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This research revealed fracture behavior of concrete in using recycled aggregates by Acoustic Emission as one of the Non-destructive Inspection. The phenomenon of acoustic emission (AE) is the propagation of elastic waves generated from a source, known as a micro-crack in an elastic material. There were taken to use low-treated recycled aggregate, crushed returned ready mixed concrete for aggregate and normal aggregate. Examination measured AE under the uniaxial compression test. The condition of load is repeated loading. As a result, fracture behavior due to low treated recycled aggregate was detected by AE. It is clarified that AE of concrete with low treated recycled aggregate appeared in low stress level. It has been understood that difference of aggregates becomes clear from Kaiser effect in repeated loading. In relation between RA value and average frequency, it has been understood the adhesion properties of the cement paste in recycled aggregate are appreciable.

Acoustic Emission (AE) and hysteresis parameters were studied from PZT-5A (soft) and PZT-8 (hard) ceramics during the application of ac fields in the frequency range of 0.2 Hz to 5 Hz. AE was found to occur mainly due to domain switching in these ceramics during the application of ac fields. In PZT-5A, a threshold field was observed for the AE activity to begin in both the direction of the applied field and in general, it was found to decrease with increasing frequency. Apart from this, AE activity was found to decrease with increasing number of applied ac cycles, and is attributed to domain pinning. In the case of PZT-8, AE was found to occur even at zero field at higher frequencies and AE activity was found to increase with increasing number of field cycles. These are explained on the basis of the defect dipole — domain interaction in these ceramics.

The classic plate theory (CPT) as a theoretical solution to an impact load has been used in a thin plate. However, The CPT is not any more useful solution for the impact load in the industrial power plant, which is generally constructed by the thick plate. In this paper a novel and effective approach is developed to determine the time history of the impact load on a thick aluminum plate based on the analysis of the acoustic waveforms measured by a sensor array located on the thick plate surface in combination with the theoretical Green's function for the plate. The Green's functions are derived based on either the exact elastodynamic or theory the approximate shear deformation plate theory (SDPT). If the displacement is measured on the plate, then the time history of impact load can be calculated by deconvolving the measured displacement with the theoretical Green's function. The reconstructed time history for impact load is compared with the time history of the impact load measured by the force transducer. A good prediction is found. This technique presents a valuable method for identification of source and may be applied to in-service structures under impact to signals recorded from acoustic emission of propagating cracks.

Deformation rules and acoustic emission characteristics of granite under repeated loading-unloading are studied using the MTS Mechanics Testing System and Disp. acoustic emission instrument. It's found that the axial stiffness of granite as well as Poisson's ratio increases with the development of the plastic strain while the change trend of lateral stiffness is negative. For brittle rocks, strain is produced by three mechanisms: elastic deformation, axial micro-cracking and compaction. The peak AE rate is increasing with the plastic strain, which indicates that using the axial plastic strain as the internal damage variable to represent rock damage is reasonable.

The tensile responses and the associated damage evolutions of a 3D C/SiC composite with and without heat treatment on the fiber preforms were compared. The results show that the composite without heat treatment exhibits a largely non-linear stress-strain behavior up to rupture as well as a lower strength and a strain-to-failure. The damage evolution characterized by acoustic emission indicates the composite failures in the region of matrix cracking multiplication. However, the composite with heat treatment has a larger strength and a strain-to-failure, and the damage evolution indicates that the composite had experienced the region of matrix cracking saturation and then fiber bundle pull-out just prior to final failure. Microstructural observations on the fractured specimens revealed the interfacial bonding between fibers and matrix becomes weaker after heat treatment.

A study on the acoustic emission (AE) characteristics during deformation of nacre material was performed. We found that intermittent AE events are generated during nacre deformation. These avalanches may be attributed to microfracture events of the aragonite (CaCO₃) nano-asperities and bridges during tablet sliding. These events show several critical features, such as the power-law distributions of the avalanche sizes and interval. These results suggest that the underlying fracture dynamics during nacre deformation display a self-organized criticality (SOC). The results also imply that the disorder and long-range correlation between local microfracture events may play important roles in nacre deformation.

