• Title/Summary/Keyword: acoustic emission parameters

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SENSORS IN DEVURRING AUTOMATION

  • Lee, Seoung-Hwan
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 1999.10a
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    • pp.560-564
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    • 1999
  • Burr sensing for burr size measurement and deburring process control is one of the essential elements in an automated deburring procedure. This paper presents the implementation of capacitance sensing and acoustic emission (AE) to deburring. The first application is the "on-line" measurement of burrs using a capacitance sensor. A non-contact capacitance gauging sensor is attached to an ultra precision milling machine which was used as a positioning system. The setup is used to measure burr profiles along machined workpiece edges. The proposed scheme is shown to be accurate, easy to setup, and with minor modifications, readily applicable to automatic deburring processes. As the second example, AE signals were sampled and analyzed for the sensor feedback of a precision deburring process - laser deburring -. The results, such as the sensitivity of AE signals to burr shapes and edge detection capability show a clear correlation between physical process parameters and the AE signals. A subsequent control strategy for deburring automation is also briefly discussed.

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Study on characteristics of SCC and AE signals for the weld HAZ of HT-60 steel under corrosion control (부식제어하에서 HT-60강 용접부의 SCC 및 AE 신호 특성에 관한 연구)

  • 나의균;고승기
    • Proceedings of the KWS Conference
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    • 1999.05a
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    • pp.241-244
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    • 1999
  • The purpose of this study is to examine the characteristics of stress corrosion cracking(SCC) and acoustic emission(AE) signals for the weld HAZ of HT-60 steel under corrosion control in synthetic seawater. Corrosive environment was controlled by potentiostat, and SCC experiment was conducted using a slow strain rate test method at strain rate of 10$^{-5}$ /sec. In order to verify the miroscopic fracture behaviour of the weldment during SCC phenomena, AE test was done simultaneously. Besides, correlationship between mechanical parameters and AE ones was investigated. In case of the parent, reduction of area(ROA) at -0.5V was samller than any other applied voltage such as -0.8V and -1.1V. In addition, reduction of area for the PWHT specimens at -0.8mV was larger than that of the weldment due to the softening effect according to PWHT. In case of the weldment, a lots of events was produced because of the singularities of the weld HAZ compared with the parent.

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Effects of Welding Parameters on the Weld Strength and Acoustic Emission in Friction Welding (마찰용접에 있어서 용접강도와 AE에 미치는 용접조건의 영향에 관한 연구)

  • Oh, Sae Kyoo
    • Journal of Advanced Marine Engineering and Technology
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    • v.7 no.1
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    • pp.23-33
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    • 1983
  • 경제성과 압접성능의 우수성 때문에 일반 산업기계, 방위산업기계 및 우주항공기계등의 부품생산에 응용되고 있는 마찰 용접에 있어서, 현재 주 관심사 중의 하나는 용접강도에 대한 신뢰성 높은 공정중 비파괴 검출이며 이들의 실용화를 위한 정량적 해석이다. 그러나 이러한 연구는 아직 개발 완성되지 못하고 있다. 본 연구에서는, AE법에 의한 용접강도의 공정중 비파괴적 QC시스템 개발을 최종 목적으로 한 설계자료를 얻기 위하여, 이종강의 봉과 봉, 관과 관의 마찰용접강도와 AE 총누적량에 용접조건이 미치는 영향이 실험적으로 조사되었고, 회전 속도를 매개 변수로 하여 용접강도와 AE 총누적량과의 정량관계가 수립되었다.

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A Study on Insulation Degradation Diagnosis Using a Neural Network (신경회로망을 이용한 절연 열화진단에 관한 연구)

  • 박재준
    • The Journal of Information Technology
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    • v.2 no.2
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    • pp.13-22
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    • 1999
  • In this paper, we purpose automatic diagnosis in online, as the fundamental study to diagnose the partial discharge mechanism and to predict the lifetime by introduction a neural network. In the proposed method, we use AE(acoustic emission) sensing system and calculate a quantitative statistic parameter by pulse number and amplitude. Using statically parameters such as the center of gravity(G) and the gradient if the discharge distribute(C), we analyzed the early stage and the middle stage. the quantitative statistic parameters are learned by a neural network. The diagnosis of insulation degradation and a lifetime prediction by the early stage time are achieved. On the basis of revealed excellent diagnosis ability through the neural network learning for the patterns during degradation, it was proved that the neural network is appropriate for degradation diagnosis and lifetime prediction in partial discharge.

