Defect Detection Sensitivity Research Articles

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

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

Mode conversion of fundamental guided ultrasonic wave modes at part-thickness crack-like defects

Guided ultrasonic waves can be employed for efficient structural health monitoring (SHM) and non-destructive evaluation (NDE), as they can propagate long distances along thin structures. The scattering (S0 mode) and mode conversion of low frequency guided waves (S0 to A0 and SH0 wave modes) at part-thickness crack-like defects was studied to quantify the defect detection sensitivity. Three-dimensional (3D) Finite Element (FE) modelling was used to predict the mode conversion and scattering of the fundamental guided wave modes. Experimentally, the S0 mode was excited by a piezoelectric (PZT) transducer in an aluminum plate. A laser vibrometer was used to measure the out-of-plane displacement to characterize the mode-converted A0 mode, employing baseline subtraction to achieve mode and pulse separation. Good agreement between FE model predictions and experimental results was obtained for perpendicular incidence of the S0 mode. The influence of defect depth and length on the scattering and mode conversion was studied and the sensitivity for part-thickness defects was quantified. The maximum mode conversion (S0-A0 mode) occurred for ¾ defect depth and the amplitude of the mode-converted A0 and scattered S0 modes mostly increased linearly as the defect length increased with an almost constant A0/S0 mode scattered amplitude ratio. Similar forward and backward scattering amplitude was found for the mode converted A0 mode. The mode conversion of the S0 to SH0 mode has the highest sensitivity for short defects, but the SH0 mode amplitude only increased slightly for longer defects. Employing the information contained in the mode-converted, scattered guided ultrasonic wave modes could improve the detection sensitivity and localization accuracy of SHM algorithms.

Comparative Study on Multiple Methods for Partial Discharge Detection of Gap Defect in Medium Voltage Switchgear

Abstract The medium voltage switchgear is a key equipment in the distribution network of the power system, which directly faces users. Once a fault occurs, it will cause huge economic losses and adverse social impacts. Partial discharge measurement is an important means to ensure the safe operation of switchgear. The physical quantities measured by different partial discharge detection methods are different, so the detection effects by different methods for the same defect are also different. This paper takes the common internal air gap defects of solid insulation in switchgear as the research object, and conducts partial discharge detection tests on real switchgear equipment. At the same time, radio frequency method, ultrasonic method, transient earth voltage (TEV) method, and pulse current method are used to detect and analyze the partial discharge generated by the defect. The results show that the radio frequency method, TEV method, and pulse current method have good detection sensitivity for air gap defects, but the ultrasonic method cannot detect this type of defect. Comparing the amplitudes of the three electrical signals, it can be found that the correlation between the TEV method and the pulse current method is relatively high, while the correlation between the radio frequency method and the two is relatively low. As the applied voltage increases, the amplitude of the radio frequency method changes linearly, which can more sensitively reflect the degree of defect change.

Guided Wave Phased Array Ultrasonic Imaging for Thin Plate Inspection

This study focuses on the application of phased array ultrasonic testing with guided waves for Structural Health Monitoring (SHM) in thin plates. Addressing the limitations of conventional ultrasonic testing efficiently by inspecting thin, long structures. This study employs phased array technology to thin isotropic plates at high frequency(2MHz), thereby elevating imaging and defect detection in structural components. Through the utilization of the full matrix capture (FMC) scanning approach in 3-D finite element (FE) simulations, A0 and S0 modes were generated on steel plates with through-hole geometries. The process involved capturing reflected signals to form an FMC matrix that includes all A-scan signals. Subsequently, the total focusing method (TFM) algorithm was applied to FMC data for the reconstruction of focused images of A0 and S0 modes separately. A key improvement in our approach is integrating a time-domain filtering method into the FMC data. This enhancement addresses the dispersive nature of guided waves, thus improving the accuracy in identifying defect locations and sizes within the images. Our finite element simulations on steel specimens, with varied through-hole locations, revealed the method's potential in accurately imaging defects located deeper within the structure. Additionally, the study expanded to evaluate the efficacy of phased array ultrasonic testing (PAUT) in SHM, particularly in conjunction with guided waves to study the flaw detection sensitivity of the defect through the TFM algorithm on isotropic steel plates up to wavelength(λ)/16. This whole analysis considered crucial factors such as ultrasonic wave mode, frequency, and group velocity for the structure under inspection. This study represents a significant contribution to the field of SHM, introducing a sophisticated, real-time monitoring methodology that promises to enhance structural integrity assessment. Keywords: SHM, PAUT, GW, FMC, TFM FE Simulations, Time Domain Filtering, Ultrasonic Testing.

