Pencil Lead Break Research Articles

An adaptive localization method of simultaneous two acoustic emission sources based on energy filtering algorithm for coupled array signal

This paper proposes a novel localization method of simultaneous two acoustic emission (AE) sources to decouple the two coupled AE waves in the structure and obtain more accurate localization results by means of frequency-domain notch-weighted beamforming. Firstly, the notch-wave theory is analyzed and combined with propagation characteristics of AE waves to derive the mechanism and method of the frequency-domain notch-weighted beamforming. After that, the localization process of the simultaneous two AE sources is proposed based on the presented frequency-domain notch-weighted beamforming method. Then, the simulation is performed to obtain the data of two AE sources emitted in different positions with different energies to study the localizing effect of the frequency-domain notch-weighted beamforming method. Finally, the proposed method is verified by pencil-lead-breaking (PLB) test on a steel plate. The research results show that the frequency-domain notch-weighted beamforming method can effectively localize the simultaneous two AE sources.

Acoustic emission source location using Lamb wave propagation simulation and artificial neural network for I-shaped steel girder

Acoustic emission (AE) is often used for structural health monitoring (SHM) in the wide field of engineering structures and one of its most beneficial attributes is the ability to localize the damage/crack based on the AE events. The vast majority of ongoing work on AE monitoring focues on geometrically simple structures or a confined area, but the AE source location strategies are rather complicated for real engineering structures. In this paper, an effective method for source localization in realistic structures is presented based on the application of artificial neural networks (ANN), using finite element (FE) simulation results of Lamb waves as the modelling basis. Pencil lead break experiments and related FE simulations on a steel-concrete composite girder are conducted to evaluate the performance of the method. The identification of different wave modes is carried by comparing alternative onset time detection methods. Numerical results are found to be matching closely with the experimental results. To get a reliable ANN model, the validated FE model is used to create a comprehensive database with five different sensor arrangements. It is found that the proposed method is superior to the classical Time of Arrival (TOA) method with the same input data. The results indicate that using trained neural networks based on numerical data is a viable option for AE source location in the case of the I-shaped girder, increasing the likelihood of design and optimization of the AE technique in monitoring realistic structures.

Differences in the Acoustic Emission Characteristics of 345 MPa Normal-Strength Steel and 460 MPa High-Strength Steel

This study characterized 345 MPa normal-strength steel (NSS) and 460 MPa high-strength steel (HSS) with the acoustic emission (AE) technique. Pencil lead break (PLB) and tensile tests were performed, and the generated AE signals were recorded. The AE signals from the PLB tests were converted into the frequency and time-frequency domains via a fast Fourier transform (FFT) and wavelet transform. The AE signals generated during the tensile process were divided into four stages for analysis. The AE parameters, such as the counts, amplitude, hit and duration, were extracted from the signals. An FFT was conducted to obtain the features of frequency spectrum at each stage. The comparisons revealed the similarities and differences in the AE characteristics between the two steels. It was found that the energy of the AE signals for the 460 MPa HSS was less overall than that of the 345 MPa NSS. The analysis results indicated that the AE technique is a method that can potentially be used to distinguish the two steels, and additional studies for accurate identification are needed. As 460 MPa HSS is being widely used but related AE studies are limited, this study provides data for additional investigations of the damage in HSS structures with the AE method.

Laser-based structural training algorithm for AE localization and damage accumulation visualization in a composite wing skin with various sub-structures

Acoustic emission (AE) is a transient phenomenon in which an elastic wave propagates to a structure as a result of structure damage such as micro-cracks, internal-cracks, friction, fiber breakage and fiber matrix debonding in a structure. AE encompasses a lot of information related to the integrity of the structure. Particularly, AE is used to overcome the difficulties in detecting damages that are hard to detect, such as micro-cracks and internal cracks. Composite structures are widely used in high technology structures due to their high stiffness to weight ratio. However, the source localization in the composite structure has been difficult for years due to variables such as wave propagation direction and speed along the fiber direction. In this paper, we propose laser scan based structural training technique with a single AE sensor (lead zirconate titanate sensor) in a real composite wing skin with a spar and stringers. This technique compensates the difference in characteristics between actual AE and laser-induced elastic wave in the frequency domain using a structural training algorithm. Source localization was performed using cross-correlation based waveform similarity between actual AE and laser-simulated AE A pencil-lead break method, which is a standard method to mimic the AE induced by actual composite damage. The proposed laser scan based structural training technique derive 4.36 mm error distance in average in the actual composite wing skin structure with a spar and stringers. The flexibility of this technique for various amplitude of damaged signals, which induced cracks was verified by using various diameter of pencil-lead. To visualize the accumulated damage, the concept of damage index, which quantify AE is appended at localized point. Finally, we implemented the damage project system to intuitively identify the damage location in large wing skin structures.

