Showing papers in "Ndt & E International in 2022"
TL;DR: In this paper , the authors proposed a mathematical-physic modeling of skewness to quantitatively characterize crack depth in a rail sample and compared the accuracy of crack depth determination in stationary and scanning modes.
Abstract: Eddy current pulsed thermography (ECPT) has been used in the characterization of Rolling Contact Fatigue (RCF) cracks in rail by taking advantage of electromagnetic thermal execution. However, quantitative estimation of depths of RCF defects remains challenging due to volume eddy current heating and lateral thermal diffusion under the stationary and scanning inspections. This work proposes math-physic modelling of skewness to quantitatively characterize crack depth in a rail sample. In particular, a physical model linked instrument describing the accumulation of Joule heating via eddy current accompanied by heat diffusion and mathematical skewness has been established. A comparison between the accuracy of crack depth determination in stationary and scanning modes was carried out in terms of the thermal response and skewness. Moreover, the effects of crack types on depth quantification have been analyzed. The comparative experimental results indicated that the skewness under stationary conditions is more robust against the noise, and this has verified the efficiency for quantifying RCF cracks in stationary and scanning modes.
21 citations
TL;DR: In this article , a deep learning framework for artefact identification and suppression in the context of non-destructive evaluation is proposed, based on the concept of autoencoders, which is developed for enhancing ultrasound inspection and defect identification through images obtained from full matrix capture data and the total focusing method.
Abstract: This paper proposes a deep learning framework for artefact identification and suppression in the context of non-destructive evaluation. The model, based on the concept of autoencoders, is developed for enhancing ultrasound inspection and defect identification through images obtained from full matrix capture data and the total focusing method. An experimental case study is used to prove the effectiveness of the method while exploring its practical limitations. A comparison with a state-of-the-art methodology based on image analysis is addressed for the identification and suppression of artefacts. In general, the proposed method efficiently provides accurate suppression of artefacts in complex scenarios, even when the defect is located below the footprint of the ultrasonic probe, and also yields the physical parameters needed for imaging as a by-product. • A deep learning method for artefact identification and suppression is proposed. • The grey-box model provides physical parameters used for imaging. • The method is trained using defect-free model data and tested in experimental data. • Ultrasonic image interpretation is facilitated by removing artefacts from raw data.
12 citations
TL;DR: Wang et al. as mentioned in this paper proposed a modified minimum variance method, where waveform information was integrated into algorithm imaging procedure to improve the performance of image reconstruction and localization. But, this method is not suitable for low-resolution images.
Abstract: Lamb wave minimum variance imaging is a promising method for visual damage identification and localization with a sparse transducer array. Imaging performance of minimum variance is highly dependent on the design accuracy of look-direction to describe amplitude relationship of array reflection signals. Look-direction is the combination of a directivity reflection pattern and an attenuation with propagation distance. However, reflection pattern is closely related to damage parameters (e.g. type, orientation and size) and these parameters are usually unknown beforehand. Therefore, accurate design of look-direction is difficult or even impossible, and design error can significantly degrade imaging performance. To overcome this limitation, a modified minimum variance method is proposed in this study. Besides amplitude information, waveform information is integrated into algorithm imaging procedure. Correlation coefficient between local signal and excitation waveform is calculated to generate the distribution of weights for diagonal loading. Diagonal loading weight is an adjustable coefficient in minimum variance algorithm to control the tolerance for look-direction error. With larger weights for potential damage locations, tolerance for inaccuracy in look-direction is increased, and imaging performance is accordingly improved. Experiments on both aluminum plates and composite laminates are carried out to demonstrate the performance improvement of modified method.
12 citations
TL;DR: The Livermore Tomography Tools (LTT) as mentioned in this paper is built for computed tomography (CT) research and can process x-ray and neutron CT data accurately and rapidly from raw detector counts to reconstructed volumes with flexibility to handle many special cases.
Abstract: This paper introduces the capabilities and availability of a customizable scientific software package called Livermore Tomography Tools (LTT) built for computed tomography (CT) research. It was initially developed to process x-ray and neutron CT data accurately and rapidly from raw detector counts to reconstructed volumes with the flexibility to handle many special cases. Our goals were to provide quantitatively accurate results reported in physical units (e.g., mm−1 or cm−1) while exploiting all available computational advantages to maximize speed and conserve memory. Written in C/C++ with support for multiple CPUs and GPUs, LTT runs on many computing platforms (Linux/Unix, Windows, and Mac; laptops to supercomputers). As a result, LTT can: process data acquired from various custom-built and commercially available CT scanners, model and simulate x-ray and neutron interactions to encourage algorithm prototyping, and allow for rapid insertion of the latest algorithms. We describe LTT's software architecture, user interfaces, and its 88 algorithms (as of this writing) for pre-processing, reconstruction, post-processing, and simulation that support many scanner geometries (parallel-, fan-, cone-beam, and custom). Several applications are presented that illustrate LTT's accuracy, speed, and flexibility relative to other solutions.
