Detection Of Defects In Materials Research Articles

Deep Learning and Long-Duration PRPD Analysis to Uncover Weak Partial Discharge Signals for Defect Identification

This study focuses on improving the defect recognition accuracy under weak partial discharges (PDs) in epoxy resin through phase-resolved partial discharge (PRPD) analysis. The method is to refine the data rather than enhance the algorithm. Two measurement conditions are compared until PRPD pattern saturation is achieved: one-minute and one-hour durations. The PD data specifically target three void types located at different positions within the epoxy material. The aim is to evaluate how the presence of weak PDs at the PD extinction voltage (PDEV) influences defect recognition accuracy. This research sheds light on the potential implications of neglecting the significance of weak PD signals in defect detection. A convolutional neural network (CNN) model is trained using PRPD data recorded at the repetitive PD inception voltage (RPDIV) and tested using the new PRPD data from both conditions recorded from a lower PDIV to a PDEV. The trained CNN model achieves a defect recognition accuracy of 100% for a one-hour duration, highlighting the importance of not neglecting weak PD signals. This emphasizes the significance of extended measurement duration and pattern saturation in capturing and analyzing weak PD signals for an improved defect recognition. This study contributes to the advancement of practical applications by understanding the behavior of the epoxy material and enhancing defect detection techniques.

Defect-depth-field algorithm for simulating magnetic flux leakage signals based on discrete magnetic dipole model

Magnetic flux leakage (MFL) testing is widely used for non-destructive detection of defects in ferromagnetic materials, in which fast and accurate processing of MFL signals is critical for reconstructing and evaluating the defects. Of the detection technologies, the existing magnetic dipole model (MDM) can simulate the MFL signals with regular or limited types of defects, but not with complex defects, such as naturally occurring corrosion material losses. With the help of finite element method (FEM), MFL signals of complex defects can be analysed but demanding time-consuming calculation. In this paper, an improved MDM method, i.e., the discrete MDM (DMDM) is proposed, and an algorithm for automatic MFL signals processing is further developed based on the DMDM and the defect-depth-field concept. By comparison, the difference of the simulated MFL signals between the DMDM and the FEM is less than 3%, and the maximum discrepancy of the MFL signals between the simulated and the experimental is about 6%. In addition, the simulation with the DMDM can be thousands of times more efficient than the FEM, indicating significant advantage over the existing method in both improving the accuracy and enabling the fast signal processing of reconstruction for any types of defects in MFL testing.

Research on the Forward Solving Method of Defect Leakage Signal Based on the Non-Uniform Magnetic Charge Model.

Pipeline magnetic flux leakage inspection is widely used in the evaluation of material defect detection due to its advantages of having no coupling agent and easy implementation. The quantification of defect size is an important part of magnetic flux leakage testing. Defects of different geometrical dimensions produce signal waveforms with different characteristics after excitation. The key to achieving defect quantification is an accurate description of the relationship between the magnetic leakage signal and the size. In this paper, a calculation model for solving the defect leakage field based on the non-uniform magnetic charge distribution of magnetic dipoles is developed. Based on the traditional uniformly distributed magnetic charge model, the magnetic charge density distribution model is improved. Considering the variation of magnetic charge density with different depth positions, the triaxial signal characteristics of the defect are obtained by vector synthesis calculation. Simultaneous design of excitation pulling experiment. The leakage field distribution of rectangular defects with different geometries is analyzed. The experimental results show that the change in defect size will have an impact on the area of the defect leakage field distribution, and the larger the length and wider the width of the defect, the more sensitive the impact on the leakage field distribution. The solution model is consistent with the experimentally obtained leakage signal distribution law, and the model is a practical guide by which to improve the quality of defect evaluation.