Shear Wave Ultrasound Inspection of Flaws in Silicon Wafers using Focused Transducers.

Silicon parts can contain micrometer-sized vertical cracks that are challenging to detect. Inspection using high-frequency focused ultrasound has shown promise for detecting defects of this size and geometry. However, implementing focused ultrasound to inspect anisotropic media can prove challenging, given the directional dependence of wave propagation and subsequent focusing behavior. In this work, back surface-breaking defects at various orientations within silicon wafers (0-, 15-, and 45-degrees relative to the [010] crystallographic axis) are experimentally inspected in an immersion tank setup. Using 100 MHz unfocused and focused shear waves, the impact of medium anisotropy on focusing and defect detection is evaluated. The scattering amplitude and defect detection sensitivity results demonstrate orientation-dependent patterns that strongly rely on the use of focused transducers. The defects along the 45-degree orientation reveal two-lobe scattering patterns with maximum amplitudes less than half that of the defects in the 0-degree orientation, which in contrast show a one-lobe scattering pattern. The experimental results are further explored using Finite Element (FE) modeling and ray tracing to visualize the impact of focusing on wave propagation within the silicon. Ray tracing results show that the focused beam profiles for the 45- and 0-degree orientations form a butterfly wing and elliptical focusing profile, respectively, which correspond directly to experimentally found scattering patterns from defects. Additionally, the FE scattering results from unfocused transducers reveal single lobe scattering for both 0- and 45-degree orientations, proving the varying scattering patterns to be driven by the anisotropic focusing behavior.

Design of magnetic flux leakage detector for mine wire rope

At present, the online detection technology of wire rope damage in coal mine hoisting needs to be improved. Given the large number of detection elements and high detection cost in the wire rope detection device, the magnetic flux leakage detection device of the wire rope damage detection system is designed. The magnetic sensor is selected. At the same time, in order to improve the strength of the magnetic leakage field at the defect and reduce the number of magnetic sensors, a magnetic focusing device is designed. The finite element simulation shows that the magnetic focusing device can effectively improve the sensitivity of defect detection. The above research provides the basis for the design of steel wire rope damage detection systems and is of great significance to ensure the safe and efficient operation of mine hoisting equipment.

Optimal design of iron-cored coil sensor in magnetic flux leakage detection of thick-walled steel pipe

Thick-walled steel pipes, which bear high internal pressure, are widely applied in nuclear power and pressure pipelines. If there are defects in the inner wall, they are easy to expand and cause accidents. Therefore, the thick-walled steel pipe must be subject to non-destructive testing after production. For the magnetic flux leakage (MFL) testing method, the detection sensitivity gradually decreases with the increase of wall thickness. To solve this problem, a new structure of MFL probe is proposed in this paper. The influence of the iron core permeability on the MFL signal is analyzed theoretically, and the effect of the core length and diameter on the MFL signal is analyzed by simulation. The variation of the MFL signal with the change of the iron core and coil lift-off is studied respectively. The simulation results are verified by experiments. It is found that the lift-off of the iron-cored coil is determined by the iron core position. Based on this phenomenon, an MFL array probe is designed, which can be used for online detection of thick-walled steel pipes to improve the detection sensitivity of inner wall defects.

Full-surface defect detection of navel orange based on hyperspectral online sorting technology.

The whole-surface hyperspectral image acquisition of navel orange is particularly important for surface defect detection and quality classification. Because the light intensity at the edge of the navel orange is lower than that in the middle, the defects on the surface of the navel orange cannot be effectively identified. In this paper, a hyperspectral online sorting device for the whole-surface defects of navel orange is proposed. First of all, the image data of navel orange is collected by online detection sorting equipment and the spectral image of the characteristic wave peak of 1655.72nm was extracted. Then, the light intensity at the edge of the navel orange is enhanced by nonuniformity correction based on quadratic curve fitting, and the light intensity correction of the navel orange is realized. Finally, the corrected image is segmented by the threshold to obtain surface defects, and the number of surface defect pixels is improved effectively compared with that before light intensity correction. Ultimately, the online sorting test is carried out, and the detection accuracy is 100%. This indicates that this method effectively improves the sensitivity of defect detection. At the same time, the dimensionality reduction of hyperspectral data is also carried out, which is conducive to improving the efficiency of online detection.

