Pencil Lead Break Tests Research Articles

The Influence of Coupling-Specific Parameters and Structural Parameters on the Electromechanical Impedance of Acoustic Emission Sensors

Acoustic emission (AE) sensors are widely used for testing and monitoring purposes, necessitating effective field-testing methods to assess their functionality during use, ideally resulting in a method for self-diagnostic. A novel approach for field-testing is to measure the electromechanical impedance (EMI) of the AE sensor. This method requires minimal additional equipment and eliminates human interaction, which is needed for current field-tests like pencil-lead break tests. However, the EMI includes the mechanical impedances of the sensor, the coupling layer, and the structure. Therefore, the measurements contain information about all three and influences of the structure and the coupling layer need to be investigated to be able to establish a method for self-diagnosis in field. Additionally, the piezoelectric effect is temperature-dependent, which affects the measured EMI spectra and may result in erroneous assessments of the sensor's condition if not accounted for. This work presents experimental studies on the influence of measuring equipment, environmental temperature, coupling-specific parameters and structural parameters on the EMI of AE sensors. Moreover, numerical simulations have been conducted to provide additional support and to extend the experimental studies of the latter two. To ensure reproducible sensor coupling to a structure, a setup was constructed to couple the sensor to the structure at the same position and with the same force. The thickness of the coupling layer is determined by a small amount of silicon carbide particles with a specific diameter. This setup is also used to examine the effect of structural parameters.

Experimental and numerical investigation of the sensor effect on the acoustic emission during Pencil-lead Break tests on PMMA plates

Pencil leads are broken on PMMA plates at various source-sensor distances to evaluate the influence of the propagation distance and the sensor type on AE signals. The resulting waves are detected using a broadband or a resonant sensor. The ability of five transducers, PKBBI sensor, MICRO80 sensor, MICRO200HF sensor, Nano30 sensor, and WD sensor, to recreate the characteristic forms of plate waves is evaluated. Because of the response spanning between 300 to 400 kHz frequency range, Nano30 and MICRO80 sensors have a bigger magnitude in the S0 mode than in the A0 mode, especially for small sensor-source distances. Their different responses demonstrate why similar test specimens and test settings can provide diverse findings due to the sensor effect, which is significant for the use of AE data in the classification of AE sources. Then, based on the normalization method, we suggest a procedure to acquire equivalent values for the selected descriptors using Laplacian Score and Principle Component Analysis. In order to gain a thorough understanding of the sensor effect through 3D finite element simulation, a comparative analysis is conducted between the perfect contact sensor and the resonant sensor with sensor effect. The sensor effect arises from the convolution of the Fourier Transform of the signal with the sensitivity curve in the frequency domain. By comparing the waveforms in the time and frequency domain, it is useful to determine how the different sensors differ from the AE signals.

Acoustic Emission simulation: assessing the influence of AE source modeling and addressing the issue of dimensionality in the propagation medium

A thorough understanding of simulating Acoustic Emission (AE) requires the source modeling and propagation medium to be considered respectively. Crack initiation and propagation simulation as an AE source can be performed by the Successive Node Release (SNR) method and the Direct Node Release (DNR) method. Both are compared to show how the source model affects the AE signals. The SNR method is commonly used to simulate the crack initiation and propagation. It is necessary to consider the influence of the crack velocity profile on the crack initiation [1]. However, for the DNR method, all nodes are instantaneously released once the critical crack length is attained. Hence, it is not necessary to consider the velocity profile. Instead, it is sufficient to define the average crack velocity. However, the kinetic energy varies when employing different methods, leading to a variance in the source energy. We thus assess the suitable conditions to apply the DNR method to represent crack initiation and investigate the influence of the node release methods on the AE signals. Secondly, a comparative assessment of the Pencil-lead Break test on PMMA plates is conducted using both two-dimensional (2D) and three-dimensional (3D) finite element simulations. This analysis is valuable for understanding the deficiencies and constraints of the 2D simulation. However, there is a significant difference in the AE signals between 2D and 3D simulations, suggesting that the 2D simulation is unable to capture the AE information during wave propagation, in spite of its lower computational cost. Therefore, to obtain extensive and precise information from the PLB test, it is crucial to have an extensive understanding of the limits of the 2D simulation. This understanding should take into account factors such as the distance between the sensor and the source, as well as the descriptors chosen to characterize the AE.