Ultrasonic defect detection in noisy signals by a nonconvex sparse regularization approach

Ultrasonic testing has been an important tool for the detection of hidden defects in materials yet its effectiveness is usually compromised by the noise originated from both the testing system and the material being evaluated. Motivated by the prior knowledge that only a small number of defects or reflectors exist within the tested material thus the measured signal can be viewed as a linear combination of a few echoes, a signal processing method is proposed in this study where a nonconvex sparse regularization method based on lp-norm (0 < p < 1) penalty is employed to suppress noises in ultrasonic NDT signals thus enhancing the effectiveness and accuracy of flaw detection. Based on a specially designed over-complete Gabor dictionary, the nonconvex sparse regularization method is introduced to sparsely represent the noisy signal. After signal representation, noise is suppressed by a pruning operator, facilitating the identification and reconstruction of flaw-reflected echoes. The performance of the proposed method is quantitatively evaluated and compared with competing algorithms using simulated noisy ultrasonic signals in a statistical manner. Experimental results are also presented to demonstrate the effectiveness of our method.

Defect Detection and Depth Estimation in Composite Materials for Pulsed Thermography Images by Nonuniform Heating Correction and Oriented Gradient Information.

Pulsed thermography is a nondestructive method commonly used to explore anomalies in composite materials. This paper presents a procedure for the automated detection of defects in thermal images of composite materials obtained with pulsed thermography experiments. The proposed methodology is simple and novel as it is reliable in low-contrast and nonuniform heating conditions and does not require data preprocessing. Nonuniform heating correction and the gradient direction information combined with a local and global segmentation phase are used to analyze carbon fiber-reinforced plastic (CFRP) thermal images with Teflon inserts with different length/depth ratios. Additionally, a comparison between the actual depths and estimated depths of detected defects is performed. The performance of the nonuniform heating correction proposed method is superior to that obtained on the same CFRP sample analyzed with a deep learning algorithm and the background thermal compensation by filtering strategy.

Two-stage convolutional neural network for joint removal of sensor noise and background interference in lock-in thermography

Lock-in thermography provides a fast, low-cost, and accurate non-destructive testing for subsurface defect detection in materials. However, there is serious sensor noise and background interference presented in the infrared images captured by lock-in thermography. In this paper, we set up lock-in thermography experiments to analyze the characteristics of sensor noise and background interference. Based on their different characteristics, we design a two-stage CNN model for the joint removal of sensor noise and background interference. This model consists of a number of multi-direction feature extraction blocks (MDFEBs) to extract local features in different directions for pixel-level sensor noise removal, and an encoder–decoder architecture with large receptive fields to extract global features for background interference correction. Experimental results demonstrate that our proposed method outperforms the well-established infrared image denoising and non-uniform background correction techniques on specimens made of different materials and at various excitation frequencies. The proposed two-stage CNN model provides high-quality input of infrared images for the following subsurface defect detection task in lock-in thermography.

Deep learning based atomic defect detection framework for two-dimensional materials

Defects to popular two-dimensional (2D) transition metal dichalcogenides (TMDs) seriously lower the efficiency of field-effect transistor (FET) and depress the development of 2D materials. These atomic defects are mainly identified and researched by scanning tunneling microscope (STM) because it can provide precise measurement without harming the samples. The long analysis time of STM for locating defects in images has been solved by combining feature detection with convolutional neural networks (CNN). However, the low signal-noise ratio, insufficient data, and a large amount of TMDs members make the automatic defect detection system hard to be applied. In this study, we propose a deep learning-based atomic defect detection framework (DL-ADD) to efficiently detect atomic defects in molybdenum disulfide (MoS2) and generalize the model for defect detection in other TMD materials. We design DL-ADD with data augmentation, color preprocessing, noise filtering, and a detection model to improve detection quality. The DL-ADD provides precise detection in MoS2 (F2-scores is 0.86 on average) and good generality to WS2 (F2-scores is 0.89 on average).

Excitation of Mechanical Resonances in the Stationary Ring of a Mechanical Seal by a Continuously Operated Electromagnetic Acoustic Transducer.

