspection process. Magnetic flux leakage (MFL) is an electromagnetic non-destructive testing technique which has been commonly utilized to detect welding defects in pipelines. In the present study, Maxwell electromagnetic simulation software was used to carry out numerical study on the welding defects in pipelines, including incomplete penetration and undercut. The Ф406 pipeline with a wall thickness of 7 mm was selected as the study case to establish the numerical model. Setting the life-off value at 1 mm, the distribution of magnetic leakage field was investigated for pipeline without defect, pipeline with incomplete penetration defect and pipeline with undercut defect respectively, the characteristic values describing the depth and width of defects were found. Furthermore, quantified equations which can be used to describe the defect depth were proposed. Finally, experimental research was carried out to validate the effectiveness of the numerical model, and the experimental results showed good consistence with the numerical calculation results. The research results indicate that, it is technically feasible and reliable to diagnose the incomplete penetration and undercut welding defects in pipelines using MFL.

Influence of water film on subsurface defect detection using eddy current pulsed thermography

Eddy current pulsed thermography (ECPT) is an effective non-destructive testing (NDT) technique for detecting flaws in metallic materials. However, due to the low radiation and high reflection properties of metallic materials, as well as the inhomogeneous emission caused by the complex states of the material surface, the detection of subsurface defects becomes difficult. In this paper, the physical mechanisms underlying the interference of the thin water film in ECPT detection are studied. Two static comparative experiments have been carried out on the ferromagnetic plate with several artificial subsurface defects. In the first comparative experiment, a high radiation point has been artificially added near the defects. The experimental results showed that water film could eliminate the influence of inhomogeneous emission. In the second experiments, the effectiveness of water film in improving the detectability of subsurface defects has been demonstrated and the subsurface defects with a maximum buried depth of 0.4 mm can be detected.

Analysis of Non-destructive Testing for Improved Inspection and Maintenance Strategies

A significant trouble for research is making an arrangement for the administration and upkeep of the fabricated legacy. To foster a philosophy for non-destructive testing (NDT) of substantial designs in light of the distinguishing proof of (a) the responsiveness of the NDT methods, (b) the vulnerability of the NDT estimations, and (c) the best mix of NDT strategies, a huge exploratory program was established. To decide how they are utilized in Indian legislative offices, this exploration assesses NDT strategies. The consequences of an overview poll that was dispersed to Divisions of Transportation (Specks) in all Indian states filled in as the establishment for the ends and examinations presented here. The survey was processed by a total of 28 state agencies, and the main results collected from the states are shown and thoroughly discussed in this report. Additionally, inspection-hard to-find bridge flaws and ongoing research into developing innovative NDT techniques were looked into.

Experiment and numerical simulation of stress detection for oil and gas pipelines based on magnetic stress coupling of pipeline steel

In this study, a stress detection method is proposed to address the challenge of developing a nondestructive stress testing technique for oil and gas pipelines in real-time and online due to the complexity of the service environment and particularity of the transportation medium. The method is based on the magnetic mechanical properties of pipeline steel materials and the magnetic induction intensity stress coupling relationship. To evaluate the proposed method, six commonly used pipeline steels (X52, X56, X60, X65, X70, and X80) were selected as research objects, and their hysteresis curves were tested. The local magnetization key parameters of oil and gas pipelines were determined based on their magnetic properties. In addition, a finite element simulation model was established for oil and gas pipelines with coupling of magnetic field and stress field. This study appraise the distribution of magnetic field and magnetic induction intensity along the length and cross section height of the oil and gas pipelines simulation model after local magnetization, without being pressurized. Moreover, it examines the mechanism of magnetic induction intensity and stress variation of the simulation model under different internal pressures of the oil and gas pipelines. To this end, a coupling relationship of axial stress, circumferential stress, and magnetic induction intensity was separately established. Moreover, stress–strain test and magnetic induction intensity test systems were integrated to test the X80 pipeline steel. Through analyzing the test data and finite element simulation data, the validity of magnetic coupling simulation of oil and gas pipelines and the reliability of stress detection were verified. The results provide a theoretical basis for the key technologies of nondestructive online stress detection of oil and gas pipelines.

FFRC and SASW nondestructive evaluation of concrete strength from early ages

Free-Free Resonant Column and Spectral Analysis of Surface Waves are two nondestructive testing techniques which are becoming increasingly relevant in construction engineering, as they offer the possibility of assessing the mechanical properties of the building materials without damaging the sample. In the present study, these two testing methods are applied on concrete samples with increasing curing times, from 9 h to 60 days, estimating the dynamic elastic modulus and proposing relationships to obtain the compressive strength from these values. The nondestructive results are compared with the actual strength of the material obtained via uniaxial compression tests, proving the accuracy of the proposed nondestructive testing methodologies, even for early ages. SASW tests are also carried out on a reinforced concrete sample, concluding that the presence of the reinforcement does not alter the results of the test.

