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Journal articles on the topic 'Acoustic emission analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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1

Rao, A. K. "Acoustic Emission and Signal Analysis." Defence Science Journal 40, no. 1 (January 1, 1990): 55–70. http://dx.doi.org/10.14429/dsj.40.4450.

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2

Fenenko, K. A. "Cluster analysis of acoustic emission signals from the friction zone of tribosystems." Problems of tribology 25, no. 2 (June 5, 2020): 25–33. http://dx.doi.org/10.31891/2079-1372-2020-96-2-25-33.

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3

Yu, Zhen‐zhong, and Philip C. Clapp. "Quantitative analysis of acoustic emission signals." Journal of Applied Physics 62, no. 6 (September 15, 1987): 2212–20. http://dx.doi.org/10.1063/1.339525.

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4

MASLOV, L., and O. GRADOV. "Fracture energy analysis via acoustic emission." International Journal of Fatigue 8, no. 2 (April 1986): 67–71. http://dx.doi.org/10.1016/0142-1123(86)90055-1.

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5

Yu, Yang, and Jia Zhao. "Study on Denoising of Corrosion Acoustic Emission Signals of Tank Bottom Based on Independent Component Analysis." Applied Mechanics and Materials 142 (November 2011): 180–83. http://dx.doi.org/10.4028/www.scientific.net/amm.142.180.

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When tank bottom is detected by acoustic emission method, many corrosion acoustic emission signals can be obtained and adulterated many noise signals, which influence badly the estimation to the corrosion situation of tank bottom. In order to identify acoustic emission sources and disturbance sources without changing the characterization of acoustic emission sources, independent component analysis is used to deal with the denoising of corrosion acoustic emission signals of tank bottom in this paper. In the paper, acoustic emission signals of double exponential model is respectively mixed with white noise signals and stochastic noise signals, and acoustic emission sources and disturbance sources are respectively represented by double exponential model of acoustic emission signals and noise signals, which are independent on statistics, and then FastICA is used to simulation analysis, which is successful to identify acoustic emission signals and white noise signals. The results demonstrate that fastICA is effective to denoise acoustic emission signals.
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6

Shi, Jian, and Peng Wang. "Characteristics Analysis of Acoustic Emission Signals from Titanium under Tensile Fracture." Advanced Materials Research 335-336 (September 2011): 1459–64. http://dx.doi.org/10.4028/www.scientific.net/amr.335-336.1459.

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The parameters of acoustic emission signals were analyzed which titanium plate specimens with crack under tension fracture process used acoustic emission technique. Based on the results of stress-strain curves, crack opening displacement-time curves and CCD images, the relationship between parameters of acoustic emission signal and mechanical behavior of the titanium was investigated. The results show acoustic emission signals increased significantly during yield, crack propagation and fracture with different load speeds, the energy amplitude range of acoustic emission signals was mainly from 50dB to 65dB. The characteristic parameters including AE energy, hit and amplitude can be used to represent the mechanical and inner deformation form of titanium during plastic deformation and fracture process.
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7

Ji, Ming, Nong Zhang, and Feng Gao. "Damage Evolution Analysis of Calcareous Mudstone with Different Water Content under Uniaxial Compression." Advanced Materials Research 168-170 (December 2010): 1388–95. http://dx.doi.org/10.4028/www.scientific.net/amr.168-170.1388.

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Uniaxial compression and acoustic emission experiments of calcareous mudstone with different water content were carried out by using microcomputer controlled electro-hydraulic servo compression testing machine control system of YAW series equipped by coal-rock acoustic and electric data acquisition system of CTA-1-type. Mchanical properties and acoustic emission law of calcareous mudstone were studied. It is concluded from experiment result that rock’s elastic modulus and compressive strength both decrease with increase water content but peak stress shows the opposite trend. It is also found that calcareous mudstone is brittleness with low water content but when water content reaches saturation, calcareous mudstone presents plastic features. Acoustic emission curve fits well with stress-strain curve: acoustic emission activity begins intensifying when stress reaches 70% of peak stress, correspondingly, acoustic emission is up to maximum at peak stress. Based on Weibull hypothesis and acoustic emission experiment, damage law of water bearing calcareous mudstone is researched and damage evolution equation with time variable is advanced.
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8

SAKASHITA, TETSUFUMI. "Frequency analysis of an evoked acoustic emission." AUDIOLOGY JAPAN 33, no. 5 (1990): 501–2. http://dx.doi.org/10.4295/audiology.33.501.