Anisotropy Corrected FMC/TFM Based Phased Array Ultrasonic Imaging in an Austenitic Buttering Layer

For the narrow gap dissimilar weld between a ferritic steel and a nickel base superalloy, a nickel base alloy buttering layer is deposited on the ferritic steel side as an intermediate layer. The bonding between the buttering layer and the ferritic steel is required to be inspected from the buttering layer side. The buttering layer exhibits very high elastic anisotropy due to elongated columnar grains with preferred orientations. In this paper, the effect of elastic anisotropy on the phased array ultrasonic imaging of defects in the buttering layer is demonstrated for data acquired in full matrix capture (FMC) mode and reconstructed with the total focusing method (TFM). The anisotropy in the buttering layer leads to distorted flaw images, which limits the lateral resolution and defect detection sensitivity. Angle-dependent ultrasonic velocity measured in through-transmission FMC mode has been used for processing the FMC data to obtain high-resolution TFM images with improved sensitivity. The velocity values used are in line with the grain orientations observed by electron-backscatter diffraction (EBSD) studies. Further, an alternate approach is also proposed to obtain a TFM image with improved resolution using a suitable isotropic velocity. The approach can be implemented in any commercial phased array ultrasonic system having the FMC-TFM feature.

Rail Defect Detection Using Ultrasonic A-Scan Data and Deep Autoencoder

Rail defects, especially transverse defects (TDs), can pose risks to safe and efficient railroad operations. Effective rail defect detection is critical for the prevention of broken rail-induced accidents and derailments. In this study, a deep autoencoder (DAE) rail defect detection framework is developed to process ultrasonic A-scan data collected by a roller search unit and to identify the presence of TDs in rail samples. An autoencoder is a semi-supervised learning algorithm that identifies observations in a dataset that significantly deviate from the remaining observations and can be used for rail defect detection. Ultrasonic A-scan signals collected from both pristine and damaged rail segments are analyzed, where the pristine dataset is used to train a DAE model. To improve the accuracy and sensitivity of defect detection, we optimize the architecture and hyperparameters of the DAE model. Moreover, we evaluate the performance of two features extracted from the DAE model through receiver operating characteristic curves and confusion matrix. The DAE features outperformed conventional knowledge-driven features in the accuracy and robustness of defect detection, especially with the presence of noise.

Exceptional Points Induced by Time-Varying Mass to Enhance the Sensitivity of Defect Detection

The exceptional point (EP), a non-Hermitian degeneracy at which eigenvalues and eigenvectors coalesce simultaneously, is investigated in mechanical oscillators involving a time-varying mass. A dynamic modulation mechanism is employed to create the time-varying mass, which can potentially develop an EP by acting as an energy source and drain. To fully demonstrate the existence of EPs, the eigenvalue problem for the modulated system is theoretically formulated through two different schemes, based on the state-space and harmonic balance methods. A second-order EP is confirmed by both methods and exists as a result of the coalescence of fundamental and harmonic eigenmodes. As a salient property of this second-order EP, the square-root behavior of the eigenfrequency subject to small perturbations of the system parameters is theoretically demonstrated, and utilized to devise a proof-of-concept model for mechanical sensors with high sensitivity to void defects in elastic solids. The EP behavior induced by a time-varying mass may find potential applications in damage assessment and health monitoring of engineering structures.

Guided Wave Focusing Imaging Detection of Pipelines by Piezoelectric Sensor Array

The sensor array-based guided wave focusing detection method can effectively improve the detection sensitivity of defects and realize the visual imaging. In this research, the three-dimensional finite element simulation model of stainless steel pipeline for guided wave focusing detection was established, and the L(0, 2) mode of guided wave was excited by applying a load on the end surface of the pipeline. And in the experiment, the excitation and reception of L(0, 2) mode guided waves on the outer surface of the stainless steel pipeline were realized by the piezoelectric transducer array. A 16-channel guided wave focusing experimental system was integrated to conduct the defect detection experiments on a stainless steel pipe with diameter of 140 mm and wall thickness of 5 mm. The total matrix data acquisition was performed, and then the amplitude total focusing (TFM) imaging and sign coherence factor (SCF) imaging of the pipe were realized. In this way, the experimental results showed that the pipeline defect detection method and the system proposed in this research can achieve the longitudinal and circumferential positioning and imaging of defects, like holes and scratches.