Pipeline leakages have plagued pipeline transportation for long time. Therefore, accurately extracting the features of leak signal in the presence of noise, and prompt identification of leak states and leak sizes is essential when leakage occurs. A novel leakage detection method based on the improved adaptive filter, whose parameters were optimized by the particle swarm optimization (PSO), was formulated and applied. The PSO-adaptive filter proved to be an effective signal processing method in contrast with variational mode decomposition (VMD). Its efficiency stems from the fact that the adaptive filter employs the noise collected from the detection environment. Therefore, the filter can adjust its parameters according to the changing situation. What is more, the application of PSO is conducive to automatically set suitable parameters for adaptive filter. After signal denoising, principal component analysis (PCA) was used for feature dimension reduction and selecting optimal features. The features after PCA proved to be more helpful in pattern recognition than the features without PCA. Furthermore, the relationship between the recognition results of leakage sizes and the measurement distance of the sensor was studied. Experimental results show that the method used in this paper can identify the leakage states with the accuracy of 100%. The identification result of leakage size reaches an accuracy of 86.75% under the influence of the measurement distance.

This paper reports that synergetics are used to analyze the crazing evolution. On this basis, chaotic effect is explored. The chaos equation is established and verified. The theoretical derivation are consistent with the experimental results. We design a special specimen with a special loading mode, the transient monitoring function of acoustic emission (AE) technology is used to track and detect the crazing inside the PMMA in real time, and the experiments show that synergetics can explain the crazing properties of polymer. Importantly, the mathematical explanation is also given. The AE analysis, synergetics, and craze photo reached a conclusion that the crazing has chaotic behavior. After analyzing the AE events and crazing at different stress levels, the accuracy of synergetic approach for crazing is verified. By studying the course of AE events and crazing, the self-organization effect is proposed. The research results will provide data support for the application of PMMA in ship, aircraft, and precision instruments.

In order to explore the causes of acoustic emission (AE) signals during coal failure, the coal samples with original joints were subjected to uniaxial compression experiments, and the AE signals were monitored by AEwin Test for Express-8.0. Based on the multifractal theory, the multifractal characteristics of AE were analyzed. The results showed that the AE counts and accumulative counts change over time corresponded well with the load-time, which reflected the degree of crack evolution and loading. During the initial loading stage, the cracks expanded gradually along the trace of the original cracks, which could induce a few AE events, while with the increase of load, the cracks enlarged gradually and then joined together to form a macroscopic fracture, which would cause much more AE events within a larger value. Multifractal spectrum [$f(\alpha )-\alpha$] of AE was more concentrated in the right side, illustrating that the frequency of small signals was greater than that of the large signals in AE sequences, which revealed cracks expanding and microfracture events dominated during the loading process. The greater the multifractal spectrum width ($\mathrm{\Delta }\alpha )$ was, the larger the AE signals differences were, which reflected that AE varied more intensely. The more developed the original cracks, the more obvious the multifractal characteristics. This research revealed the causes and percentage of the AE events within small or large signals, which would help us to recognize crack evolution of coal and generation mechanism of AE.