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9

Kiselev, J., B. Ziegler, H. J. Schwalbe, R. P. Franke, and U. Wolf. "Detection of osteoarthritis using acoustic emission analysis." Medical Engineering & Physics 65 (March 2019): 57–60. http://dx.doi.org/10.1016/j.medengphy.2019.01.002.

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10

Varlow, B. R., J. Zhao, D. W. Auckland, and C. D. Smith. "Acoustic emission analysis of high voltage insulation." IEE Proceedings - Science, Measurement and Technology 146, no. 5 (September 1, 1999): 260–63. http://dx.doi.org/10.1049/ip-smt:19990471.

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11

Elfergani, H. A., R. Pullin, and K. M. Holford. "Acoustic Emission Analysis of Prestressed Concrete Structures." Journal of Physics: Conference Series 305 (July 19, 2011): 012076. http://dx.doi.org/10.1088/1742-6596/305/1/012076.

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12

Unander, Tor Erling. "ANALYSIS OF ACOUSTIC EMISSION WAVEFORMS IN ROCK." Research in Nondestructive Evaluation 15, no. 3 (July 2004): 119–48. http://dx.doi.org/10.1080/09349840490480792.

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13

Ohtsu, M. "Acoustic Emission Theory for Moment Tensor Analysis." Research in Nondestructive Evaluation 6, no. 1 (1995): 169–84. http://dx.doi.org/10.1080/09349849508968097.

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14

Ohtsu, M. "Acoustic Emission Theory for Moment Tensor Analysis." Research in Nondestructive Evaluation 6, no. 3 (January 1995): 169–84. http://dx.doi.org/10.1080/09349849509409555.

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15

Bukkapatnam, S. T. S., S. R. T. Kumara, and A. Lakhtakia. "Analysis of Acoustic Emission Signals in Machining." Journal of Manufacturing Science and Engineering 121, no. 4 (November 1, 1999): 568–76. http://dx.doi.org/10.1115/1.2833058.

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Acoustic emission (AE) signals are emerging as promising means for monitoring machining processes, but understanding their generation is presently a topic of active research; hence techniques to analyze them are not completely developed. In this paper, we present a novel methodology based on chaos theory, wavelets and neural networks, for analyzing AE signals. Our methodology involves a thorough signal characterization, followed by signal representation using wavelet packets, and state estimation using multilayer neural networks. Our methodology has yielded a compact signal representation, facilitating the extraction of a tight set of features for flank wear estimation.
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16

Teti, R., C. Bastiolia, and G. Romano. "Acoustic emission analysis of GFR thermoplastic composites." NDT & E International 25, no. 6 (December 1992): 296. http://dx.doi.org/10.1016/0963-8695(92)90699-h.

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17

Morgner, W. "On-line application of acoustic emission analysis." NDT & E International 27, no. 4 (January 1994): 220. http://dx.doi.org/10.1016/0963-8695(94)90570-3.

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18

James Li, C., and S. Y. Li. "Acoustic emission analysis for bearing condition monitoring." Wear 185, no. 1-2 (June 1995): 67–74. http://dx.doi.org/10.1016/0043-1648(95)06591-1.

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19

Qin, Hong Wu, Hai Fu Li, and Xiao Li Wang. "Hardware-Software Complex of Transfer Analysis Features of NDT Objects." Applied Mechanics and Materials 313-314 (March 2013): 1311–15. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.1311.

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The term “acoustic emission” means radiation processes of exertion waves which are produced by internal sources located in the thickness of material under investigation. Acoustic emission method is used as a means of analysis of materials, constructions, productions control and diagnosis during operating time. Energetic parameters as the acoustic emission energy itself may be obtained only based on the whole spectrum analysis. It is strictly contraindicated to measure signal energy in narrow band, naming such measures as “energetic parameters”. Energetic parameters as the acoustic emission energy itself may be obtained only based on the whole spectrum analysis.
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20

Liu, Lei, Guo Ping Sun, and Mu Sen Li. "Study on Acoustic Emission Signal Frequency Analysis for Diamond Growth Process." Advanced Materials Research 1094 (March 2015): 163–67. http://dx.doi.org/10.4028/www.scientific.net/amr.1094.163.