A Hybrid GAN-Inception Deep Learning Approach for Enhanced Coordinate-Based Acoustic Emission Source Localization

In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies.

A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids.

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time.

A Proposed Non-Destructive Method Based on Sphere Launching and Piezoelectric Diaphragm.

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The pencil-lead break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection.

Electromechanical Impedance of Acoustic Emission Sensors used for Self-Diagnosis

Acoustic Emission (AE) is a well established NDT method which is used more and more for continuous monitoring, heading towards SHM. Today, AE sensors in the field are tested with a pencil lead break test (Hsu-Nielsen source), which requires manual interaction. Sensors are replaced if the sensitivity is too low. However, since the test is performed on the monitored structure, the structure itself may influence the test. Therefore, a detailed check of the sensor in the lab, according to SE02 and ISO 24543, is done afterwards. An automated solution, that can test AE sensors in the field, without human interaction, that needs minimal additional hardware would benefit AE based monitoring systems. The electromechanical impedance (EMI) spectrum is mostly used for local damage detection with piezoelectric transducers. In the past it has been shown that the EMI spectrum can also be used to enable automated self-diagnostic capabilities in the low frequency spectrum (Park et al., 2004) and in the higher frequency regime (Mueller, 2017) including the eigenfrequencies of the transducers. The usage of EMI for AE sensors can, thorough research in this matter provided, solve the issues mentioned above. This work presents results of EMI spectrum analysis of two sets of 12 and 17 sensors, respectively, with different levels of damage from pressure vessel burst tests. The results are compared to that of a face-2-face inspection, which is an established method of verification in the lab according to ISO 24543. It is shown that the EMI spectrum is capable of similar self-diagnostic capabilities. Thus, combining the advantages of global monitoring with AE sensors, while providing a remote and automatic method of self-diagnostic of AE sensors at any point with minimal additional hardware and software requirements.

Acoustic emission onset time detection for structural monitoring with U-Net neural network architecture

Acoustic Emission (AE) is a non-destructive structural health monitoring technique, which studies elastic waves emitted during crack formation. Utilizing piezoelectric sensors, these waves are converted into electrical signals for subsequent analysis, offering insights into crack propagation and structural durability. This study focuses on the identification of AE signal onset times, crucial for determining crack locations. Conventional methods often encounter challenges with background noise, prompting the need for innovative approaches. Leveraging a U-Net neural network, specialized in segmentation tasks, onset time identification is approached as a one-dimensional segmentation challenge. Through training and testing on Pencil Lead Break (PLB) test data, commonly used in AE evaluations, the effectiveness of the method is demonstrated even with continuous signals, suggesting potential applicability in real-time monitoring.

Lamb wave-based corrosion source location on a plate of magnesium alloy WZ73 using the acoustic emission technique

Corrosion on a plate of magnesium alloy WZ73 in contact with 0.001 mol/L NaCl solution was analysed by long-range testing using the acoustic emission (AE) technique. At a distance d = 1.66 m to the source, the Lamb wave propagation of resulting AE events was examined and used for corrosion source location. As a first step, pencil lead break test was carried out and corresponding Lamb wave analysis served as reference to the varying corrosion signals of lower intensity. Additionally, the plate was excited by a piezo transducer for imitating a selected corrosion event. For analysis of the propagating wave field, the plate surface was measured by a scanning laser Doppler vibrometer (SLDV), allowing a high-resolution corrosion signal reconstruction and path tracking of direct and reflected Lamb wave packets. Based on the SLDV results, a deeper understanding of the complex wave field propagation during the sensor-based corrosion analysis was achieved.