Source performance metrics for EUV mask inspection

Rules are derived to obtain specifications on radiance, power, lifetime, and cleanliness of the source for an actinic patterned mask inspection system. We focus on the physical processes and technological aspects governing the requirements of radiation sources for reticle inspection. We discuss differences and similarities to scanner with respect to magnification, system etendue, and image recording. Source radiance requirements are estimated from a perspective of targeted throughput and defect detection sensitivity. The derivations consider the influence of photon shot noise on signal detection and conservation laws of light etendue and radiant flux. We describe the scaling laws for required radiance with targeted sensitivity index, optical contrast, field size, and system throughput. In addition, we address the limits on the required brightness and minimum repetition rate set by mask damage threshold. Finally, system and source cleanliness requirements and criticality of the source availability and lifetime are discussed. The analysis can be applied to other microscopy-based metrology and inspection applications.

In‐situ curing of3Dprinted freestanding thermosets

AbstractDirect‐ink‐writing (DIW) provides a high‐efficiency way for thermoset printing while rapid in‐situ curing has a significant role in manufacturing rate, quality, performance, and flexibility. In this review, in‐situ curing methods that can be integrated into DIW were discussed, including frontal polymerization, electromagnetic heating, photochemistry, electron beam, and resistance heating curing. The in‐situ process monitoring and curing kinetic analysis technologies such as differential scanning calorimetry (DSC), Raman spectroscopy, Fourier transform infrared spectroscopy (FT‐IR), broadband dielectric spectroscopy (BDS), ultrasonic dynamic mechanical analysis (UDMA), fluorescence spectroscopy, were briefly presented. The working mechanism and features of these characterization measurements are studied. Furthermore, machine learning and other artificial intelligence tools used for the optimization of printing materials, topology design, printing path, and defect detection sensitivity are reviewed. Finally, some future research directions for the DIW and in‐situ curing of thermosets are addressed.

Influence of Core and Shield of Coil on Skin Depth in Eddy Current Testing

ABSTRACT In eddy current (EC) nondestructive testing, coil is usually wound on core or covered by shield to improve the sensitivity of defect detection and ability of anti-interference of the probe. However, when core or shield is used, the magnetic field will be redistributed, resulting in a change in the speed of EC attenuation in the depth direction. The purpose of this paper is to reveal the influence of core and shield on skin depth of EC. The results of the finite element analysis show that applying core or shield on coil results in smaller skin depth and the skin depth decreases as the core or shield approaches the test sample. In addition, when both core and shield are used, the reduction of skin depth is minimal if both core and shield are ferromagnetic. The simulation results are verified by experiment.

Defect detection in pipes using Van der Pol systems based on ultrasonic guided wave

The sensitivity of small defect detection and the location of small defect in pipes are the main difficulties of the present ultrasonic guided wave inspection technology. A Van der Pol (VdP) chaotic system's sensitivity to initial conditions and capability of being immune to noise was used to establish the relationship between local defect positions and chaos damage factors in this paper. Simulation studies of phase trajectory and Lyapunov exponents based on damping ratio detection of a Van der Pol system were performed. Five kinds of oblique defects with inclined angles of 0°, 30°, 45°, 60°, and 90° were investigated by experiment and analyzed using the location method of a Van der Pol chaotic system. Both the simulated and experimental results show that the proposed method could be used to inspect weaker guided waves and locate tiny defects.

Natural Rail Surface Defect Inspection and Analysis Using 16-Channel Eddy Current System

Trains are used as the fastest mode of transportation for both people and cargo. The train moves along a special path called “rail”, where fatigue can be accumulated due to wheel-rail contact load as a result of continuous train operation. Consistent and regularly scheduled safety management is required since corrosion rate of the rails located on outside environment is very high. Researchers have actively investigated and developed rail defect inspection systems employing non-destructive techniques to address these problems. In particular, the eddy current inspection technique does not involve contact with the surface of the test specimen and offers the advantage of excellent rail defect detection sensitivity. Therefore, a 16 Channel array eddy current inspection device was developed to inspect the surface defects of the rail. An equation was derived to predict the correlation between the depth and phase of an artificial defect using the eddy current inspection device, and the derived equation was applied to the natural defect specimen.