냉간압연률이 다른 STS316L의 탄성파 특성

In this study, the elastic wave was detected by the pencil lead break and tensile test using a stainless steel sheet with a thickness of 1 mm transformed by cold rolling. The detected elastic wave was analyzed by the time-frequency analysis method using Labview. The elastic wave detected from the pencil lead break and tension could be characterized by time-frequency analysis using Labview. As the degree of cold rolling increased, the amount of martensite increased and the dominant frequency decreased.

Feasibility of studying 460 MPa high-strength steel affected by strain aging with acoustic emission method

Strain aging significantly influences the behavior of partially damaged structural steel. The effects of strain aging can be determined based on mechanical experiments, but the sampling process causes further damage to the structures. To explore the possibility of using nondestructive testing (NDT) technology to distinguish the strain aging effects on steel materials, this study investigates the characteristics of acoustic emission (AE) signals for high-strength steel (HSS) affected by strain aging. First, different strain aging effects were applied to 460 MPa HSS specimens. Second, pencil lead break (PLB) tests were performed on the specimens with strain aging, and the generated AE signals were recorded. Finally, tensile tests were conducted, that the strain aging effects on mechanical behavior were determined. The obtained AE signals were compared by extracting the AE parameters and analyzed in the frequency and time-frequency domains with a fast Fourier transform (FFT) and a wavelet transform (WT), respectively. The study shows that the strain aging effects change the characteristics of the AE signals. Compared to the prestrain, the aging time has a more pronounced impact on the AE behaviors. This research proposes a possible NDT method to determine the effects of strain aging on steel materials. Experimental data are provided to detect the degree of partial damage to 460 MPa HSS owing to strain aging using the AE method.

Three-dimensional acoustic emission source localisation in concrete based on sparse least-squares support vector regression

In order to improve the accuracy of concrete damage localisation based on acoustic emission (AE) monitoring, a multi-output sparse least-squares support vector regression (S-LS-SVR) method is attempted for AE source localisation in concrete. The AE events are produced by pencil lead breaks and the response wave is received by piezoelectric sensors. A Newton iterative method, an improved exhaustive method and two S-LS-SVR approaches (S-LS-SVR(A) and S-LS-SVR(B)) are used to locate the AE source, then the positioning accuracies of the methods in the three coordinate directions are compared and analysed. The results show that the accuracy of AE source localisation using the S-LS-SVR(B) model is higher than that of the other methods. The accuracy of the S-LS-SVR model using the time difference of arrival (TDOA) and the sequential number of sensors that arrive successively as input parameters is higher than that of the other AE signal combination trialled as the input. This shows that the S-LS-SVR(B) model is better than the S-LS-SVR(A) model. The intelligent S-LS-SVR(B)-based localisation method provides a basis for application in actual damage detection.

Towards a better understanding of the CMUTs potential for SHM applications

The ability of capacitive micromachined ultrasonic transducer (CMUTs) to design broadband sensors for structural health monitoring (SHM) is studied through both multi-frequency and bandwidth aspects. Elementary cells are composed of circular membranes fabricated using the standard MUMPS Process. The multi-frequency aspect, which involves different individual membranes from 50 to 250 μm radius, is theoretically addressed through a numerical modeling. The targeted frequency range, consistent with the SHM application, is then between 80 kHz and 2 MHz. Geometrical features induced by the manufacturing process greatly affect the dynamic properties of the membranes and this is experimentally validated. The bandwidth aspect is also addressed on an array of identical 100 μm radius membranes thus involving their intrinsic capabilities. Harmonic excitation with targeted frequencies 300, 530 and 800 kHz, below and beyond the resonance frequency of the membranes, are performed. The influence of the bias voltage VDC on the signal-to-noise ratio is studied according to the excitation frequency. As a result, a signal-to-noise of 20 dB is achieved around the resonance frequency. Finally, the circular membranes array is tested for acoustic emission sensing through a pencil lead break test. In spite of a low signal-to-noise ratio, acoustic events are clearly detected. The multi-frequency aspect and the large bandwidth capability of the CMUTs are hence demonstrated and highlight the adaptability of the sensor to its environment.