STUDY ON ACOUSTIC BLACK HOLE EFFECT OF ACOUSTIC EMISSION SIGNALS IN PINUS SYLVESTRIS VAR. MONGOLICA LITV

The difference in density and wave velocity causes distinct wave impedance between air and wood, resulting in complex acoustic emission (AE) signals due to reflection on the wood's surface. This study explores the suppression of AE signal reflection by modifying the structure of thin wood panels, utilizing the theory of acoustic black holes (ABH). Initially, aone-dimensional ABH structure was created by forming a wedge structure on one side of thespecimen. Pencil-lead break (PLB) tests simulated sudden AE sources on the specimen's surface. AE signals were collected using three equidistant sensors on the upper surface, with asampling frequency of 2MHz. The AE signal was then segmented into frequency bands using the differential method and analyzed in both time and frequency domains. Comparisons were made to understand the impact of the one-dimensional ABH on AE signal propagation. Results demonstrated that the one-dimensional ABH effectively suppressed AE signal reflection on thewood's surface, reducing the high-frequency components by 18.31%, 20.83%, and 12.09% for each sensor, respectively. Furthermore, the experimental cut-off frequency of 0.98 kHz surpassed the theoretically calculated value of 0.39 kHz due to the disparity between the ABH structure's thickness and the theoretical prediction.

Research on the detection of the hole in wood based on acoustic emission frequency sweeping

In order to detect the hole defect in the wood, the intrinsic frequency of wood is investigated by frequency sweeping and combining with pencil-lead break (PLB) tests to examine the influence of the hole defect on the intrinsic frequency to obtain the significant change index for non-destructive detection of the holes. Initially, the sweeping frequency test is conducted in the range of 10–180 kHz on two different sizes of Zelkova schneideriana and Pinus sylvestris var. mongholica specimens, followed by the signal's frequency domain analysis to determine the natural frequency and its range. Then, holes with diameters of 5, 10, and 20 mm are drilled on the Zelkova schneideriana specimens to explore the impact of hole diameter on the natural frequency using PLB and frequency sweeping. Finally, 1, 3, and 5 holes of 8 mm are drilled to investigate the influence of hole number on natural frequency. The results showed that the natural frequency of wood falls within two frequency ranges of 30–50 kHz and 150–180 kHz. In addition, the hole hinders the AE signal's propagation. The energy decay rate in the two natural frequency ranges increases as the diameter and number of holes rise. In addition to the traditional method for calculating signal propagation speeds, this study proposes effective measures for detecting holes in wood.

Investigation of Acoustic Emission Wave Propagation Pattern with Defect Locations in a Multilayer Structure

Acoustic emission (AE) is used to monitor conditions of various structures across many industrial sectors, including containment vessel or storage tank of nuclear materials. Periodic monitoring, inspection, and analysis of structure conditions can help prevent failure and accidents. Understanding the transient elastic waves in multi-layered structures (planar or rounded types) has long been of great interest. This paper experimentally investigates changes in AE wave propagation patterns in multilayer planar structures (detecting and assessing the effect of coating layers, assumed surrogate of deposits or protective layer). Epoxy phenolic coated two mild steel plates were assembled (without any adhesion), and two piezoelectric AE sensors were placed on the coating layer. The pencil lead break (PLB) test was used to initiate the AE waves from the surface and cross-section of different layers. From wavelet transforms (WT) analysis, significant energy zone changes were observed up to the 450 kHz frequency level with PLB on the surface and cross-section of different layers. Love wave propagation on the coated plate structure resulted in wave pattern changes with PLB locations and layers. Wave duration, energy, energy ratio, and peak amplitude levels were also analysed to characterise the AE wave pattern relationship with defect location in a multilayer plate-like (planar) structure. The approaches used in this work could potentially be useful in providing a greater understanding of defects within multilayer nuclear containment structures, and also offering an alternative way to monitor corrosion related degradation of structures with insulations.