Acoustic/ultrasonic testing is now a common method in the field of nondestructive testing for detecting material defects or monitoring ongoing mechanical changes in a structure during operation. In many applications, piezoelectric transducers are used to generate mechanical waves inside the specimen. Their actual operating frequency is highly dependent on the dimensions of the transducer. Larger dimensions of the piezoelectric transducer allow for a lower operating frequency. However, these dimensions limit the use of piezoelectric transducers in certain applications where the size of the transducer is restricted due to limited installation space and when low-frequency excitation is required. One application that places these requirements on the transducer is the monitoring of mechanical seals. Here, the transducer must be mounted on the stationary ring of the seal. In this paper, a continuously operated electromagnetic acoustic transducer (EMAT) is presented as an alternative to piezoelectric transducers as a transmitter. The advantage of a EMAT is that it meets the requirements of limited sensor size (sensor area < 10 × 6 mm) and can excite mechanical waves with frequencies below 10 kHz. A structural analysis of the stationary ring shows that the first two mechanical resonances occur around 4 and 5.5 kHz. An experimental study meterologically demonstrates the ability of the EMAT to excite these first two mechanical resonances of the ring. A comparative simulation agrees well with the measurement.

Detection of subsurface defects in silicon carbide bulk materials with photothermal radiometry

&lt;sec&gt;With excellent physical, mechanical and processing properties, silicon carbide (SiC) has gradually become a preferred lightweight optical material for primary mirrors of large space optical systems. The subsurface defects generated during the preparation and processing procedures of SiC will affect the optical quality of the primary mirrors and the imaging performance of the corresponding optical systems employing the SiC primary mirror as well. In this work, photothermal radiation (PTR), a powerful nondestructive testing technique for detecting sub-surface defects of solid materials, is employed to characterize the subsurface defects of bulk SiC material for primary mirrors.&lt;/sec&gt;&lt;sec&gt;Theoretically, three-dimensional one-layer and three-layer PTR theoretical models are developed to describe the defect-free and defect regions of an SiC bulk material. By analyzing the frequency dependence of PTR phase of the SiC bulk material with different defect depths, an empirical formula for estimating the defect depth via a characteristic frequency (appearing at the minimum of the PTR phase-frequency curve) defined thermal diffusion length is proposed, and simulation results show reasonably good agreement between the estimated and simulated defect depths in a depth range of 0.05–0.50 mm. Experimentally, an SiC bulk sample with a subsurface defect region is tested by the PTR via position scanning and modulation frequency scanning to obtain the position and frequency dependent PTR amplitude and phase. From the spatial distributions of PTR amplitude and phase measured at different frequencies and the phase difference frequency curves of measurement positions in the defect region, the depth and shape of the defect region are estimated and found to be in good agreement with the actual shape of the defect region, which is destructively measured via a depth profiler. The experimental and calculated results demonstrate that the PTR is capable of detecting non-destructively the subsurface defects of SiC bulk material. In addition, for subsurface defects with relatively flat interface, the defect depth can be determined accurately by the developed empirical formula.&lt;/sec&gt;

Crack detection in upsetting of aluminum alloy using acoustic emission monitoring technology

Acoustic emission technology relies on detecting and converting elastic (acoustic) waves from solids undergoing internal structural irreversible changes into electrical signals. This technique represents a non-destructive testing method used in industrial fields such as weld monitoring, proof testing, and flaw detection of pressure vessels. In this study, the applicability of acoustic emission technology for detecting and monitoring defects in a workpiece during a metal forming process was studied. A notch shape was designed using the finite element method to induce a premature crack on the surface of aluminum alloy cylindrical specimens by compressing them using a pair of flat dies. During the upsetting tests, acoustic emission signals and images of the notched specimen were recorded in real time. The obtained acoustic emission signals were analyzed through various parameters such as amplitude, energy, and RMS to determine the crack occurrence. Moreover, the signal results were compared with those from images obtained simultaneously to confirm the crack detection. The experimental results showed that acoustic emission technology could successfully detect crack occurrence in aluminum materials during the upsetting tests. These results imply an opportunity to detect material defects during various metal forming processes using acoustic emission technology.