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The growth process of diamond single crystals under HPHT was tested with acoustic emission (AE) technology, and the AE signals were analyzed with fast Fourier transform and wavelet packet analysis. The results show that there are many new frequency peaks in the frequency range that is higher than 80 kHz, and comparative analysis shows that these new frequency peaks are caused from the growth process of diamond crystals. The value of the signal energy and peak in different frequency bands are compared along with the diamond growth process. And it has reflected the dynamic changes in the time domain of the acoustic emission signals stimulated by the different acoustic emission sources. It also shows that the frequency of acoustic emission signals could be used as the effective means to distinguish the different acoustic emission sources in the diamond growth process.
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21

Zhang, Tao, Zhou-Mo Zeng, Yi-Bo Li, Wei-Kui Wang, and Xu Bian. "Characteristics Analysis of Vacuum Gas Leak Detection Signals Based on Acoustic Emission." International Journal of Automation Technology 8, no. 1 (January 5, 2014): 57–61. http://dx.doi.org/10.20965/ijat.2014.p0057.

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Gas leaks can cause major accidents resulting in both human injuries and financial losses. Vacuum pressureinduced air leaks lead to the emission of acoustic signals. Vacuum leak tests are implemented in this paper, and signals excited by leaks through apertures of three different diameters are investigated. According to the acoustic emission signal processing theory, several characteristic parameters were utilized to analyze the generation of continuous vacuum leakage acoustic emission signals. Applicable signal characteristics are used to distinguish the vacuumleaks through apertures of different sizes. It can be inferred that the acoustic emission method can detect vacuum gas leaks as they happen, and signal parameter characteristics analysis can be used to distinguish between the different aperture sizes. This paper is of practical significance to the work of acoustic emission vacuum leak detection.
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22

Zhang, Tian Jun, Sheng Hong Yu, Jin Hu Ren, and Wei Cui. "The EMD Analysis AE Signals of Rock Failure under Uniaxial Compression." Applied Mechanics and Materials 571-572 (June 2014): 845–52. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.845.

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The wavelet packet basis is difficult to be extracted by wavelet analysis at present. To solve this problem, an experiment of Acoustic Emission under uniaxial compression is conducted by SAEU2S acoustic Emission system and Electro-hydraulic servo universal testing machine and the method of empirical mode analysis is adopted to explore the acoustic emission signal in this paper. Firstly with the method of empirical mode decomposition, the acoustic emission signal is decomposed into the forms of intrinsic mode function with several local time scale and residual components, and then these data is analyzed. After the noise-reducing IMF and residual components are refactored, the error between the final and the initial reconstruction signals is less than 10-6. The experiment indicates that the EMD method is effective in processing the local rock acoustic emission signals. The EMD method also provides an efficient way to predict deformation trend of rock damage through deformation of waveform analysis.
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23

Dou, Yan Tao, Xiao Li Xu, Xiao Jun Cai, Guo Xin Wu, and Zhi Xiang Sun. "The AE Identification Methods to Welding Defect by Wavelet Analysis." Applied Mechanics and Materials 63-64 (June 2011): 355–60. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.355.

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In the engineering applications area, welding defect is a major hidden danger of structure security. the bending failure process of welded specimens is detected by using AE technique and the data samples of typical welding defect source are collected, and by using wavelet technique the typical AE datas acquired through experiment are analyzed, characteristic information of the typical acoustic emission source such as electromagnetic noise, plastic deformation, micro-crack initiation, crack unsteady expansion and fracture, etc are extracted. A serial acoustic emission source identification methods based on the energy spectrum coefficients of wavelet are established and which can realize accurately distinguishing of different acoustic emission sources, so as to provide a theoretical basis to detect equipment welding defects by acoustic emission technology dynamic in engineering practice.
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24

Zhang, Kai, Jianxiang Yin, and Yunze He. "Acoustic Emission Detection and Analysis Method for Health Status of Lithium Ion Batteries." Sensors 21, no. 3 (January 21, 2021): 712. http://dx.doi.org/10.3390/s21030712.