Application of an acoustic emission source-tracing method to visualise shear banding in granular materials

The acoustic emission (AE) technique can be used to locate failure-induced AE sources and provides an alternative to continuously visualise the development of failures inside stressed materials; however, a literature review reveals almost no studies regarding its application for source location in granular materials. Yet the visualisation of ubiquitously observed strain localisation and shear banding is critical to an in-depth understanding of the progressive failure mechanism of granular materials. In this paper, an original AE source three-dimensional (3D) tracing code based on the idea of ‘time difference of arrival’ (TDOA) was developed using Matlab programming, for the first time, to continuously visualise particle-scale interactions involved in saturated granular soils. After validating its feasibility through a series of pencil lead break (PLB) tests, the developed AE source 3D tracing code was applied to saturated granular soils subjected to drained triaxial shearing. Both the results of PLB tests and drained triaxial shearing tests demonstrate a good consistency between the traced AE source and actual PLB/particle interaction-induced sources. This suggests that the developed AE source 3D tracing code could be used to visualise the initiation and evolution of strain localisation and shear banding in saturated granular soils subjected to drained triaxial shearing.

Online acoustic emission source localization in concrete structures using iterative and evolutionary algorithms

This study is motivated by the need to develop an efficient online structural health monitoring (SHM) framework to accurately localize damage induced acoustic emission (AE) sources in concrete structures. Experimental studies are carried out in concrete slabs considering pencil lead break (PLB) as artificial damage source to initialize acoustic emission (AE) waves. A simplified yet robust Iterative Planar Source (IPS) localization algorithm based on arrival time (ToA) is proposed first to identify arbitrarily selected several damage source locations for (1) rectangular (2)circular, and (3) zig-zag distributed AE sensor network arrangements. The results of the proposed localization algorithm are then compared with those obtained from an evolutionary particle-swarm optimization (PSO) algorithm for each sensor network arrangement to assess the performance of the AE source monitoring strategy. It is found that the zig-zag arrangement of the distributed AE sensors is the most efficient sensor network arrangement for damage source localization. The obtained results clearly represent the accuracy and robustness of the proposed online monitoring framework for localizing damage induced acoustic emission sources in concrete structures without extensive manual intervention.

A Weighted Linear Least Squares Location Method of an Acoustic Emission Source without Measuring Wave Velocity.

The location of an acoustic emission (AE) source is crucial for predicting and controlling potential hazards. In this paper, a novel weighted linear least squares location method for AE sources without measuring wave velocity is proposed. First, the governing equations of each sensor are established according to the sensor coordinates and arrival times. Second, a mean reference equation is established by taking the mean of the squared governing equations. Third, the system of linear equations can be obtained based on the mean reference equation, and their residuals are estimated to obtain their weights. Finally, the AE source coordinate is obtained by weighting the linear equations and inserting the parameter constraint. The AE location method is verified by a pencil lead break experiment, and the results show that the locating accuracy of the proposed method is significantly higher than that of traditional methods. Furthermore, the simulation test proves that the proposed method also has a better performance (location accuracy and stability) than the traditional methods under any given scale of arrival errors.

Study of Knock Sensors as Low-Cost Alternatives to Acoustic Emission Sensors

The real-time monitoring of manufacturing processes is essential to achieve high-quality standards and uniformity at reduced costs. The monitoring of machining processes is usually performed by acoustic emission sensors, which measure the dynamic waves of mechanical stress that propagate through the material during the grinding process. This work studies the application of knock sensors, commonly used in the monitoring of combustion engines, as a low-cost and high-robustness alternative to acoustic emission sensors in the monitoring of the grinding process. Built to operate mechanically attached to the engine block, the knock sensor is resistant to high temperatures, liquids, and particles. In order to demonstrate the feasibility of the sensor, different tests were performed. By determining its resonant frequency, it was possible to identify the response curve of the sensor and its optimal operating range. The acoustic impulse test, generated by the pencil lead break technique, allowed the comparative analysis of the spectral performance between the acoustic emission sensor and the knock sensor. The performance of the knock sensor in an industrial environment was also verified in a case study. The results showed a similar spectral behavior between both sensors when subjected to the same stimulus, demonstrating the feasibility of using the knock sensor to monitor the grinding process.