Dark-field structured illumination microscopy for highly sensitive detection of 3D defects in optical materials

Precise three-dimensional (3D) inspection of surface and subsurface defects for optical components has become more and more urgent in high-power laser systems. In this paper, a dark-field structured illumination microscopy (DSIM) is proposed for 3D defect detection method in optical components. This method improves the axial resolution by structured light modulation, and enhances the defect imaging sensitivity by a high-pass spectrum filter placed at the spectrum plane of the microscopic system. Experiments prove that our method performs well on detecting sensitivity and axial resolution. It can measure the 3D defects of both flat and curved optical components, and the detection depth range is wide enough to cover the subsurface defect depth of polished optics. Therefore, the DSIM provides the possibility for a more accurate and complete evaluation of optical components’ quality.

Optimal slope angles of the air-coupled transducer from viewpoint of the effective Lamb waves propagation in different materials

Air-coupled transducers (ACT) allow for non-contact generation of acoustic waves which can be later transferred into elastic waves due to interactions with the solid specimen. Proper slope of the ACT in relation to the investigated specimen surface during acoustic waves generation is important as it leads to the possibility of generation of desired Lamb wave modes in the form of elastic waves with selected propagation velocity values. Effective generation of these modes ease the analysis of elastic waves propagation in specimens, simultaneously helping in detection of material defects if they exist. The attempt to determine optimal slope angles was undertaken in case of aluminum (Al), carbon fiber-reinforced polymer (CFRP) and glass fiber-reinforced polymer (GFRP) plate-like structure. Due to relatively low excitation frequency of the used ACT (40 kHz) the investigation was focused on the slope angles referred to the generation of zero-order modes - antisymmetric A0 and symmetric S0. In CFRP plate case the issue of efficient generation of shear horizontal (SH0) wave mode was additionally considered. In Al and CFRP plate case optimal slope angles were considered numerically (Comsol, Matlab) and experimentally (lab investigations, Matlab) while in GFRP plate case they were estimated experimentally. Although not all of the slope angles were successfully determined within different approaches, the results helped to form some interesting conclusions. After determination of the slope angles the performance of sloped/non-sloped ACT was compared in full wavefield measurements (comparison of signal energy maps calculated with the usage of root mean square (RMS) formula). Recommendations for further work were depicted within the summary section.

A new magneto-acousto-electrical detection method for defects in high conductivity non-ferromagnetic materials

In order to further enrich the non-destructive detection methods for defects in high conductivity non-ferromagnetic materials, a new method of magneto-acousto-electrical (MAE) defect detection is proposed. First, a mathematical model of MAE multi-physical field coupling with coil induction was established. Subsequently, a geometric model of the object to be tested was constructed, and the simulation software COMSOL was used to solve the physical process of MAE defect detection. Two-dimensional distributions of mass point vibration velocity, current density of the coil’s cross-section, and one-dimensional curves of material vibration velocity surface integral, local current, and induced voltage as a function of time were obtained for various defect widths, defect shapes, and materials. The simulation result shows that the induction coil voltage pulse contains abundant information about the defect boundary information and the detection accuracy reaches 0.1 mm. Finally, experiments on MAE defect detection were conducted to prove the feasibility of the method. The research demonstrates a reliable method for the detection of internal defects in non-ferromagnetic materials with high electrical conductivity, such as aerospace and railways.

A dual-scale morphological filtering method for composite damage identification using FBP