12 citations
TL;DR: Wang et al. as mentioned in this paper proposed a modified minimum variance method, where waveform information was integrated into algorithm imaging procedure to improve the performance of the LW imaging with a sparse transducer array.
Abstract: Lamb wave minimum variance imaging is a promising method for visual damage identification and localization with a sparse transducer array. Imaging performance of minimum variance is highly dependent on the design accuracy of look-direction to describe amplitude relationship of array reflection signals. Look-direction is the combination of a directivity reflection pattern and an attenuation with propagation distance. However, reflection pattern is closely related to damage parameters (e.g. type, orientation and size) and these parameters are usually unknown beforehand. Therefore, accurate design of look-direction is difficult or even impossible, and design error can significantly degrade imaging performance. To overcome this limitation, a modified minimum variance method is proposed in this study. Besides amplitude information, waveform information is integrated into algorithm imaging procedure. Correlation coefficient between local signal and excitation waveform is calculated to generate the distribution of weights for diagonal loading. Diagonal loading weight is an adjustable coefficient in minimum variance algorithm to control the tolerance for look-direction error. With larger weights for potential damage locations, tolerance for inaccuracy in look-direction is increased, and imaging performance is accordingly improved. Experiments on both aluminum plates and composite laminates are carried out to demonstrate the performance improvement of modified method.
12 citations
TL;DR: In this article, a deep learning framework based on the concept of autoencoders is proposed for enhancing ultrasound inspection and defect identification through images obtained from full matrix capture data and the total focusing method.
Abstract: This paper proposes a deep learning framework for artefact identification and suppression in the context of non-destructive evaluation. The model, based on the concept of autoencoders, is developed for enhancing ultrasound inspection and defect identification through images obtained from full matrix capture data and the total focusing method. An experimental case study is used to prove the effectiveness of the method while exploring its practical limitations. A comparison with a state-of-the-art methodology based on image analysis is addressed for the identification and suppression of artefacts. In general, the proposed method efficiently provides accurate suppression of artefacts in complex scenarios, even when the defect is located below the footprint of the ultrasonic probe, and also yields the physical parameters needed for imaging as a by-product.
12 citations
TL;DR: In this article, the results of rotating eddy current testing were superposed by uniformed current testing in orthogonal directions to improve the detection ability and expand the application of the rotating current testing technique.
Abstract: The results of rotating eddy current testing can be regarded as the results superposed by uniform eddy current testing in orthogonal directions. However, in the case of directional nondestructive testing problems, such as weld detection, only the results from the induced current that is perpendicular to the crack have the greatest sensitivity. In contrast, the results from other induced current orientations may experience substantial noise generated by welds. In this work, a brief method is proposed to transform the results of rotating eddy current testing into the results of uniform eddy current testing at the desired eddy current orientation. The experiments indicated a good match between the signal distribution and amplitude of the transformed results and the results of the uniform eddy current testing. Moreover, the proposed method was applied for weld detection. The results revealed that the weld noise significantly decreased and that the locations of slits could be clearly identified. The results validated that the proposed method can improve the detection ability and expand the application of the rotating eddy current testing technique.
11 citations
TL;DR: In this article , a comparison between R2-based analysis, principal component thermography (PCT), and sparse Principal Component Thermography (S-PCT) was performed on a laboratory-scale section of a roadway.
Abstract: Roadways' sub-pavement voids caused by eroded soil through damaged culverts lead to safety hazards, traffic inconvenience, and expensive repairs. Infrared thermography (IRT) could help to identify those voids before the structural integrity of roadway pavements is compromised. However, IRT suffers from poor signal-to-noise ratio. This study implements the use of three advanced image-processing techniques to increase the accuracy of IRT in detecting voids underneath a roadway. A comparison between R2-based analysis, principal component thermography (PCT), and sparse principal component thermography (S-PCT) is presented and validated through extensive tests on a laboratory-scale section of a roadway. Results show pros and cons of the three techniques and how S-PCT allows determining the physical size of sub-pavement voids with an accuracy above 95%. This research provides the foundation for comparing advanced image-processing techniques that can progress the use of IRT as a more accurate and cost-effective nondestructive evaluation method for roadways’ condition monitoring.