Thermodynamic Approach for the Identification of Instability in the Wood Using Acoustic Emission Technology

In order to monitor the crack growth of the wood material better and reduce failure risks, this paper studied the attenuation characteristics of acoustic emission signals in wood through pencil lead breaking (PLB) tests, in the aim of estimating the true amplitude value of the acoustic emission source signal. The tensile test of the double cantilever beam (DCB) specimens was used to simulate the crack tip growth within wood material, monitoring acoustic activity and location of crack tips within wood material using acoustic emission technology and digital image correlation (DIC). Results showed that the attenuation degree of acoustic emission signals increased exponentially as the propagation distance increased, and the relationship between relative amplitude attenuation rate and the propagation distance of the acoustic emission signal was established by the regression method, which provides the input parameters for the establishment of the crack instability prediction model in the next step. Based on a thermodynamic approach, a theoretical model for predicting crack instability was established, and the model was verified by DCB tests. The model uses acoustic emission parameters as the basis for judging whether the crack is instable. It provides theoretical support for the application of acoustic emission technology in wood health monitoring.

Failure Analysis of Polymer Blinder Using Acoustic Emission Method

In this work the features of the propagation of the acoustic waves in the matrix sample (DERAKANE 411-350 epoxy vinyl ester resin) by the source pencil lead break (PLB) with different places and extract the feature of the registered AE parameters from the tensile test were studied. Using the typical features of the AE signals such as the frequency characteristic and the energy of the signals for classify and characterize the AE source in both PLB and tensile test. By analyzing the features of the AE signals based on the continuous wavelet transform, the features of the high frequency and the energy of the signal can be used to characterize the AE source, which is useful to establish relation between the characteristics of acoustic signals and the parameter of the fracture of structure in the material.

Evaluating Onset Times of Acoustic Emission Signals Using Histogram Distances

Determining the onset of a transient signal, for example in seismograms, acoustic emission (AE) signals, or ultrasonic signals is very important in non-destructive process monitoring and geophysics. In some cases, the AE from a malfunction is relatively weak, with a low signal-to-noise ratio. Thus, the signals are often hidden, making it rather difficult to separate them from the background noise. The present work proposes a new method of evaluating onset times based on bin-to-bin histogram distances measured with the Bhattacharyya coefficient. A criterion and a standard procedure for determining an onset are formulated. A key parameter, window length, was discussed in detail. Tests on AE signals from a pencil-lead break, single-grit scratching, and filament breakage reveal the feasibility and effectiveness of the proposed method. This method is believed to be especially appropriate when the emission signal strength of the target malfunction is lower than environmental AE signals generated by other stationary sources. It can also be used as an alternative method for identifying onsets in AE signals in process monitoring and other fields.

Acoustic emission propagation characteristics and damage source localization of asphalt mixtures

Fracture of asphalt materials has been a critical issue that affects the fatigue performance and service life of asphalt pavements. Acoustic emission (AE) technique, as a kind of non-destructive testing (NDT) method, can effectively detect minor damage in various materials. The objective of this paper was to propose a damage source localization method suitable for understanding the fracture behavior of asphalt mixtures by AE detection. Firstly, the pencil-lead-break (PLB) tests were conducted on asphalt mixture beams to explore the propagation characteristics of AE signals, and a reasonable layout scheme of AE sensors was determined for monitoring the AE process of asphalt mixtures. Secondly, the wavelet threshold denoising method was utilized to extract effective information from AE signals associated with the pencil-lead breaks on the surface of asphalt mixture beam by simultaneously considering the wavelet basis functions, decomposition levels and threshold rules. Finally, an effective time difference of arrival (TDOA) estimation method combining noise reduction, wavelet decomposition and cross-correlation processing was established to accurately locate the damage source of asphalt mixtures. The results show that there is less serious attenuation of amplitude and energy of AE parameters and frequency spectrum of AE waveforms within the range of 100 mm in asphalt mixtures. The larger signal-to-noise ratio (SNR) and the smaller root mean square error (RMSE) of denoised AE signals indicate that the threshold denoising method optimized by the Fruit Fly Optimization Algorithm (FOA) is more effective for AE detection of asphalt mixtures. The improved TDOA estimation method could obtain a minimum TDOA value, and the calculated locations of AE events are closer to the actual PLB points.