Study the effects of ferrous materials inside wood on the propagation characteristics of acoustic emission signals

ABSTRACT This study compares the propagation characteristics of acoustic emission signals for wood containing ferrous material by implanting ferrous nails into wood specimens to investigate the acoustic emission (AE) detection method of foreign metal objects inside the wood. It obtains specimens for the comparative test by drilling holes and implanting ferrous nails into identical Zelkova schneideriana and Pinus sylvestris var. mongholica specimens. Initially, the AE source is generated by conducting pencil-lead break (PLB) tests on the specimen surfaces according to ASTM standards, and the AE signals are then collected at the specimen surface with a 1 MHz sampling frequency. The effects of ferrous materials on the propagation characteristics of AE signals are examined using FFT and EWT methods for the time–frequency analysis of the original AE signals. Finally, the signal generator produces the 150 kHz pulse train at the PLB location. The AE signal energy is calculated based on the alternating current principle to establish the corresponding AE energy attenuation function. The results showed that the proportion of AE signals in the frequency ranges of (30–60 kHz) and (150–180 kHz) increases significantly when ferrous nails are present in the specimens.

Experimental characterization, modelling and simulation of ae signal due to crack propagation in PMMA

Acoustic Emission (AE) is a non-destructive instrumentation that allows the detection of cracking, which dissipates elastic waves resulting from local crack-induced displacement jumps within the material. However, based on the AE experiments, a link between the crack fracture mechanics and the AE features is difficult to establish quantitatively, especially under quasi-static and dynamic loading conditions. Numerical simulations have proved to be very promising for this purpose. In this study, Pencil-Lead Break (PLB) tests are carried out on PMMA plate specimens with different thicknesses in order to determine the damping characteristics of PMMA and experimentally validate the AE numerical simulations. The influence of the specimen thickness, the distance between the sensor/source and the type of sensors on the measured acoustic emission are also studied.

Application of Pencil Lead Break (PLB) Point Source in the Detection of Interfacial Defects in Adhesive Bonds

The presence of kissing bonds (zero-thickness disbond) along the interface of an adhesive bond is highly detrimental to its strength and longevity. The detection of these kind of defects has previously been attempted using several techniques such as ultrasonic, infrared thermography, and X-ray spectroscopy, etc. This study aims to assess the effectiveness of pencil lead break (PLB) tests as a source in detecting the defects distributed along the interface of an adhesive bond. The defects were introduced artificially using polytetrafluoroethylene (PTFE) spray along one of the interfaces of the adhesive bond fabricated with aluminium plates bonded with an epoxy adhesive. Three different interfacial defect area percentages, 0%, 25% and 40% and three adhesive layer thicknesses (i.e., 0.1 mm, 0.25 mm, and 0.5 mm) were considered. The PLB tests were conducted, and the recorded signals were analysed to assess the variation of AE features with the defect area percentage and adhesive layer thicknesses. Different source-sensor location configurations were also considered. The 200 kHz-highpass component of the recorded signals was found to be sensitive to the presence of the interfacial defects. The duration above a chosen threshold was found to be the distinguishing factor between the different defective specimens. Of the different sensor-source configurations tried, the configurations with the PLB on the 0.5 mm side were seen to be sensitive to the presence of defects.

Verification of 3D AE Source Location Technique in Triaxial Compression Tests Using Pencil Lead Break Sources on a Cylindrical Metal Specimen

Recently, the acoustic emission (AE) technique has been widely applied in the field of geotechnical engineering. One of the main applications of this technique is to locate damage sources, which is known as the AE source location technique. In this research, the 3D AE source location technique based on the time difference of arrival is verified by conducting pencil lead break (PLB) tests on a cylindrical metal specimen. From the results of this study, the existing method is proven to have the least error in terms of distance from the coordinate origin, but with some errors along x, y, and z axes. When the PLB sources originate in the middle part of the specimen, the calculated result has a higher accuracy, as compared to other positions. It seems that the calculated AE sources tend to be concentrated on the central part with some errors. Moreover, outside noises induced by a hammer hit have virtually no effect on this AE source location technique.

