• Title/Summary/Keyword: AE Signal

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Data-Driven Modelling of Damage Prediction of Granite Using Acoustic Emission Parameters in Nuclear Waste Repository

  • Lee, Hang-Lo;Kim, Jin-Seop;Hong, Chang-Ho;Jeong, Ho-Young;Cho, Dong-Keun
    • Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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    • v.19 no.1
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    • pp.75-85
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    • 2021
  • Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.

Grinding Characteristics of Diamond Burs in Dentistry (치과용 다이아몬드 버의 연삭가공 특성)

  • Lee, Keun-Sang;Lim, Young-Ho;Kwon, Dong-Ho;Choi, Man-Yong;Kim, Kyo-Han;Choi, Young-Yun
    • Journal of the Korean Society for Precision Engineering
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    • v.14 no.12
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    • pp.66-72
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    • 1997
  • This paper aims at reviewing the possibility application over normal or abnormal, detection used by AE and the wear characteristics of grinding process. In this study, when diamond bur in dentistry with chosen grinding conditions were tuned at grinding. The variation of grinding resistance and AE signal is detected by the use of AE measuring system. The tests are carried out in accordance with diamond burs and workpiece: arcyl and bovine. According to the experiment results, the following can be expected: AE has the possibility to detect the state normality and abnormality. Hpwever, the grinding resistance measuring can find it difficult to detect it. It can be accurately excepted from AE occurrence pattern in contact start point of diamond bur and bovine, grinding condition and derailment point. It is known that AErms is well compatible with grinding resistance. According to the increase of the material removal rate, the specific energy of the diamond bur is inclined to dectease and the grinding resistance has a tendency to increase.

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The Acoustic Emission Energy Analysis of Subambient Pressure Tri-Pad Slider

  • Pan Galina;Hwang Pyung;Xuan Wu
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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    • 2004.11a
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    • pp.139-142
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    • 2004
  • The object of the present work is the acoustic emission energy analysis of subambient pressure tri-pad slider. Head/disk interaction during start/stop and constant speed were detected by using acoustic emission (AE) test system The frequency spectrum analysis is performed using the AE signal obtained during the head/disk interaction Natural frequency analysis was performed using Ansys program. Acoustic emission energy was calculated for the slider modes.

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Monitoring System Development of Abnormal State in Air Conditioner Compressor

  • 이감규;정지홍;강명창;김정석
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1997.04a
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    • pp.186-189
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    • 1997
  • To monitor abnormal state of rotary compressor, methods for acquisition and processing of Acoustic Emission(AE) signal are arranged and optimal AE parameter for monitoring is determined. The detecting method of abnormal compressor in real time is suggested and special-purpose minitoring system which can be applied to automatic manufacturing line is developed using one-chip microprocessor in low cost.

Optimal Supply of Grinding Fluid for Creepfeed Grinding (고능률 연삭을 위한 연삭유제 공급의 최적화)

  • 박재현;홍순익;하만경;송지복
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1996.11a
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    • pp.90-94
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    • 1996
  • Thermal problems of creep-feed grinding are more serious than regular grinding. So grinding fluid supply in creep-feed grinding is very important. Grinding fluid supply quantity is not linear with effectiveness because grinding wheel is porosity material and the grinding area is solid contact area. In this paper, by using AE signal, optimal quantity of fluid supply was determined. And surface characteristics of wet creep-feed grinding were analized.

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Detection and Evaluation of Microdamages in Composite Materials Using a Thermo-Acoustic Emission Technique (열-음향방출기법을 이용한 복합재료의 미세손상 검출 및 평가)

  • 최낙삼;김영복;이덕보
    • Composites Research
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    • v.16 no.1
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    • pp.26-33
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    • 2003
  • Utilizing a thermo-acoustic emission (AE) technique, a study on detection and evaluation of microfractures in cross-ply laminate composites was performed. Fiber breakages and matrix fractures formed by a cryogenic cooling at $-191^{\circ}C$ were observed with ultrasonic C-scan, optical and scanning electron microscopy. Those microfractures were monitored in a non-destructive in-situ state as three different types of thermo-AE signals classified on the basis of Fast-Fourier Transform and Short-Time Fourier Transform. Thus, it was concluded that real-time estimation of microfracture processes being formed during cryogenic cooling could be accomplished by monitoring such different types of thermo-AEs in each time-stage and then by analyzing thermo-AE behaviors for the respective AE types on the basis of the AE signal analysis results obtained during thermal heating and cooling load cycles.