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The health detection of lithium ion batteries plays an important role in improving the safety and reliability of lithium ion batteries. When lithium ion batteries are in operation, the generation of bubbles, the expansion of electrodes, and the formation of electrode cracks will produce stress waves, which can be collected and analyzed by acoustic emission technology. By building an acoustic emission measurement platform of lithium ion batteries and setting up a cycle experiment of lithium ion batteries, the stress wave signals of lithium ion batteries were analyzed, and two kinds of stress wave signals which could characterize the health of lithium ion batteries were obtained: a continuous acoustic emission signal and a pulse type acoustic emission signal. The experimental results showed that during the discharge process, the amplitude of the continuous acoustic emission signal decreased with the increase of the cycle times of batteries, which could be used to characterize performance degradation; there were more pulse type acoustic emission signals in the first cycle of batteries, less in the small number of cycles, and slowly increased in the large number of cycles, which was in line with the bathtub curve and could be used for aging monitoring. The research on the health of lithium ion batteries by acoustic emission technology provides a new idea and method for detecting the health lithium ion batteries.
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25

Zhang, Qing, and Yang Zhang. "Research Status and Prospect of Concrete Acoustic Emission Technology." Applied Mechanics and Materials 170-173 (May 2012): 470–73. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.470.

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By discussing the current research status of the concrete acoustic emission technology from four aspects as: the concrete acoustic emission of different conditions; the characteristics of the concrete acoustic emission; the Kaiser effect and Felicity effect of the concrete acoustic emission and the analysis of the concrete acoustic emission data. The concrete acoustic emission research should be done with the practical project, and establish effective processing and analyzing technology of the concrete acoustic emission, to apply the concrete acoustic emission technology better to the measurement of real project.
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26

Li, Xing Wei. "Application of Working Face Rock Burst Prediction of Grey Modeling Cusp Catastrophe Analysis Based on the Acoustic Emission." Applied Mechanics and Materials 373-375 (August 2013): 689–93. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.689.

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According to acoustic emission mechanism of the material stress process, establish the gray cusp catastrophe model of acoustic emission parameters based on the grey theory and catastrophe theory. Using the model, carried out mutation analysis of the acoustic emission parameters of the coal mining face supporting pressure, then realized to face discrimination and prediction of rock burst. Point mutations in the roof sandstone acoustic emission rate of this parameter, has important significance for fracture mechanics study of working face roof sandstone.
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27

Li, Weijie, Siu Chun Michael Ho, Devendra Patil, and Gangbing Song. "Acoustic emission monitoring and finite element analysis of debonding in fiber-reinforced polymer rebar reinforced concrete." Structural Health Monitoring 16, no. 6 (December 1, 2016): 674–81. http://dx.doi.org/10.1177/1475921716678922.

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The acoustic emission technique is widely used for mechanical diagnostics and damage characterization in reinforced concrete structures. This article experimentally investigated the feasibility of debonding characterization in fiber-reinforced polymer rebar reinforced concrete using acoustic emission technique. To this end, carbon-fiber-reinforced polymer rebar reinforced concrete specimens were prepared and they were subjected to pullout tests to study the interfacial debonding between concrete and reinforcement. Test results showed that the debonding failure between concrete and reinforcement was characterized by the total peeling off of the helical wrapping layer of the carbon-fiber-reinforced polymer reinforcement. The response of acoustic emission activity was analyzed by descriptive parameters, such as cumulative acoustic emission hits, amplitude, and peak frequency. The evolution of debonding failure is thus characterized by these acoustic emission parameters. The results demonstrated a clear correlation between the damage evolution of carbon-fiber-reinforced polymer rebar pullout and the acoustic emission parameters. In addition, finite element analysis was adopted to study the stress field during the pullout of the reinforcement. The simulation results agreed well with the experimental investigations.
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28

Liang, Zhong Yu, Zhan Qing Chen, and Xiao Yan Ni. "Experimental Analysis on Acoustic Emission of The Fracture Marble Under High Tempreture." Advanced Materials Research 197-198 (February 2011): 1430–34. http://dx.doi.org/10.4028/www.scientific.net/amr.197-198.1430.

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The marble acoustic emission characteristics under different temperature tested and analyzed under uniaxial compression, and the contrast analysis that passes to acoustic emission the signal and the mechanics parameters to the marble can get,The characteristic curve of acoustic emission is distinguishing with the variety of temperature and stress level, the acoustic emission characteristic and stress-strain curve of fracture rock exists corelation characteristic. At the same stress-level, along with the temperature raising up, the relevant dimension has the trend of the aggrandizement, and the related coefficient will change randomly. The marble then expresses a mechanics characteristics of soft rock at over 1000oC, and the strength-limit descending sharply. The process parameters of AE count in the time domain has a better self-similar characteristics.
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29

Yang, Jie, Yanna Zheng, and Huijing Wang. "Modifications and Statistical Analysis of Acoustic Emission Models Based on the Damage and Fractal Characteristics." Advances in Materials Science and Engineering 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/1898937.