Damage degree evaluation of masonry using optimized SVM-based acoustic emission monitoring and rate process theory

Evaluation of masonry damage is considerably challenging because of the non-homogeneous nature of masonry materials and the rapid attenuation of signals. To this end, a methodology combining qualitative analysis employing support vector machine-based acoustic emission (AE) monitoring and quantitative analysis using AE rate process theory was proposed. In this work, firstly, the propagation attenuation characteristics of the AE amplitude and frequency were studied using pencil-lead break tests. The hybrid PSO-SVM model was employed to automatically identify the damage stages of the AE signals based on the field in-situ axial compression test data. Then, laboratory compression and shear tests on small-scale specimens were performed with AE monitoring. The Boltzmann sigmoidal modified function between relative stress and AE hits was established. Based on qualitative and quantitative analysis, a damage index-based early warning range was proposed, which can help to decide the damage level of masonry structures. The results of this paper can provide some new insight for the analysis of masonry damage.

Design and development of a high-speed data acquisition system for acquiring acoustic emission signals

One of the most promising non-destructive testing method suitable for in-situ detection of damages in structures and materials is Acoustic emission (AE) testing. AE waveforms can be related to various source mechanisms using suitable signal processing techniques and are characterized by low amplitude, high frequency and short duration. Data acquisition system to capture and analyze such high frequency range AE signals are expensive. One of the key components in such commercial systems are the signal conditioning units. Thus, in the present work, a signal conditioning unit (SCU) is designed and developed to process the AE signals. Further, a low-cost AE signal data acquisition system was developed by using the fabricated signal condition unit. Signals from the data acquisition system was saved using Python libraries for further analysis. The developed system was tested at lab-scale by acquiring the signals generated during pencil lead break tests on a stainless steel and composite plates. The performance of the in-house developed AE data acquisition system has been compared with the commercially available one and the comparison results are presented in this paper.

All-phase fast Fourier transform and multiple cross-correlation analysis based on Geiger iteration for acoustic emission sources localization in complex metallic structures

Nowadays, the localization and identification of acoustic emission (AE) source is widely utilized to structural health monitoring (SHM) of complex metallic structures. However, traditional AE source localization methods are generally difficult to localize and characterize AE sources in plate-like structure that has complex geometric features. To alleviate the problem, a novel AE source localization method based on all-phase fast Fourier transform and multiple cross-correlation analysis is proposed in this article. Moreover, least squares and Geiger iteration algorithm are applied to determine the coordinates of AE sources. In addition, an improved Bayesian information criterion (BIC) version named autoregressive BIC (i.e., AR-BIC) is presented to increase the accuracy of source localization. To validate the performance of the proposed approach, the classical pencil lead break tests are carried out on a 316 L stainless steel with 10 laser cladding layers. Experimental waveforms are generated from AE sources near laser cladding layers, the surface of the structure, and on its edges. Additionally, to evaluate the performance of the proposed approach in three-dimensional AE source localization, an industrial storage tank is used to acquire three-dimensional AE sources through manually striking. Finally, to further verify the effectiveness of the proposed approach, comparisons with conventional AE source location methods (i.e., PAC or SAMOS AE acquisition system, Newton’s method, and multiple cross-correlation based on Geiger algorithm) and two representative approaches (i.e., deep learning and Bayesian methodology) for localizing AE sources generated by complex metallic structures are conducted. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM of complex metallic structures.

Defect localization on rolling element bearing stationary outer race with acoustic emission technology

Accurate rotational speed information is typically required to diagnose bearing failures under variable speed conditions. This paper proposes a fault localization method to obtain the defect position on a bearing based on the spatial localization of acoustic emission (AE). Firstly, a localization model independent of accurate wave propagation velocity is established for the annular structure of the bearing. Secondly, the Akaike Information Criterion method is used to calculate the arrival time of the AE signal; the characteristic function is used for the measured signal with low signal-to-noise ratio to improve the calculation accuracy of the arrival time. Lastly, the effectiveness of the method in the fault diagnosis of rolling bearings is verified by pencil lead break tests and dynamic tests using a thrust rolling bearing. The results of the experiments demonstrate that this method can locate AE sources. Future work will involve the implementation of the proposed method on larger bearings and verification of the accuracy in industrial machines.