Delamination is the main type of defect that reduces the performance of carbon fiber-reinforced polymer (CFRP) composite materials and can be detected by traditional linear ultrasonic methods. However, the traditional ultrasonic inspection method only analyzes its position through the waveform change, and it is difficult to intuitively and accurately study the specific shape and position of the defect in the form of images. This study obtains delamination defect images of CFRP composite plates through fan-beam ultrasonic computed tomography (UCT) and finite element simulation. The Filtered back-projection method based on UCT is used as the basic algorithm for reconstructing layered defect images of CFRP plates. To obtain accurate defect size and location, a novel dual-scale morphological filtering (DSMF) method combined with Canny operator is proposed for edge detection of delamination defects. The DSMF method alternately filters by combining two structuring elements of different sizes while suppressing noise as much as possible and preserving the edge details of the image. As the results verify, the use of the DSMF method combined with Canny edge-detection for edge detection significantly reduces the average recognition error from 16.83% to 4.37% compared to existing methods. The results show that the DSMF method can effectively reduce the influence of artifact interference and non-edge pixels on defect-recognition; thus, the size and contour information of the obtained defects are more accurate than existing methods. Experimental studies using realistic composite plates further validated the proposed method. The method of ultrasonic tomography combined with Canny edge detection based on a DSMF method can be well applied to edge detection and contour recognition of layered defects in CFRP materials.

Magnetic nanoemulsion aided optical defect detection in carbon steel components: Effect of defect width variation on optical contrast

Optical detection of defects of varying widths in ferromagnetic steel components is carried out using polyacrylic acid stabilized magnetic nanoemulsions (MNEs). Magnetic field lines are leaked from the defect regions, due to permeability variation, when a defective ferromagnetic steel component is magnetized. Under the influence of such magnetic flux leakage (MFL), the MNE droplets exhibit orientational ordering, where the inter-droplet separation within the chains varies with the strength of the magnetic field. Exploiting this tunable optical grating phenomenon, wide area naked eye visualization of the surface opening and buried defects is carried out. The defect morphology is accurately estimated using a double thresholding-based image processing technique. The optical contrast varies non-monotonically with defect width for the surface opening defects, which is due to the formation of the surface dipoles on the defect edges. The surface dipoles are strongly coupled for lower defect width due to smaller spatial separation, which causes the MFL to be lower. With increasing defect width, the surface magnetic field lines are diffused leading to a lowering of the MFL. In between, for ∼2 mm wide defect, MFL is the highest and the generated optical contrast is found to be the strongest. The MFL profiles are numerically estimated using finite element modeling and experimentally verified using a Hall probe. The cross correlation between the generated optical contrast and MFL magnitudes clearly demonstrates the non-monotonic effect of defect width on the optical contrast of the MNE-based sensors for large area naked eye non-destructive defect detection in ferromagnetic materials.

Evaluation of Fracture Strain for Cold Drawn Thin-Walled Steel Tubes via Small Round-Bar Tensile Test

The evaluation of tube burring formability is a crucial task for finding a suitable material for tube-based automobile parts. The local strain at the ductile fracture site (fracture strain) should be evaluated for this purpose. Moreover, a cold-drawn steel tube has a strong anisotropic shaped microstructure and possibly causes anisotropic fracture strain behavior. Based on this background, the study evaluated the axial and hoop directional fracture strains of cold-drawn steel tubes using the small round-bar tensile specimen. The burnished surface ratio on the pierced surface was also investigated for possibility estimation of in-line formability inspection. As a result, three tubes are presented with inferior, nearly the same, and superior hoop directional fracture strains compared with the axial strains, where exceeding 40% deterioration in the hoop direction occurs by a combination of grain elongation and carbide aggregation. The scanning electron micrographs suggest that the microvoid growth and linkage percolated thorough carbides on the elongated grain boundaries. For the piercing test, the 30% fracture strain deterioration resulted in a 4% decrease in the burnished surface ratio on the pierced surface. This result suggested that the estimation of the pierced surface can detect material defects before the actual tube-burring process.

Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing.