10 citations
TL;DR: In this paper, an intelligent denoise laser ultrasonic imaging method was developed to inspect the micro defects on the rough surface of SLM components, which can increase the average SNR from 27.0 dB to 35.2 dB.
Abstract: The random microdefects are inevitable during the Selective laser melting (SLM) process due to the principle of discrete-stacking. The rough surface induced strong background noise reduces the probability of detection of traditional laser ultrasonic testing system. In this study, an intelligent denoise laser ultrasonic imaging method was developed to inspect the micro defects on the rough surface of SLM components: (1) a non-contact laser ultrasonic scanning setup was established for data acquisition of multiple ultrasonic signals; (2) a denoising algorithm based on unsupervised machine learning was designed and trained by abundant ultrasonic data to enhance the signal to noise ratio (SNR); (3) a signal matching algorithm based on the cross-correlation and self-normalized method was established to match the amplitude and arrival time of Rayleigh waves from different scanning points. The performance of developed method was verified using micro hole defects on the rough surface of a SLM part. The results indicated that the established denoising algorithm could increase the average SNR from 27.0 dB to 35.2 dB. All holes with diameter of 50 μm and 100 μm can be detected and sized based on the high SNR image without removing the rough surface. The conclusion can be drawn that the proposed intelligent denoise laser ultrasonic imaging method is a very potential way for the online inspection of SLM.
10 citations
TL;DR: In this paper , a new fusion algorithm applied to the THz and X-ray CT NDT imaging data, based on the combination of saliency region analysis (SRA) and wavelet based multi-scale transforms (W-MST), is proposed to detect delamination and inclusion defects of GFRP composites.
Abstract: Inspection of thin and multi-depth delamination and foreign inclusion defects inside glass fiber reinforced polymer (GFRP) composites is a challenge for traditional non-destructive testing (NDT) techniques. Terahertz (THz) and X-ray computed tomography (CT) are two prevalent NDT methods, which also have their unique advantages and relative weakness individually. Specifically, in our previous study, it is found that THz NDT method has a higher contrast and X-ray CT NDT method has a higher resolution for detecting defects in GFRP composites. In this paper, a new fusion algorithm applied to the THz and X-ray CT NDT imaging data, based on the combination of saliency region analysis (SRA) and wavelet based multi-scale transforms (W-MST) is proposed to detect delamination and inclusion defects of GFRP composites. Moreover, the strategy of weighted least square optimization (WLSO) is adopted to eliminate the effects of unregistered images. An exhaustive number of combinations (36 in total) of the proposed fusion algorithm, which include the selection of fusion rules for saliency map, wavelet decomposition levels and wavelet basis functions, have been quantitively compared using objective evaluation indices such as standard deviation (SD) and spatial frequency (SF) to quantify the improvement of detection effect in terms of contrast and resolution. Among them, there are five optimal combinations applicable to five pairs of different source images, and the averages of the SD and SF indicators of fused images have increased by 126% and 190% compared with the source images, respectively. The inspection efficacy of defects using the proposed fusion method has been analyzed visually, showing that the new approach can effectively accentuate the complementary advantages of THz and X-ray NDT methods, resulting in quantifiably improved defect inspections.
10 citations
TL;DR: In this article , the authors investigated the non-destructive detection of defects in thermal images of industrial materials based on segmentation of images generated using enhanced truncated-correlation photothermal coherence tomography (eTC-PCT).
Abstract: This work investigates the non-destructive detection of defects in thermal images of industrial materials based on segmentation of images generated using enhanced truncated-correlation photothermal coherence tomography (eTC-PCT). eTC-PCT is an active infrared thermography modality, which is being applied to the field of non-destructive testing (NDT) and in biomedical & dental thermophotonic imaging. In this report, we combine eTC-PCT with a computer vision algorithm to sharply delineate holes and manufacturing defects (cracks) inside industrial materials. To this end, the eTC-PCT reconstructed image is processed through three consecutive algorithm stages: A threshold selection filter is followed by filtered image segmentation using the K-means algorithm (clustering method) and the outcome is applied to the delineation of (otherwise blurred) discontinuity boundaries by means of the Canny edge detection algorithm. The role of each method is described and it is demonstrated that the combination of these three algorithms is optimal for achieving significant delineation enhancement (sharpness) of blind hole and crack boundaries in industrial materials.
TL;DR: In this article , a comparative study between different periodic and permanent magnet electromagnetic acoustic transducers (PPM EMAT) configurations on a 323.8 mm diameter, 10.2 mm thick steel pipe is presented.