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The damage process is accompanied by the acoustic emission for quasibrittle materials. And in the process of material damage evolution, the length of microcracks satisfies the fractal distribution. Research on their relationship in theory is helpful to reveal the law of material damage evolution and acoustic emission activities. Damage variable expressions are proposed based on the damage and fractal characteristics firstly. Then, the statistical models for acoustic emission considering damage and fractal characteristics are established by deducing the relationship between acoustic emission parameters and load cycles and fractal dimensions. The effects of damage and fractal effects on acoustic emission parameters are analyzed finally. The results show that the damage accelerates the AE activity to the rougher material, the opposite to the more homogeneous material. It can also be seen that the increase of the fractal dimension, the homogeneity constant m, will substantially increase the AE activities.
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30

Liang, S. Y., and D. A. Dornfeld. "Tool Wear Detection Using Time Series Analysis of Acoustic Emission." Journal of Engineering for Industry 111, no. 3 (August 1, 1989): 199–205. http://dx.doi.org/10.1115/1.3188750.

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This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.
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31

Carpinteri, Alberto, Giuseppe Lacidogna, and Amedeo Manuello. "Damage Mechanisms Interpreted by Acoustic Emission Signal Analysis." Key Engineering Materials 347 (September 2007): 577–82. http://dx.doi.org/10.4028/www.scientific.net/kem.347.577.

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Acoustic emissions (AE) are ultrasonic waves generated by the rapid release of energy from discontinuities or cracks spreading in materials subject to a stress and strain field. By identifying the complete shape of the signals and taking into account a larger quantity of data, it becomes possible to ascertain the three-dimensional location of damage sources from AE sensor records. In this connection, the authors have fine-tuned an original procedure that uses seismic analysis techniques, such as the moment-tensor solution. The experimental program consisted of tests conducted in situ on masonry walls of historical buildings.
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32

Mahmoud, H., F. Vlasic, P. Mazal, and M. Jana. "Leakage analysis of pneumatic cylinders using acoustic emission." Insight - Non-Destructive Testing and Condition Monitoring 59, no. 9 (September 1, 2017): 500–505. http://dx.doi.org/10.1784/insi.2017.59.9.500.

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33

Shen, Gongtian, Junjiao Zhang, and G. Lackner. "International acoustic emission standard analysis and development outlook." Insight - Non-Destructive Testing and Condition Monitoring 62, no. 12 (December 1, 2020): 724–34. http://dx.doi.org/10.1784/insi.2020.62.12.724.

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At present, the major acoustic emission (AE) standards in the world mainly consist of American, European, International Organization for Standardization (ISO) and Chinese standards. The status of the four groups of standards is analysed in this paper and compared based on quantity and type. A comparative analysis of the content of certain similar standards is carried out. Compared with ISO and European standards, the quantity of American and Chinese standards is larger due to the rapid conversion of new technologies into standards. European standards are well-principled and systematic and the general standards provide the basis for all AE testing and standard development. ISO standards, even when dealing in part with similar content, seek to be consistent with European and American standards, to have a distinct presentation of methodologies and to follow the principles of developing standards for new technologies.
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34

Wentzell, Peter D., and Adrian P. Wade. "Chemical acoustic emission analysis in the frequency domain." Analytical Chemistry 61, no. 23 (December 1989): 2638–42. http://dx.doi.org/10.1021/ac00198a010.

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35

Quadro, Alexandre L., and J. R. T. Branco. "Analysis of the acoustic emission during drilling test." Surface and Coatings Technology 94-95 (October 1997): 691–95. http://dx.doi.org/10.1016/s0257-8972(97)00509-4.

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36

Webster, J., W. P. Dong, and R. Lindsay. "Raw Acoustic Emission Signal Analysis of Grinding Process." CIRP Annals 45, no. 1 (1996): 335–40. http://dx.doi.org/10.1016/s0007-8506(07)63075-3.