A Simplified Integration of Multi-Channel Ultrasonic Guided Wave System for Phased Array Detection and Total Focusing Imaging

Ultrasonic guided waves based on sensors array effectively improve the detection sensitivity of defects and realize the intuitive imaging of defects. In this research, a 16-channel guided wave excitation/acquisition system is simply integrated for the array focusing detection. Using a FPGA (Field Programmable Gate Array) as the main control unit, a high-voltage excitation of a multi-channel tone-burst signal with synchronous signal acquisition is designed. Experiments are conducted by the developed multi-channel system and the piezoelectric linear array. A guided wave phased array and the total focusing imaging algorithm are demonstrated on a 1mm aluminum plate. The experimental results show that the system can meet the practical requirements of guided wave array focusing detection. By combining the total focusing method and the phase-based sign coherence factor, the compounded image shows that the artifacts can effectively be reduced, and the contrast is improved. Meanwhile, this system can solve the problem of distributed sensor array detection, in which the process is cumbersome and inefficient previously. Through the system and the sensor array, the guided wave phased focusing method and the composite total focusing method can effectively improve the sensitivity and the positioning accuracy of defect detection in thin plate.

Surface defect detection of cylindrical lithium-ion battery by multiscale image augmentation and classification

While deep convolutional neural networks (CNNs) have recently made large advances in AI, the need of large datasets for deep CNN learning is still a barrier to many industrial applications where only limited data samples can be offered for system developments due to confidential issues. We thus propose an approach of multi-scale image augmentation and classification for training deep CNNs from a small dataset for surface defect detection on cylindrical lithium-ion batteries. In the proposed Lithium-ion battery Surface Defect Detection (LSDD) system, an augmented dataset of multi-scale patch samples generated from a small number of lithium-ion battery images is used in the learning process of a two-stage classification scheme that aims to differentiate defect image patches of lithium-ion batteries in the first stage and to identify specific defect types in the second stage. The LSDD approach is an efficient prototyping method of defect detection from limited training images for quick system evaluation and deployment. The experiments show that, based on only 26 source images, the proposed LSDD (i) constructs two augmented multi-scale datasets of 19,309 and 6889 image patches for training and test, respectively, (ii) achieves 93.67% accuracy for discriminating defect image patches in the first stage, and (iii) reaches 90.78% mean precision rate and 93.89% mean recall rate for defect type identification in the second stage. Our two-stage classification scheme has higher defect detection sensitivity than an intuitive one-stage classification scheme by 0.69%, and outperforms the one-stage scheme in identifying specific defect types. For comparing with YOLOv3 detector, less defect misdetections are observed in our approach as well.

Poly acrylic acid stabilized magnetic nanoemulsions for visual defect detection: Effect of pH on detection sensitivity and colloidal stability

Magnetic field assisted tunable colour contrast in magnetic nanoemulsions is exploited for visual detection of defects in ferromagnetic components. We demonstrate an enhanced defect detection sensitivity by stabilizing oil-in-water magnetic nanoemulsion with poly acrylic acid (PAA) at varying pH. In this detection technique, the variation of magnetic permeability, in the vicinity of the defects, causes magnetic flux leakage, which results in a distinct colour contrast on the magnetic nanoemulsion sensor due to the formation of anisotropic periodic structures, parallel to the external field direction, where the inter-droplet spacing within the linear chains depends on the effective magnetic flux. By exploiting this colour and magnetic flux dependence, severity of the defects is assessed by observing the colour contrast. Our studies indicate that defect detection sensitivity decreases with increasing pH due to globule-to-coil conformational transition of the interfacial PAA molecules. With increasing pH, the PAA network swells due to mobile counter-ions induced osmotic pressure and electrostatic repulsion between the charged monomer units, leading to a solvated coil-like configuration, which is confirmed from the increase in hydrodynamic diameter and zeta potential. The inter-droplet force measurements show an increase in decay length by ~3.5 times, when pH is increased from ~1.5 to ~4.0. The lower decay length at pH ~ 1.5 indicates the formation of linear chain-like structures with smaller inter-droplet spacing near the defect edges, where the magnetic flux leakage is maximum. This results in a fewer number of randomly oriented droplets at the defect centre, thereby enhancing the defect detection sensitivity at lower pH. However, nanoemulsions stabilized at pH > 3 show increased colloidal stability, as compared to pH ~ 1.5. Our studies indicate that nanoemulsions at pH ~ 3.5 exhibit an optimum colloidal stability with good defect detection sensitivity. The obtained results are beneficial for developing high sensitivity, reliable and reusable magnetic nanoemulsion sensors for naked eye visualization of defects in ferromagnetic components.