We address the detection of material defects, which are inside a layered material structure using compressive sensing-based multiple-input and multiple-output (MIMO) wireless radar. Here, strong clutter due to the reflection of the layered structure’s surface often makes the detection of the defects challenging. Thus, sophisticated signal separation methods are required for improved defect detection. In many scenarios, the number of defects that we are interested in is limited, and the signaling response of the layered structure can be modeled as a low-rank structure. Therefore, we propose joint rank and sparsity minimization for defect detection. In particular, we propose a non-convex approach based on the iteratively reweighted nuclear and -norm (a double-reweighted approach) to obtain a higher accuracy compared to the conventional nuclear norm and -norm minimization. To this end, an iterative algorithm is designed to estimate the low-rank and sparse contributions. Further, we propose deep learning-based parameter tuning of the algorithm (i.e., algorithm unfolding) to improve the accuracy and the speed of convergence of the algorithm. Our numerical results show that the proposed approach outperforms the conventional approaches in terms of mean squared errors of the recovered low-rank and sparse components and the speed of convergence.

Defect imaging in carbon fiber composites by acoustic shearography

Carbon fiber reinforced polymer (CFRP) composites are increasingly used in aerospace, automobile, marine and power industries due to its good mechanical strength and low material density. The defect detection in CFRP has been challenging because of their complex material configuration and various types of defects. In this paper, we reported the use of acoustic shearography imaging for defect detection in carbon fiber composite materials. CFRP specimens with different levels of defects were prepared by open-hole compression tests according to ASTM standard. These specimens were subsequently tested with acoustic shearography imaging method. It is found that acoustic shearography can successfully image various defects efficiently. By synchronizing the stroboscopic laser with the ultrasonic wave frequency, the defect imaging quality can be significantly improved. Comparison with X-ray computed tomography results shows that acoustic shearography can provide sufficiently good defect imaging results with significantly reduced imaging time.

Deep Learning-Based Defect Detection From Sequences of Ultrasonic B-Scans

Ultrasonic testing (UT) is one of the commonly used non-destructive testing (NDT) techniques for material evaluation and defect detection. The acquisition of UT data is largely performed automatically by using various robotic manipulators which can ensure the consistency of the recorded data. On the other hand, complete analysis of the acquired data is still performed manually by trained personnel. This makes the reliability of defect detection highly dependent on humans’ knowledge and experience. Most of the previous attempts for automated defect detection from UT data analyze individual A-scans. In such cases, valuable information present in the surrounding A-scans remains unused and limits the performance of such methods. The situation is better if a B-scan is used as an input, especially if the dataset is large enough to train a deep learning object detector. However, if each of the B-scans is analyzed individually, as it was done so far in the literature, there is still valuable information left in the surrounding B-scans that could be used to improve the precision. We showed that expanding the input layer of an existing method will not lead to an improvement and that a more complex approach is needed in order to effectively use information from neighboring B-scans. We propose two approaches based on high-dimensional feature maps merging. We showed that proposed models improve mean average precision (mAP) compared to the previous state-of-the-art model by 2% for input resolutions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> pixels, and 3.4% for input resolutions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$384\times 384$ </tex-math></inline-formula> pixels.

Non-Destructive Evaluation of Mixing and Defects of Friction Stir Welded Dissimilar Aa6061-T6 and Az31b by X-Ray Computed Tomography

As a non-destructive evaluation method for friction stir welded joints, this research aims to develop and corroborate a method for material flow analysis and defect detection based on X-ray computed tomography (X-ray CT). Using a cylindrical FSW tool with a broad shoulder, joints of dissimilar materials AA6061-T6/AZ31b are friction welded employing tool rotary speed ranging from 1000 to 1500 RPM and tool feed from 125 to 400 mm/min. The welded joints are scanned via X-Ray CT with an image bit depth of 16-bit then segmented based on the Hounsfield Units scale (HU) and the global Otsu thresholding method. This segmentation divides the DICOM images into masks for each different material, from which 3D renderings are generated to record volumetric data. For analyzing elemental mixing, measurements of material penetration and transfer are carried out. Corroborating these results was accomplished using destructive cross-sectioning and Energy Dispersive X-ray Spectroscopy (EDX). The results show that this method can detect internal defects and characterize the material mixing with results comparable to that of destructive EDX analysis. The effect of improving scan resolution on reconstructed images was shown to slightly improve the accuracy of the thresholding method while reducing the standard deviation of segmented material ranges.