In this paper, acoustic emission (AE) signal of coal and rock samples during the heating process are measured. The results show that AE energy of coal samples is higher than that of rock samples. Based on the multifractal theory, the multifractal characteristics of AE signal are researched. The multifractal spectrum width ($\mathrm{\Delta }\alpha$) of coal samples is wider than that of rock samples, which means AE signal of coal samples is more complex than AE signal of rock samples during the heating process. Multifractal parameter ($\mathrm{\Delta }f$) is more than zero, illustrating that small AE signal is dominate. The time-varying multifractal characteristics are analyzed, and the change trend of multifractal spectrum width ($\mathrm{\Delta }\alpha$) of coal and rock samples is consistent. At the stage of 40–50${}^{\circ }$C, multifractal spectrum width ($\mathrm{\Delta }\alpha$) gets the maximum value, whereas multifractal spectrum width ($\mathrm{\Delta }\alpha$) gets the minimum value at the stage of 80–100${}^{\circ }$C. For coal samples, multifractal parameter ($\mathrm{\Delta }f)$ is more than zero except at the stage of 40–50${}^{\circ }$C. However, multifractal parameter ($\mathrm{\Delta }f)$ of rock samples is always more than zero during the entire heating process. By $R/S$ analytical method, Hurst exponent of AE signal is calculated. The results show that Hurst exponent of coal and rock samples are more than 0.5, which indicate that AE signal presents persistence, and there is a positive correction between AE signal and temperature. In different temperature levels, Hurst exponent curve presents an increase trend after the initial decrease.

To study the damage evolution mechanism of gas-bearing coal and formation causes of acoustic emission signals during this process, the loaded experiments of gas-bearing coal were performed, and acoustic emission (AE) data radiated in this process were collected. Based on the multifractal theory, the causes of AE were explored in various loaded phases. The results showed that at the low stress stage, the fractures close and the friction/slip could cause low-energy acoustic emission events, and the multifractal spectrum had a smaller width. By contrast, at the high stress stage, the cracks expand, penetrate, and rupture, which would lead to AE events with the release of high energy, reflecting an increase in the width of the multifractal spectrum. At the initial loading stage, the time-varying multifractal spectrum was characterized by a chaotic behavior, but as the loading progressed, it gradually became orderly. In the elastic stage, coal experienced elastic deformation without damage, the ratio of strong and weak AE signals was almost the same, and both $\mathrm{\Delta }{f}_m$ and $\mathrm{\Delta }{\alpha }_m$ were close to 0. In the plastic fracture stage, coal body consumed huge amounts of energy and suffered fracture. This also caused the coal body to radiate a large amount of AE signals. An analysis of these signals indicated that strong signals dominated and showed an increasing trend, and $\mathrm{\Delta }{f}_m$ was less than 0 and continued to decrease. The time-varying multifractal characteristics reveal the formation mechanism of AE signals from gas-bearing coal, which contributes to improve our understanding of the mechanism of gas-bearing coal damage.

Coal–rock dynamic disasters seriously threaten safe production in coal mines, and an effective early warning is especially important to reduce the losses caused by these disasters. The occurrence of coal–rock dynamic disasters is determined by mining-induced stress loading and unloading. Therefore, it is of great significance to analyze the precursory information of coal deformation and failure during true triaxial stress loading and unloading. In this study, the deformation and failure of coal samples subjected to true triaxial loading and unloading, including fixed axial stress and unloading confining stress (FASUCS), are experimentally investigated. Meanwhile, acoustic emission (AE) during the deformation of coal samples is monitored, and the multi-fractal characteristics of AE are analyzed. Furthermore, combined with the deformation and failure of coal samples, the precursory information of coal deformation and rupture during true triaxial stress loading and unloading is obtained. Finally, the relationship between multi-fractal characteristics and damage evolution of coal samples under FASUCS is discussed. The results show that the multi-fractal spectral widths of AE time series under the conditions of FASUCS with different initial confining stresses or unloading rates are quite different, but the dynamic changes of multi-fractal parameters $\mathrm{\Delta }\alpha$ and $\mathrm{\Delta }f$ are similar. This indicates that the microscopic complexity of AE events of coal samples under different conditions of FASUCS differs, but the macroscopic generation mechanism of AE events has inherent uniformity. The dynamic changes of $\mathrm{\Delta }\alpha$ and $\mathrm{\Delta }f$ can reflect the stress and damage degree of coal samples. The dynamic change process of $\mathrm{\Delta }\alpha$ well accords with the damage evolution process of coal samples. A gradual decrease of $\mathrm{\Delta }\alpha$ corresponds to a slow increase of damage, while a sharp increase of it corresponds to a rapid growth of damage. At the same time, the mutation point of damage curve at distinct stress difference levels shares the same variation trend with the $\mathrm{\Delta }\alpha$ mutation point. The change of $\mathrm{\Delta }\alpha$ can reflect the damage process of coal samples, which can be used as precursor information for predicting coal–rock rupture. The finding is of great significance for the early warning of coal–rock dynamic disasters.