CMP process monitoring system using AE sensor (AE를 이용한 CMP 공정 감시에 관한 연구)

  • Park, Sun-Joon;Kim, Sung-Ryul;Park, Boum-Young;Lee, Hyun-Seop;Jeong, Hea-Do
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2007.11a
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    • pp.51-52
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    • 2007
  • This paper compared wired Acoustic Emission (AE) signals with wireless AE signals. According to the material and process condition, each process signal has distinguishable characteristic to show each removal phenomenon. Therefore, wired and wireless AE sensors having different bandwidth are complementary for CMP process monitoring. Especially, the AE sensor was used to investigate abrasive and molecular-scale phenomena during CMP process, which was compatible to acquire high level frequency. In experiment, wireless AE system was used to get signals in rotary system, using bluetooth. But, it is possible to acquire only RMS signals, which can not analyze abrasive and molecular-sale phenomena. Second, wired AE system was installed using mercury slip-ring, which is suitable not only for rotation equipment but also for acquiring original signals. The acquired signals were analyzed by FFT for understanding of abrasive and molecular revel phenomena in CMP process, finally, we verified that two types of AE sensor with different bandwidth were complementary for CMP process monitoring.

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A Study on the P Wave Arrival Time Determination Algorithm of Acoustic Emission (AE) Suitable for P Waves with Low Signal-to-Noise Ratios (낮은 신호 대 잡음비 특성을 지닌 탄성파 신호에 적합한 P파 도달시간 결정 알고리즘 연구)

  • Lee, K.S.;Kim, J.S.;Lee, C.S.;Yoon, C.H.;Choi, J.W.
    • Tunnel and Underground Space
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    • v.21 no.5
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    • pp.349-358
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    • 2011
  • This paper introduces a new P wave arrival time determination algorithm of acoustic emission (AE) suitable to identify P waves with low signal-to-noise ratio generated in rock masses around the high-level radioactive waste disposal repositories. The algorithms adopted for this paper were amplitude threshold picker, Akaike Information Criterion (AIC), two step AIC, and Hinkley criterion. The elastic waves were generated by Pencil Lead Break test on a granite sample, then mixed with white noise to make it difficult to distinguish P wave artificially. The results obtained from amplitude threshold picker, AIC, and Hinkley criterion produced relatively large error due to the low signal-to-noise ratio. On the other hand, two step AIC algorithm provided the correct results regardless of white noise so that the accuracy of source localization was more improved and could be satisfied with the error range.

Development of Feature Selection Method for Neural Network AE Signal Pattern Recognition and Its Application to Classification of Defects of Weld and Rotating Components (신경망 AE 신호 형상인식을 위한 특징값 선택법의 개발과 용접부 및 회전체 결함 분류에의 적용 연구)

  • Lee, Kang-Yong;Hwang, In-Bom
    • Journal of the Korean Society for Nondestructive Testing
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    • v.21 no.1
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    • pp.46-53
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    • 2001
  • The purpose of this paper is to develop a new feature selection method for AE signal classification. The neural network of back propagation algorithm is used. The proposed feature selection method uses the difference between feature coordinates in feature space. This method is compared with the existing methods such as Fisher's criterion, class mean scatter criterion and eigenvector analysis in terms of the recognition rate and the convergence speed, using the signals from the defects in welding zone of austenitic stainless steel and in the metal contact of the rotary compressor. The proposed feature selection methods such as 2-D and 3-D criteria showed better results in the recognition rate than the existing ones.

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