Research on detection method of concrete compressive strength based on dielectric properties

Compressive strength of concrete structures is a crucial quality parameter that determines their performance throughout lifespan. This study introduces an innovative approach of utilizing electromagnetic nondestructive testing to assess the compressive strength of mature concrete. The dielectric constant and compressive strength of standard specimens were tested by open coaxial probe and pressure testing machine, respectively, using ordinary Portland cement concrete with various water-cement ratios (0.4, 0.45, 0.5, 0.55, 0.6 and 0.65) as the research object. It has been discovered that increasing porosity reduces the number of polarized particles per unit volume of concrete, resulting in a decrease in polarization intensity. This decrease is manifested as a reduction in the dielectric constant in macro parameters. The relationship between porosity and dielectric constant was characterized using a series-parallel composite concrete dielectric model. Additionally, as porosity increases, the compressive strength of concrete decreases exponentially. By considering porosity as an intermediate variable, a concrete compressive strength model based on the dielectric constant was established. The model exhibits an average error of 3.62%, indicating the precise characterization of concrete's compressive strength using the dielectric constant. This model can serve as a theoretical basis for the implementation of electromagnetic nondestructive testing techniques in strength evaluation.

Characterization of a leaky earth dam using integrated geophysical surveys

Early earth dams were mainly constructed using a soil core wall, which is susceptible to a variety of potential risks such as inhomogeneity, poor compactness, and leakage due to multiple fillings and biological damage. To ensure safe operation of the dam, cost-effective geophysical non-destructive testing techniques should be considered during routine maintenance. This paper presents a case study of an earth dam designed for water storage and flood control that experienced a leak problem. To provide a basis for future leakage control, we carried out 2D integrated geophysical surveys utilizing Electric Resistivity Tomography (ERT), Ground Penetrating Radar (GPR), and Surface Wave (SW) methods along the dam axis. In conjunction with the fast attenuation characteristic of GPR signals, the low resistivity anomalies recovered by the ERT provide evidence to reveal the leakage area within the dam structure. Moreover, the shear wave velocity variation retrieved by the SW method helps to determine the distribution of bedrock and soil layers. By integrating the results mentioned above, the interpretation outcome yields a comprehensive understanding of the internal condition of the earth dam. The successful investigation of the dam structure and the mapping of the leakage area demonstrate the effectiveness of integrated geophysical surveys in characterizing earth dams. It also suggests that it is essential to consider the difference between various geophysical results and the underlying reason when integrating them to characterize leaky earth dams.

Improving efficiency of local wavenumber estimation for damage detection in thin-walled structures

Full-field imaging of guided ultrasonic waves is an emerging Nondestructive Testing (NDT) technique for the inspection of thin-walled structures. One of the most effective full-field processing techniques is the Local Wavenumber Estimation (LWE) which can be applied to detect various types of damage in both homogeneous and multilayer specimens. In this paper, we discuss the possibilities for optimizing the efficiency of the LWE algorithm to facilitate its practical applications. We propose several different signal processing approaches to optimize the algorithm's computational efficiency without sacrificing the damage imaging quality. We evaluate the influence of these improvements by measuring the computation time necessary to perform particular processing steps of the LWE process. We also provide the implementation guidelines for the proposed optimization techniques. The proposed improvements allow for a considerable reduction in the processing time of the LWE with respect to the baseline implementation of the algorithm, ranging from a factor of 10 to a factor of 24, depending on the input dataset.

RCF crack direction assessment in moving ferromagnetic material by DC electromagnetic NDT technique

The rolling contact fatigue (RCF) cracks that initiate from the surface of ferromagnetic materials in aviation bearings and high-speed rails are typically inclined at varying angles, posing a serious threat to safety. Therefore, a rapid and quantitative characterization of inclined cracks is of great importance. This paper investigates the distribution of magnetic fields and motion-induced eddy current (MIEC) in direct current (DC) electromagnetic non-destructive testing (NDT), and proposes an approach for measuring the direction of RCF cracks in moving ferromagnetic materials. The magnetic field and MIEC distributions in the moving ferromagnetic material are initially investigated, followed by a numerical simulation exploring the impact of crack inclination angle and depth on detection signals. The resulting relationship between crack propagation angle, depth, and detection signal is then obtained. Furthermore, a DC electromagnetic NDT platform has been designed and a quantitative detection approach for inclined cracks has been proposed and validated through experiments. Finally, a real scenario detection is carried out. The investigation presented in this paper demonstrates that the DC electromagnetic NDT technique is applicable not only for assessment RCF crack direction in moving ferromagnetic materials, such as rotating aviation bearings, but also for characterizing inclined cracks under conditions of fast relative motion between the probe and the ferromagnetic material (e.g., high-speed rail, pipeline, etc.).