Grinding is used to improve surface roughness and dimensioning precision in the metal industry. A large amount of heat is released during grinding. Most of this heat is transferred to the workpiece. In this case, a grinding burn may occur on the workpiece. Grinding burn is a significant issue in the production of bearings. If a burn occurs on the workpiece during grinding, the surface quality deteriorates and the internal structure and mechanical qualities of the material are adversely affected. Therefore, detecting grinding burn is critical for bearing manufacturers. In this study, during the grinding of the bearing parts, the machine conditions were changed in accordance with the real grinding scenario, and burnt and non-burned bearing data were obtained with the acoustic emission sensor. Then, time-frequency representations were obtained from these data with the continuous wavelet transform. These images have been classified in the GoogLeNet Network by transfer learning. Combinations of faulty/ normal data processed under different machine settings and combinations of faulty/ normal data processed with the same machine parameters were classified with the proposed method and compared. Faulty bearings processed with the same machine characteristics were detected with 100% accuracy using the proposed method. This percentage gives a reliable result for bearing producers. This study contributes to the literature in three ways: (a) It is based on data collected under real-world grinding situations. (12 different machine settings were employed.) (b) Various machine conditions were compared. (c) Faulty bearings were detected with high accuracy.

The reliability and performance of precision mechanical components that experience sliding under contact depend heavily on the friction and wear characteristics at the sliding interface. In order to improve the reliability of the sliding interface, there is a need to predict, measure and monitor any physical interactions at the head–disk interface (HDI). In the present work, the basic tribological characteristics of HDI were analyzed. The HDI during start–stop and constant speed operation using acoustic emission (AE) were studied. The Fast Fourier Transform (FFT) analysis of the AE signal was used to understand the interaction between the AE signal and the state of contact. In addition, we developed laser textured (LT) disk and the contact start–stop (CSS) tests were performed to investigate the effect of dimples on the stiction performance of the HDI. Furthermore, numerical analysis between the slider and disk surface pressure were performed using the boundary coordinate system and divergence formulation for the nonlinear Reynold's equation solution.

The material removal rate (MRR) during precision finishing/polishing is a key factor, which dictates the process performance. Moreover, the MRR or wear rate is closely related to the material/part reliability. For nanoscale patterning and/or planarization on nano-order thickness coatings, the prediction and in-process monitoring of the MRR is necessary, because the process is not characterizable due to size effects and material property/process condition variations as a result of the coating/substrate interactions. The purpose of this research was to develop a practical methodology for the prediction and in-process monitoring of MRR during nanoscale finishing of coated surfaces. Using a specially designed magnetic abrasive finishing (MAF) and acoustic emission (AE) monitoring setup, experiments were carried out on indium-zinc-oxide (IZO) coated Pyrex glasses. After a given polishing time interval, AFM indentation was conducted for each workpiece sample to measure the adhesion force variations of the coating layers (IZO), which are directly related to the MRR changes. The force variation and AE monitoring data were compared to the MRR calculated form the surface measurement (Nanoview) results. The experimental results demonstrate strong correlations between AFM indentation and MRR measurement data. In addition, the monitored AE signals show sensitivity of the material structure variations of the coating layer, as the polishing progresses.