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37

Cho, N. Y., J. L. Ferracane, and I. B. Lee. "Acoustic Emission Analysis of Tooth-Composite Interfacial Debonding." Journal of Dental Research 92, no. 1 (October 25, 2012): 76–81. http://dx.doi.org/10.1177/0022034512465757.

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38

La Rosa, G., C. Clienti, and F. Lo Savio. "Fatigue Analysis by Acoustic Emission and Thermographic Techniques." Procedia Engineering 74 (2014): 261–68. http://dx.doi.org/10.1016/j.proeng.2014.06.259.

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39

Vijay Yeole, Gaurav, and Sagar Ramchandra Shinde. "Acoustic Emission Signal Analysis using Data Acquisition System." International Journal of Engineering Trends and Technology 11, no. 8 (May 25, 2014): 384–87. http://dx.doi.org/10.14445/22315381/ijett-v11p275.

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40

Gerasimov, S. I., and T. V. Sych. "Numerical modelling and experimental analysis of acoustic emission." Journal of Physics: Conference Series 1015 (May 2018): 032039. http://dx.doi.org/10.1088/1742-6596/1015/3/032039.

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41

Ahmed, W., M. K. Farooq, I. U. Hassan, and N. Ali. "Acoustic Emission Analysis of Wear in Hacksaw Blades." Surface Engineering 15, no. 5 (October 1999): 423–26. http://dx.doi.org/10.1179/026708499101516731.

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42

Andreykiv, Olexandr Ye, Mykola V. Lysak, Oleh M. Serhiyenko, and Valentyn R. Skalsky. "Analysis of acoustic emission caused by internal cracks." Engineering Fracture Mechanics 68, no. 11 (July 2001): 1317–33. http://dx.doi.org/10.1016/s0013-7944(01)00026-1.

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43

Makhutov, N. A. "Acoustic Emission Analysis of Deformation and Damage Processes." Inorganic Materials 55, no. 15 (December 2019): 1511–15. http://dx.doi.org/10.1134/s002016851915010x.

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44

Lane, John, Jeffery Hooker, Christopher Immer, and James Walker. "Acoustic emission analysis of shuttle thermal protection system." Journal of the Acoustical Society of America 115, no. 5 (May 2004): 2538. http://dx.doi.org/10.1121/1.4783538.

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45

Lan, Ming-Shong, and David A. Dornfeld. "ACOUSTIC EMISSION AND MACHINING - PROCESS ANALYSIS AND CONTROL." Advanced Manufacturing Processes 1, no. 1 (January 1986): 1–21. http://dx.doi.org/10.1080/10426918608953155.

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46

Vorontsov, V. B., and V. V. Katalnikov. "Analysis of acoustic emission effect accompanying metal crystallization." Journal of Physics: Conference Series 98, no. 5 (February 1, 2008): 052005. http://dx.doi.org/10.1088/1742-6596/98/5/052005.

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47

Li, X., and J. Wu. "Wavelet analysis of acoustic emission signals in boring." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 214, no. 5 (May 1, 2000): 421–24. http://dx.doi.org/10.1243/0954405001518206.

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Using acoustic emission (AE) signals to monitor tool wear states is one of the most effective methods used in metal cutting processes. As AE signals contain information on cutting processes, the problem of how to extract the features related to tool wear states from these signals needs to be solved. In this paper, a wavelet packet transform (WPT) method is used to decompose continuous AE signals during cutting; then the features related to tool wear states are extracted from decomposed AE signals. Experimental results verified the feasibility of using the WPT method to extract features related to tool wear states in boring.
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48

Caldwell, D. L., D. L. Steele, and L. T. Guth. "Optimization of Composite Matrices Using Acoustic Emission Analysis." Journal of Reinforced Plastics and Composites 6, no. 2 (April 1987): 193–206. http://dx.doi.org/10.1177/073168448700600207.

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49

Dresen, Georg, Sergei Stanchits, and Erik Rybacki. "Borehole breakout evolution through acoustic emission location analysis." International Journal of Rock Mechanics and Mining Sciences 47, no. 3 (April 2010): 426–35. http://dx.doi.org/10.1016/j.ijrmms.2009.12.010.

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Dyjak, P., and R. P. Singh. "Acoustic Emission Analysis of Nanoindentation-Induced Fracture Events." Experimental Mechanics 46, no. 3 (March 27, 2006): 333–45. http://dx.doi.org/10.1007/s11340-006-7303-x.

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