Fractional Fourier Transform Based Adaptive Filtering Techniques for Acoustic Emission Signal Enhancement

Acoustic emission technique (AET) is a well-known non-destructive testing method used for the detection of crack growth and monitoring structural integrity of components. The main limitation in the application of AET is the fact that acoustic emission (AE) signal is often affected by background noise. A new approach is proposed in this paper to reduce the background noise and to improve the accuracy of AE signal detection. Various noise reduction methods have been applied and studied for AE signal enhancement so far. Some of the studies proved that self-adaptive noise cancellation (SANC) and adaptive noise cancellation (ANC) are better techniques for denoising the AE signal. This paper proposes the use of ANC and SANC schemes based on Fractional Fourier Transform (FrFT) for AE signal enhancement. FrFT is the generalization of the classical Fourier Transform (FT). The use of FrFT gives an advantage of an additional degree of freedom (angle of rotation in time–frequency plane) over the traditional Fourier transform and could provide improved performance. In this method, the noisy signal is rotated in time- frequency plane to extract the signal in Fractional Fourier domain (FrFD). Two adaptive filters viz. least mean squares and normalized least mean squares are studied for FrFD based ANC approach. The performance of the proposed method is validated using real AE signals acquired from three different experiments: pencil lead break test, composite drilling test and concrete compression test. Results are compared with the time and frequency domain adaptations. Moreover, the results have also been compared with the frequency domain adaptation techniques. Results have demonstrated that FrFD based ANC approach outperforms the conventional time domain and frequency domain adaptive filtering with an improved signal to noise ratio and significantly reduces mean square error.

Non-destructive determination of the stiffness matrix of a laminated composite structure with lamb wave

This study presents a combined experimental and numerical study on the determination of the stiffness matrix of transversely isotropic thin laminated composite structures non-destructively with the use of group velocity measurements. The approach relies on the usage of A0 and S0 modes of Lamb waves, triggered the by pencil-lead break, which propagate in the composite material, and enables the evaluation of various elements of the stiffness matrix. It is proven that the experimental Lamb wave method can be reliably utilized for determining the components of the stiffness matrix, Cij. The results are validated with respect to those of mechanical characterization and finite element analysis.

A Low-Cost Acoustic Emission Sensor based on Piezoelectric Diaphragm

Sensor monitoring is widely used in modern industrial automation. Industries require efficient and cost-effective monitoring and control systems. Regarding monitoring, new low-cost piezoelectric sensors have been tested in order to monitor processes through the acoustic activity generated. However, these sensors present the difficulties of being very fragile and being glued directly to the structure, thus being single-use sensors. Thus, the objective of this paper is to present a simple, effective and low-cost approach to increase the robustness of piezoelectric transducers and allow their reuse, such as a commercial acoustic emission sensor. For this, a low-cost piezoelectric diaphragm was encapsulated and tested. The characterization of the sensors is the first step in the analysis of the feasibility of an industrial application. Therefore, the sensor was characterized by the pencil lead break (PLB) method, which is recognized for simplicity and repeatability. A piezoelectric diaphragm and a commercial acoustic emission sensor were used as a comparison. Ten PLB tests were performed on a ceramic surface. Time and frequency analysis demonstrated a clear relationship between the enclosed PZT and the other sensors. Based on the good correlation of the results, it was found that the low-cost enclosed PZT can be used in the monitoring of machining processes, presenting greater robustness and simplicity in practical application.

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

Nowadays, deep learning could be an alternative approach to crack characterization. However, to the best of the authors’ knowledge, little research exists on a deep learning-based characterization of fatigue-related AE sources occurring in plate-like structures. Consequently, this paper introduces a stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. The experimental specimen are respectively a Q235B steel plate and a 316L stainless steel containing a laser cladding layer. Specifically, SDAE is pre-trained and utilized to localize AE sources that are simulated by using the classical pencil lead break (PLB) approach. Meanwhile, the number of layers and hidden nodes of SDAE used for coordinate-based location is optimized according to a Bayesian Information Criteria (BIC) approach. To validate the proposed network and simplify the analysis, experiments are carried out on the surface of plate-like structures, which only one sensor is applied. After identifying AE sources that occur near laser cladding layers, the proposed approach classifies them into four source-to-laser cladding layer distance categories. Particularly, a ten-fold cross-validation method is utilized to improve the accuracy of localization in this paper. Moreover, the effectiveness analysis to the number of sensors and comparison with conventional machine learning methods, including support vector machine (SVM) and artificial neural network (ANN), are also evaluated. In order to validate the performance of the proposed approach in terms of coordinate-based source localization. Ultimately, the results demonstrate that 100% accuracy for zonal localization, and the root mean squared (RMS) localization errors of two metallic panels are 38 mm (1.5”) and 48 mm (1.9”), respectively. Additionally, in comparison with conventional machine learning approaches (i.e. SVM and ANN) which the RMS errors were 78 mm (2.5”) and 67 mm (2.1”), respectively, the coordinates-based localization accuracy is significantly improved using the proposed approach. The results demonstrate the proposed approach is effective in AE-based structural health monitoring of plate-like structures with single-sensor.