To optimize the strengthening method using the fiber reinforced polymer (FRP) for the reinforcement of the concrete structure with cracks, the three-point bending test was conducted on the concrete beams wrapped with different layers of FRP materials. The strain gauges were pasted on the surface of the specimens to measure the initial cracking load. The crack mouth opening displacement (CMOD) was utilized to test the load–crack mouth opening displacement curve. According to the improved calculation formula of the fracture toughness, the critical effect crack length $a_c$, initiation fracture toughness $K_{\mathrm{\text{IC}}}^{\mathrm{\text{ini}}}$ and instability fracture toughness $K_{\mathrm{\text{IC}}}^{\mathrm{\text{un}}}$ of specimens were calculated. The test results showed that, under the same initial crack depth, the peak load of FRP reinforced concrete decreases with the increase of FRP pasting layer. When there was one layer wrapped over the specimen, the instability toughness of the specimen reached the maximum value and the crack resistance was the best. Based on acoustic emission testing method, the acoustic emission parameters of the above-mentioned concrete during fracture process were identified and collected. The optimal layer of the FRP reinforced concrete with cracks was analyzed from the acoustic emission method.

Manufacturing industries are always in search of preparing quality implants from Co–Cr–Mo biomaterial by doing improvements in their processes. CNC turning is basically adopted for preparing a practical surface of femoral head of hip implants. The online monitoring systems such as acoustic emissions (AEs) from the machining zone provide knowledge of the deformation characteristics of the work material in machining, which affect the machined surface topography and integrity. In this study, CNC turning experiments by using CBN insert were planned according to RSM CCD in dry cutting environment. Surface generation was monitored with the help of AE setup by correlating AE RMS voltage and counts to the surface integrity of machined surfaces. Cutting speed, feed rate and depth of cut show significant effects on the AE RMS voltage. The feed rate and depth of cut, however, also show dominating effects on the AE counts. It is also observed that at lower force components, the amplitude of AE RMS voltage corresponds to a lower surface roughness ($R_a=0.97$$\mu$m) and the counts pattern of AE signal shows nonuniform behavior which corresponds to a higher surface roughness ($R_a=1.06$$\mu$m); however, the smooth surface produced with a lower $R_a$ value (0.89 $\mu$m) shows uniform counts pattern of AE signals.

This paper presents field tests performed on a slab-on-girder pre-stressed concrete bridge. The bridge was tested under static loading, crawling loading, and dynamic loading. A full three-dimensional finite element prediction under both static and dynamic loadings was carried out and the results were compared with the field measurements. While acoustic emission (AE) monitoring of bridge structures is not a new vista, the method has not been fully exploited in bridge monitoring. Though numerous quantitative methods have been proposed, they have not yet developed to be useful for actual field tests of bridges. Therefore, in this study, an attempt was made to use the intensity analysis technique for damage quantification using the AE method.

Acoustic emission (AE) is a nondestructive testing (NDT) technique used for detecting damages, cracks, and leaks in different structures such as metals, composites, wood, fiberglass, ceramics, plastics, etc. In recent years, AE has gained popularity within the field of biomedical applications. The structure of bone is similar to composite materials, therefore, it is advantageous to use NDT technique. Thus, it can be used for monitoring the fracture behavior, crack initiation/propagation, and fatigue detection in bones. The goal of this study was to determine the usefulness of AE techniques in fracture detection phase of bones and to develop an NDT methodology for the monitoring of crack initiation and propagation in bones. This study describes AE activity during fracture of bone tissue under tensile loads. The experiments were carried out in vitro techniques using intact and fracture-simulated bovine tibias. The specimens were loaded to failure in tension using a mechanical testing machine. During the mechanical tests, AE signals were measured and recorded by using AE system processor equipped with two wideband piezoelectric sensors fixed to the surfaces of both ends of the test specimens. By superposing the load–time curve and the cumulative AE event–time curve, AE activities of crack initiation and propagation were identified. In all experiments, the cumulative AE number for each period of time rose up exponentially with the incremental tensile load. Load for AE initiation demonstrated a convincing linear interaction with AE event generation.

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains.