Acoustic Emission Signal Waveform Research Articles

In Situ Measurement of the Machining State in Small-Diameter Drilling by Acoustic Emission Sensing

In drilling small holes with diameters of 1 mm or less, minute clogging and twining of chips or the adhesion of the workpiece material can become factors in causing breakages of the drill bit; moreover, it can be difficult to identify the machining state. Acoustic emission (AE) sensing is a nondestructive inspection technique that measures the elastic-stress waves that are generated when a material is deformed and fractured. AE sensing permits highly sensitive measurements to be made without changing the rigidity of the experimental system, unlike force sensing of cutting resistance, etc. In the present study, attempts were made to identify the machining state and tool wear, and to predict abnormalities in small-diameter drilling by using the change in the frequency of AE signal waveforms arising from deformation and fracture. It was shown that it is possible to predict the breakage of the drill bit by detecting high-frequency AE signals at about 1 MHz, caused by the adhesion of the workpiece material. In addition, a correlation map of the AE frequency spectrum for identifying the machining state in a drilling operation is suggested.

Experimental characterization of impact damage in foam-core sandwich structures using acoustic emission, optical scanning and X-ray computed tomography techniques

Sandwich structures used in transportation and energy industries are commonly subjected to low-velocity impact in the course of manufacturing, service and maintenance, which can lead to the premature degradation of structural stiffness and strength. A single non-destructive evaluation technique is often difficult to provide a comprehensive assessment on impact damage from online to offline and from visible to invisible. In the present study, acoustic emission (AE), three-dimensional (3D) optical scanning and X-ray computed tomography (CT) techniques are combined to provide a systematic approach for damage characterization of thick foam-core sandwich panels under low-velocity impact. In the tests, the mechanical responses such as force-time and force-displacement curves are obtained from the testing system. The AE technique is adopted to monitor damage initiation and evolution in situ; optical scanning and X-ray computed tomography techniques are used for off-line damage identification and quantification. The results show that the AE signal waveform stream, cumulative counts and cumulative energy correlate well with the mechanical responses of the sandwich panel under the low-velocity impact, enabling capture of valuable information for online damage monitoring. Unlike general cases, the AE signals generated from low-velocity impact are found to be presented in strong bursts of nearly coincident events in the short duration. Therefore, the distributions of AE amplitude and peak frequency between adjacent AE counts are extracted to study the different damage modes and damage mechanisms. The X-ray computed tomography images enable to precisely visualize the fracture behaviors of the PET foam-core inside the sandwich panel in a nondestructive fashion, which evidently supports the AE and optical scanning results. The study is anticipated to demonstrate effectiveness and capability of the proposed experimental methodology for damage detection and characterization on sandwiched composite structures subjected to impact loading.

Evaluation of Writing Performance for Different Types of Ballpoint Pen Ink by Acoustic Emission Sensing

During contact, deformation, and fracture of surface asperities between the friction surfaces of materials, acoustic emission (AE) waves are generated as the strain energy is released. By detecting the AE waves during friction using an AE sensor, the state of friction, wear, and lubrication between the friction surfaces can be measured and evaluated with high sensitivity. In this study, in order to establish a method for evaluating the writing performance of ballpoint pens by AE sensing, the measurement method was examined, and AE signal waveforms were analyzed. It was found that AE sensing can detect phenomena that do not appear as a change in frictional force during writing. In addition, frequency analysis of the AE signal waveforms revealed that differences in writing performance depending on the ink type of the ballpoint pen can be evaluated and interpreted.

Geomechanical model test for failure and stability analysis of high arch dam based on acoustic emission technique

The internal damage of the model during the geomechanical model test is difficult to be detected. The failure and instability process of the arch dam are always accompanied by a large number of acoustic emission (AE) signals, which is related to the number of cracks in the model. AE technique was applied in the geomechanical model test of Mengdigou arch dam to analyze its failure process and overall stability. Noise filtering was conducted based on spectrum analysis of AE signal waveform. Parameter analysis was performed after noise filtering. The overall stability of the dam was evaluated by three characteristic overload safety factors: the crack initiation factor K1, nonlinear deformation factor K2 and ultimate load factor K3, which were studied by AE parameter analysis. The failure process of Mengdigou arch dam was analyzed by the statistical analysis of AE hits and the wave velocity variation before and after test. AE analysis results were compared with the displacement and video monitoring results at the same time to verify the correctness of the evaluation method based on AE technique. The results showed that the failure process of the dam and foundation was well described by the AE technique, and the three safety factors of Mengdigou arch dam were defined more clearly. And as for Mengdigou arch dam, the fracture occurred at the dam heel area in the early stage of loading and the abutment of right bank was better than the left. The foundation damage was not serious after test and the overall instability was determined by instability of dam. Based on AE technique, the three overload safety factors was determined more clearly: K1 = 1.5–2.0, K2 = 4.5–5.0, and K3 = 11–11.5, which provided an important reference for the Mengdigou arch dam construction.

Acoustic emission signal source localization on plywood surface with cross-correlation method

The high-speed data acquisition equipment which is based on NI and LabVIEW software was used to establish the acoustic emission (AE) signal collection platform, and adopted wavelet analysis method to extract the weak of AE signal waveform from noise, aiming at the localization of AE source on plywood surface. The signal cross-correlation analysis method is used to determine the time difference between the AE signals and the different sensors,so that further calculates the average propagation speed of AE signal on the plywood surface. A kind of AE signal source localization method which is based on the cross-correlation method was proposed according to the principle of geometric positioning, and verified by means of the testing experiment which selected the lead break to simulate AE source. This paper has shown that wavelet analysis can effectively extract AE signal waveform, and the localization method based on cross-correlation principle can precisely calibrate the position of AE signal source.

アコースティックエミッション技術を用いた切削工具刃先位置の認識に関する研究

The introduction of the IoT is needed in the field of manufacturing. Also, the monitoring of the machining state is required along with automation and miniaturization of super-precision machine tools in the viewpoint of maintaining accuracy and failsafe. In this study, the identification of a height of the edge of a cutting tool by an acoustic emission (AE) technique was examined. Since the feature of AE signals differs due to the mode of friction, the features of the frequency spectrum of the AE signal waveforms detected at end face turning for the different heights of a cutting tool edge in turning was examined under dry and wet cutting conditions. As a result, it was found that the difference of a height of a cutting tool edge can be identified by the change in the frequency of the AE signals.

Diagnostic Testing on Crack Propagation of Asphalt Pavement Materials

Asphalt concrete is a typical road building material of advanced asphalt pavements. Under the reciprocating vehicle loading, fatigue cracks are reduced inevitably, which seriously affect road performance. Therefore, effective detection and diagnosis of fatigue crack propagation has become a hot research issue. Acoustic emission detection test is a new dynamic nondestructive testing technology which has some features of real-time, online and overall detection. The main purpose of this paper systematically analyses the development process of acoustic emission technology applied in health monitoring field of asphalt concrete pavement structure, and to state the acoustic emission source localization principle, the parameters of acoustic emission signal analysis method, the acoustic emission signal waveform analysis method, etc. Furthermore, from the aspects of the asphalt concrete fatigue performance, crack evolution process and positioning technology, this paper fundamentally discuss the future technological development prospect of acoustic emission technology which is applied in the field of monitoring and diagnosis of asphalt concrete pavement structure, and provide a new perspective for the whole dynamic maintenance of road , and the information implementation of pavement maintenance strategies at later stage.

Microscopic study on the relationship between AE signal and wear amount

In order to establish precise in-process measurement of wear amount using an acoustic emission (AE) technique, it is necessary to investigate the relationship between the AE signal and the wear amount quantitatively. In this study, from a microscopic viewpoint using an atomic force microscope, we examined the quantitative relationship between the burst-type AE signal waveform and the quantity of wear elements generated at the elementary process of adhesive wear. As a result, it was found that the burst-type AE signal is detected at the generation of wear elements. And, there is a good correlation between the amplitude and duration of AE signal waveform and the quantity of wear elements generated on the worn surface. The result in this study corresponds to the result of our previous study that there is a linear relationship between the AE energy and wear amount under macroscopic adhesive wear.

Correlation between features of acoustic emission signals and mechanical wear mechanisms

The recognition of wear mechanisms is important for effective maintenance of dynamic machinery, because the selection of an appropriate maintenance solution is dependent on the particular mechanism of wear that occurs at the frictional interface. To permit the recognition of wear mechanisms by means of an acoustic emission (AE) monitoring technique, the features of AE signals generated during adhesive wear and during abrasive mechanical wear were examined. For adhesive wear, friction and wear experiments were conducted by using a micro-sliding friction tester of the pin-on-block type with various combinations of pure metals that showed different adhesion forces. For abrasive wear, the experiments were conducted by rubbing an iron pin on emery papers with various grain sizes. AE signal waveforms generated in each wear mechanism were recorded and a frequency analysis was performed. AE signals detected during adhesive wear showed a large peak in the high-frequency region, whereas AE signals detected during abrasive wear showed a few peaks in the low-frequency region. These results permit the recognition of wear mechanisms by the AE technique.