Abstract: Corrosion is a chemical reaction affecting a wide range of materials. Safety-critical structures in the oil, gas, petrochemical, and many other industries need a fast, reliable screening technique that can be performed without operational disruption. Partially accessible structures such as pipes at supports or under a layer of insulation are particularly challenging. In this respect, high-order shear horizontal (SH) guided wave modes can be used in medium-to long-range thickness gauging thanks to their cutoff frequency-thickness product. As the wave propagates, a reduction in the thickness behaves like a low-pass filter for high-order modes. It has been demonstrated that periodic and permanent magnet electromagnetic acoustic transducers (PPM EMAT) can be used for transmission and reception. This paper presents a comparative study between different PPM EMAT configurations on a 323.8 mm diameter, 10.2 mm thick steel pipe. Finite element (FE) simulations and experimental measurements are used to compare the effectiveness of the different configurations to measure the thickness in four intervals: less than 5.4 mm, between 5.4 and 7 mm, between 7 and 8 mm, and between 8 mm and 10.2 mm, thus allowing to detect thickness losses of up to 50% of the nominal thickness of the waveguide.
TL;DR: In this article, the authors investigated the non-destructive detection of defects in thermal images of industrial materials based on segmentation of images generated using enhanced truncated-correlation photothermal coherence tomography (eTC-PCT).
Abstract: This work investigates the non-destructive detection of defects in thermal images of industrial materials based on segmentation of images generated using enhanced truncated-correlation photothermal coherence tomography (eTC-PCT). eTC-PCT is an active infrared thermography modality, which is being applied to the field of non-destructive testing (NDT) and in biomedical & dental thermophotonic imaging. In this report, we combine eTC-PCT with a computer vision algorithm to sharply delineate holes and manufacturing defects (cracks) inside industrial materials. To this end, the eTC-PCT reconstructed image is processed through three consecutive algorithm stages: A threshold selection filter is followed by filtered image segmentation using the K-means algorithm (clustering method) and the outcome is applied to the delineation of (otherwise blurred) discontinuity boundaries by means of the Canny edge detection algorithm. The role of each method is described and it is demonstrated that the combination of these three algorithms is optimal for achieving significant delineation enhancement (sharpness) of blind hole and crack boundaries in industrial materials.
TL;DR: In this paper , an intelligent denoise laser ultrasonic imaging method was developed to inspect the micro defects on the rough surface of SLM components, which can increase the average SNR from 27.0 dB to 35.2 dB.
Abstract: The random microdefects are inevitable during the Selective laser melting (SLM) process due to the principle of discrete-stacking. The rough surface induced strong background noise reduces the probability of detection of traditional laser ultrasonic testing system. In this study, an intelligent denoise laser ultrasonic imaging method was developed to inspect the micro defects on the rough surface of SLM components: (1) a non-contact laser ultrasonic scanning setup was established for data acquisition of multiple ultrasonic signals; (2) a denoising algorithm based on unsupervised machine learning was designed and trained by abundant ultrasonic data to enhance the signal to noise ratio (SNR); (3) a signal matching algorithm based on the cross-correlation and self-normalized method was established to match the amplitude and arrival time of Rayleigh waves from different scanning points. The performance of developed method was verified using micro hole defects on the rough surface of a SLM part. The results indicated that the established denoising algorithm could increase the average SNR from 27.0 dB to 35.2 dB. All holes with diameter of 50 μm and 100 μm can be detected and sized based on the high SNR image without removing the rough surface. The conclusion can be drawn that the proposed intelligent denoise laser ultrasonic imaging method is a very potential way for the online inspection of SLM.
TL;DR: In this article, the presence of combined frequency wave due to contact acoustic nonlinearity is used to detect debonding at the adhesive joint using a nonlinear Lamb wave mixing approach.
Abstract: Many engineering structural elements make use of adhesively bonded joints due to its lighter weight, load distribution and transmission mechanisms. The safety of the structure relies greatly on the condition of the adhesive joint. In this paper, the detection of debonding at the adhesive joint is investigated using a nonlinear Lamb wave mixing approach. The method relies on the presence of combined harmonics as indicative of material nonlinearity due to dislocations or anharmonicity in intact specimens or contact nonlinearity produced by the defects. In this study, experiments and three-dimensional finite element simulations were conducted and demonstrated that the presence of combined frequency wave due to contact acoustic nonlinearity is effective for indicating debonding. The effect of the debonding width was investigated and the debonding width was found to correlate well with the combined harmonic energy generated due to debonding. The findings presented in this study provides physical insights into the effect of debonding mechanisms at adhesive joints in related to the nonlinear Lamb wave mixing approach, and can be used to further develop the debonding detection techniques using wave mixing.
TL;DR: In this article , a combined signal conditionings of differential eddy current structure based on bridge and transformer for lift-off suppression is proposed, which provides both amplitude and phase information via two signal conditionsings for multiple parameter measurement.
Abstract: Lift-off is a challenging issue in electromagnetic non-destructive testing and evaluation, especially in eddy current testing. It seriously affects the detection sensitivity and quantification accuracy. This paper proposes a combined signal conditionings of differential eddy current structure based on bridge and transformer for lift-off suppression. It provides both amplitude and phase information via two signal conditionings for multiple parameter measurement. Specifically, a multiple parameters fusion response model is established according to equivalent circuits between the electromagnetic sensing coupling and lift-off. This model enables to greatly reduces the lift-off ambiguities from mixed signal since lift-off parameter can be eliminated and defect information is increased. In particular, it is suitable for irregular lift-off variation suppression with non-parameters adjusting. Different defect samples have been conducted to validate the proposed method.
TL;DR: In this paper , the presence of combined frequency wave due to contact acoustic nonlinearity is used to detect debonding at the adhesive joint using a nonlinear Lamb wave mixing approach.
Abstract: Many engineering structural elements make use of adhesively bonded joints due to its lighter weight, load distribution and transmission mechanisms. The safety of the structure relies greatly on the condition of the adhesive joint. In this paper, the detection of debonding at the adhesive joint is investigated using a nonlinear Lamb wave mixing approach. The method relies on the presence of combined harmonics as indicative of material nonlinearity due to dislocations or anharmonicity in intact specimens or contact nonlinearity produced by the defects. In this study, experiments and three-dimensional finite element simulations were conducted and demonstrated that the presence of combined frequency wave due to contact acoustic nonlinearity is effective for indicating debonding. The effect of the debonding width was investigated and the debonding width was found to correlate well with the combined harmonic energy generated due to debonding. The findings presented in this study provides physical insights into the effect of debonding mechanisms at adhesive joints in related to the nonlinear Lamb wave mixing approach, and can be used to further develop the debonding detection techniques using wave mixing.
TL;DR: In this paper , a double-coil configuration of a high-temperature EMAT for waveform generation in paramagnetic steel was studied, and the amplitude ratio of shear and longitudinal waves was about 1 when the diameter of the EMAT eddy-current coil was equal to the inner diameter of an electromagnetic coil.
Abstract: The non-contact nature of electromagnetic acoustic transducers (EMATs) allows a continuous operation at high temperatures without physical coupling. The existing high-temperature EMATs are mainly shear-wave EMATs, and there are few reports on shear-longitudinal wave or longitudinal-wave EMATs due to the low intensity of the horizontal magnetic field. However, a desirable EMAT design characteristic is the possibility of selecting to generate different acoustic wave modes for various engineering applications. In this paper, a high-temperature EMAT with a double-coil configuration on waveform generation in paramagnetic steel was studied. By adjusting the configuration relationship between the electromagnetic coil and the EMAT eddy-current coil, the selective generation of shear-wave, longitudinal-wave, and shear-longitudinal wave modes was realized. According to quantitative analysis of shear and longitudinal waves generated by the double-coil EMAT, the amplitude ratio of shear and longitudinal waves was about 1 when the diameter of the EMAT eddy-current coil was equal to the inner diameter of the electromagnetic coil. Furthermore, shear-wave mode and shear-longitudinal wave mode high-temperature EMATs were designed and fabricated. The EMAT was placed in a high-temperature environment to continuously measure the paramagnetic steel SUS304. The amplitudes of the shear wave and longitudinal wave at 500 °C decreased by 10.2 times and 3.8 times, respectively, compared with that at 25 °C. The designed EMAT can selectively generate and receive different bulk acoustic wave modes at high temperatures.
TL;DR: In this paper, numerical simulations and physical experiments are studied for two shapes of electrodes, triangular and rectangular, by examining different sizes and different separation distances between electrodes to assess and analyze the important features of the coplanar capacitive electrodes, such as the penetration and strength of the electric field as a function of sensor geometrical properties.
Abstract: Coplanar capacitive sensors are employed in Non-destructive Testing (NDT) methods to measure the difference in dielectric properties of the materials. The most important design parameters for a coplanar capacitive sensor include the shape, size, and separation distance of the electrodes which affect the sensor performance. In addition, the impact of the shielding plate and guard electrode should be considered. In the framework of this paper, numerical simulations and physical experiments are studied for two shapes of electrodes, triangular and rectangular, by examining different sizes and different separation distances between electrodes to assess and analyze the important features of the coplanar capacitive electrodes, such as the penetration and strength of the electric field as a function of sensor geometrical properties. Therefore, a detailed analysis of numerical simulation using Finite Element Modelling (FEM) is provided to study these geometric parameters. In addition, the influence of the different frequencies, lift-off, and the presence or absence of a metal shielding plate and guard electrode on the output result is analyzed. Finally, sensors were manufactured and several experiments were carried out under different configurations. Comparison of the numerical simulation results and physical experiments illustrate that they are in good qualitative agreement.
TL;DR: In this article , an air-coupled antenna array is designed for measurement of common midpoint (CMP) GPR data, and an improved velocity spectrum algorithm considering the pavement surface is proposed to simultaneously estimate the thickness and dielectric permittivity of the pavement layer.
Abstract: Ground penetrating radar (GPR) has become an effective tool for asphalt pavement inspection. However, a ground-coupled GPR system cannot facilitate a high-speed survey due to the complex road environment and traffic condition. In this paper, an air-coupled antenna array is designed for measurement of common midpoint (CMP) GPR data. From the CMP data, an improved velocity spectrum algorithm considering the refraction on the pavement surface is proposed to simultaneously estimate the thickness and dielectric permittivity of the pavement layer. The results of a laboratory experiment demonstrate that the improved velocity spectrum method can greatly enhance the accuracy of the velocity and thickness estimations, compared with the traditional inversion method. A field measurement conducted on a highway pavement shows that the maximum error of the thickness estimation of the asphalt layer is less than 10 mm (7.1%). It is concluded that the developed CMP technique can be used for quantitative characterization of asphalt pavement.
TL;DR: In this article , an axisymmetric numerical model was created to simulate the heat transfer within these low thermal diffusivity structures during flash thermography, and it was shown that traditional 1D thermography models can be used to approximate the depth of a defect with negligible error if the defect has an aspect ratio greater than 6.
Abstract: This paper assesses the possibility of measuring small, shallow defects in low thermal diffusivity materials with existing pulse thermography techniques. Defects like these are commonly introduced in additive manufacturing (AM), and their presence can cause inconsistencies in the mechanical properties of the final part. An axisymmetric, numerical model was created to simulate the heat transfer within these low thermal diffusivity structures during flash thermography. Deviations from the ideal conditions commonly used in flash thermography models such as conduction across the flaw and in-depth absorption of the incident pulse were included in the model and their effects on defect measurability were investigated. These nonideal conditions (in addition to free convection) introduce depth measurement errors of >10% even for large aspect ratio defects (such as delaminations). The figures provided quantify the amount of error associated with these individual parameters and can be used to determine if a given thermography system is ideal. The simulation results also demonstrate that traditional 1D thermography models may be used to approximate the depth of a defect with negligible error if the defect has an aspect ratio greater than 6. Smaller defects may also be measured with minimal error. If flash thermography were performed during AM to identify the layers where defects most likely reside, the measurement limits would be even less restrictive.
TL;DR: In this paper , numerical simulations and physical experiments are studied for two shapes of electrodes, triangular and rectangular, by examining different sizes and different separation distances between electrodes to assess and analyze the important features of the coplanar capacitive electrodes, such as the penetration and strength of the electric field as a function of sensor geometrical properties.
Abstract: Coplanar capacitive sensors are employed in Non-destructive Testing (NDT) methods to measure the difference in dielectric properties of the materials. The most important design parameters for a coplanar capacitive sensor include the shape, size, and separation distance of the electrodes which affect the sensor performance. In addition, the impact of the shielding plate and guard electrode should be considered. In the framework of this paper, numerical simulations and physical experiments are studied for two shapes of electrodes, triangular and rectangular, by examining different sizes and different separation distances between electrodes to assess and analyze the important features of the coplanar capacitive electrodes, such as the penetration and strength of the electric field as a function of sensor geometrical properties. Therefore, a detailed analysis of numerical simulation using Finite Element Modelling (FEM) is provided to study these geometric parameters. In addition, the influence of the different frequencies, lift-off, and the presence or absence of a metal shielding plate and guard electrode on the output result is analyzed. Finally, sensors were manufactured and several experiments were carried out under different configurations. Comparison of the numerical simulation results and physical experiments illustrate that they are in good qualitative agreement.
TL;DR: In this paper , a measurement method for fast inspection, large area detection of coating thickness with an active long pulse thermography is proposed, which is theoretically analyzed based on the equation of 1D heat transfer in the depth direction, accordingly coating thickness can be quantitively evaluated by recording the temperature decay curve in the cooling stage and computing the time at the minimum of its 2nd derivative.
Abstract: Non-destructive detection of coating thickness is important after coating deposition and during service. The present work is to establish a measurement method for fast inspection, large area detection of coating thickness with an active long pulse thermography. The measurement method was theoretically analyzed based on the equation of 1D heat transfer in the depth direction, accordingly coating thickness can be quantitively evaluated by recording the temperature decay curve in the cooling stage and computing the time at the minimum of its 2nd derivative. Then, the proposed method was experimentally validated by uniform coating specimen with different thicknesses. Furthermore, the thickness measurement method was successfully applied to measure thickness of an uneven coating specimen within a relative error of 5% with sufficient sampling rate. Finally, some key techniques were discussed for accurate measurements including thermal excitation, sampling rate of thermography, thickness ratio of coating and substrate, etc. The results verify that active long pulse thermography is a powerful tool for non-contact and full-field measurement of coating thickness with merits of safe and easy to implement.
TL;DR: In this paper , the authors proposed a new magnetic permeability perturbation testing (MPPT) method, which uses DC magnetization to excite surface permeability of deeply buried defects and uses an eddy current probe to sense this surface surface PE.
Abstract: DC magnetization is generally considered to suppress the usual local magnetic permeability variation of ferromagnetic materials but also causes neglected non-uniform magnetic characteristics. This paper proposes a new magnetic permeability perturbation testing (MPPT) method, which uses DC magnetization to excite surface permeability perturbation of deeply buried defects and uses an eddy current probe to sense this surface permeability perturbation. The mechanism of MPPT is analyzed in detail and verified by a series of experiments. The effects of the differential distance of probe, crack size, insulation thickness, and excitation frequency on MPPT are investigated experimentally. Finally, the comparison of the proposed MPPT method with traditional eddy current testing and magnetic flux leakage testing is discussed in detail. The proof-of-concept results indicated that the MPPT testing method can realize the detection of internal defects of a 25 mm thick steel plate. This method is completely feasible and has great practical value as a supplement to conventional nondestructive testing methods.
TL;DR: In this paper, a double-coil configuration of a high-temperature EMAT with a double coil configuration on waveform generation in paramagnetic steel was studied, by adjusting the configuration relationship between the electromagnetic coil and the EMAT eddy-current coil, the selective generation of shearwave, longitudinal-wave, and shear longitudinal wave modes was realized.
Abstract: The non-contact nature of electromagnetic acoustic transducers (EMATs) allows a continuous operation at high temperatures without physical coupling. The existing high-temperature EMATs are mainly shear-wave EMATs, and there are few reports on shear-longitudinal wave or longitudinal-wave EMATs due to the low intensity of the horizontal magnetic field. However, a desirable EMAT design characteristic is the possibility of selecting to generate different acoustic wave modes for various engineering applications. In this paper, a high-temperature EMAT with a double-coil configuration on waveform generation in paramagnetic steel was studied. By adjusting the configuration relationship between the electromagnetic coil and the EMAT eddy-current coil, the selective generation of shear-wave, longitudinal-wave, and shear-longitudinal wave modes was realized. According to quantitative analysis of shear and longitudinal waves generated by the double-coil EMAT, the amplitude ratio of shear and longitudinal waves was about 1 when the diameter of the EMAT eddy-current coil was equal to the inner diameter of the electromagnetic coil. Furthermore, shear-wave mode and shear-longitudinal wave mode high-temperature EMATs were designed and fabricated. The EMAT was placed in a high-temperature environment to continuously measure the paramagnetic steel SUS304. The amplitudes of the shear wave and longitudinal wave at 500 °C decreased by 10.2 times and 3.8 times, respectively, compared with that at 25 °C. The designed EMAT can selectively generate and receive different bulk acoustic wave modes at high temperatures.
TL;DR: In this article , a thermal image spatial distribution characteristics and temporal varying regularity of the leakage outlets under the conditions of grassed and bare soil are explored, which can provide some experience and reference for using IRT to identify the leakage of earth rock dams and dykes.
Abstract: The leakage of earth rock dams and dykes is characterized by time-space randomness, concealment and small initial discharge quantity. The time from the beginning of abnormal leakage to the break of a dyke is quite limited. The key to ensure the safety of earth rock dykes is to find and identify the leakages in time. Infrared thermography (IRT) has the advantages of fast visualization, strong mobility and wide coverage, so it is a good choice to detect the leakage outlet of earth rock dams and dykes in flood season. However, there are few reports on detailed research in this field. Fully simulating the real service conditions of earth rock dams in flood season, and considering the influence of turf covering, an earth dam model with leakage tunnels is built, then whole day infrared thermography tests of the outlet of the leakages are carried out at an outdoor laboratory in the lower reaches of the Yangtze River. More refined thermal imaging experiments are carried out in the afternoon and at night respectively. The better condition of using IRT to identify the leakage of earth rock dam is explored, and the thermal image spatial distribution characteristics and temporal varying regularity of the leakage outlets under the conditions of grassed and bare soil are also explored, which can provide some experience and reference for using IRT to identify the leakage of earth rock dams and dykes.
TL;DR: In this article , a method based on eddy current pulsed thermography (ECPT) combined with feature extraction transform algorithms is proposed for transient thermal pattern separation and defect detection in composite insulators with internal conductive defects.
Abstract: Composite insulators are prone to internal conductive defects owing to their long-term high-voltage operation and outdoor aging. In this study, a method based on eddy current pulsed thermography (ECPT) combined with feature extraction transform algorithms is proposed for transient thermal pattern separation and defect detection in composite insulators with internal conductive defects. The principles of this method, including electromagnetic heating, thermal diffusion, and transient thermal response extraction, are investigated. Conductive defects in insulators have unique thermal patterns that differ from those of non-conductive defects in conductive materials. In this study, the original transient thermal responses of conductive defects are analyzed and compared with those of non-conductive defects. The effectiveness of the transient thermal pattern separation of this method is verified through experimental studies. Different feature extraction transform methods are utilized and compared to separate different thermal transient patterns and perform quantitative evaluations. It is found that ECPT combined with feature extraction transform algorithms can provide a visual and effective method to detect internal conductive defects in composite insulators.
TL;DR: In this article , the transient eddy current response of a ferromagnetic casing which a pair of eccentric coils are placed inside is studied, and time-domain expressions to transient responses which are induced by excitation field and eddy currents field are formulated with the second order vector potential.
Abstract: Transient eddy current response of a ferromagnetic casing which a pair of eccentric coils are placed inside is studied. Time-domain expressions to transient responses which are induced by excitation field and eddy current field are formulated with the second order vector potential. And based on the deduced formulas, an efficient method is proposed in this paper to obtain the transient eddy current response that would benefit the health condition analysis of a conductive casing, and the method is verified by experiments.
TL;DR: The proposed unsupervised probabilistic low-rank component factorization thermographic de-noising model is proposed to improve image performance and defect visualization and can detect voids and resin rich areas presenting a better image performance if compared to direct infrared inspection results.
Abstract: In this work, infrared thermography is used to detect defects on a 3D hybrid aluminium-CFRP composite structure. First, radiometric calibration and geometric distortion correction are performed for 3D inspection. Second, we propose a new unsupervised probabilistic low-rank component factorization thermographic de-noising model to improve image performance and defect visualization. Signal profiles and standard deviation analysis is used to assess the results, and x-ray CT inspections are compared to the infrared inspection results. Finally, we can conclude that the proposed algorithm can detect voids and resin rich areas presenting a better image performance if compared to direct infrared inspection results.
TL;DR: In this paper , the authors proposed a structural health monitoring approach for damage detection, localization, and assessment using a minimal Lamb Wave (LW) sensor-actuator set-up.
Abstract: The increasing mechanical and economical demands in modern systems and structures are forcing an inevitable need for joining dissimilar materials, thus creating the challenge of establishing a process to inspect and monitor dissimilar joints. Condition monitoring is a necessity to ensure that the structures are being safely used and to extend their lifetime. Lamb waves (LWs) are ultrasonic guided waves that are widely used for structural health monitoring of mechanical, aerospace, and civil structures. This paper proposes a novel structural health monitoring approach for damage detection, localization, and assessment using a minimal LW sensor-actuator set-up. More specifically, the proposed framework provides damage detection, localization and assessment within a dissimilar-material joint by Bayesian inference of six parameters of damage extent and location. Finite element simulations are used to simulate the measured LWs and generate a dataset required to train artificial neural networks (ANN), acting as surrogate models for LW simulation with reduced computational cost. The ANN-based LW simulations are further used as forward model within an Approximate Bayesian Computation (ABC) framework to provide probabilistic inference of the damage size and position. The results show that damage of different sizes and locations can be successfully identified with a high level of resolution and with quantified uncertainty. The results also show that data fusion for ABC inference using multiple sensor measurements can be possible with improved inference results. However, a precise and robust damage inference can be achieved using a minimal sensing set-up based on one actuator and two sensing points, with consideration of certain levels of measurement noise. These findings imply a considerable reduction of complexity of LW actuator-sensor networks, and overall, they imply a significant reduction of computational resources and cost for damage detection and assessment in structures, thus providing a step forward towards online/onboard monitoring applications.