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Characterization of Magnetic Abrasive Finishing Using Sensor Fusion (센서 융합을 이용한 MAF 공정 특성 분석)

  • Kim, Seol-Bim;Ahn, Byoung-Woon;Lee, Seoung-Hwan
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.33 no.5
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    • pp.514-520
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    • 2009
  • In configuring an automated polishing system, a monitoring scheme to estimate the surface roughness is necessary. In this study, a precision polishing process, magnetic abrasive finishing (MAF), along with an in-process monitoring setup was investigated. A magnetic tooling is connected to a CNC machining to polish the surface of stavax(S136) die steel workpieces. During finishing experiments, both AE signals and force signals were sampled and analysed. The finishing results show that MAF has nano scale finishing capability (upto 8nm in surface roughness) and the sensor signals have strong correlations with the parameters such as gap between the tool and workpiece, feed rate and abrasive size. In addition, the signals were utilized as the input parameters of artificial neural networks to predict generated surface roughness. Among the three networks constructed -AE rms input, force input, AE+force input- the ANN with sensor fusion (AE+force) produced most stable results. From above, it has been shown that the proposed sensor fusion scheme is appropriate for the monitoring and prediction of the nano scale precision finishing process.

Influence of strain rate on the acoustic emission signal characteristics in corrosive environment (부식환경하에서 음향방출신호 특성에 미치는 변형률속도의 영향)

  • Yu, Hyo-Seon;Jeong, Se-Hui
    • Korean Journal of Materials Research
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    • v.5 no.1
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    • pp.12-21
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    • 1995
  • The study was performed to study the effects of strain rate on acoustics emission( AE) during bulging test in corrosive environmentsynthetic sea water. The strain rates used were in the range $4 \times 10^{-6}S^{-1}$ to $1 \times 10^{-4} \times S^{-1}$ and the parameters used to evaluate AE signal characteristics were AE hit and amplitude. It can be observed that the cumulative AE hit and average amplitude during fracture process increase highly at decreasing strain rates while the equivalent fracture strain and the crack length of circumferencial direction become decrease. The peak point of AE signal characteristic parameters approach to the first half of test. When the average amplitude per unit equivalent fracture strain was above 20dB, it was definitly observed stress corrosion cracking phenomena. Additional, we knew that the AE test had the possibility to evaluate SCC susceptibility with various strain rates.

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Classification of Acoustic Emission Signals for Fatigue Crack Opening and Closure by Artificial Neural Network Based on Principal Component Analysis (주성분 분석과 인공신경망을 이용한 피로균열 열림.닫힘 시 음향방출 신호분류)

  • Kim, Ki-Bok;Yoon, Dong-Jin;Jeong, Jung-Chae;Lee, Seung-Seok
    • Journal of the Korean Society for Nondestructive Testing
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    • v.22 no.5
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    • pp.532-538
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    • 2002
  • This study was performed to classify the fatigue crack opening and closure for three kinds of aluminum alloy using principal component analysis (PCA). Fatigue cycle loading test was conducted to acquire AE signals which come from different source mechanisms such as crack opening and closure, rubbing, fretting etc. To extract the significant feature from AE signal, correlation analysis was performed. Over 94% of the variance of AE parameters could accounted for the first two principal components. The results of the PCA on AE parameters showed that the first principal component was associated with the size of AE signals and the second principal component was associated with the shape of AE signals. An artificial neural network (ANN) an analysis was successfully used to classify AE signals into six classes. The ANN classifier based on PCA appeared to be a promising tool to classify AE signals for fatigue crack opening and closure.

Friction Welding and AE Characteristics of Magnesium Alloy for Lightweight Ocean Vehicle (해양차량 경량화용 마그네슘합금의 마찰용접 및 AE 특성)

  • Kong, Yu-Sik;Lee, Jin-Kyung;Kang, Dae-Min
    • Journal of Ocean Engineering and Technology
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    • v.25 no.6
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    • pp.91-96
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    • 2011
  • In this paper, friction welded joints were constructed to investigate the mechanical properties of welded 15-mm diameter solid bars of Mg alloy (AZ31B). The main friction welding parameters were selected to endure reliable quality welds on the basis of visual examination, tensile tests, impact energy test, Vickers hardness surveys of the bonds in the area and heat affected zone (HAZ), and macrostructure investigations. The study reached the following conclusions. The tensile strength of the friction welded materials (271 MPa) was increased to about 100% of the AZ31B base metal (274 MPa) under the condition of a heating time of 1 s. The metal loss increased lineally with an increase in the heating time. The following optimal friction welding conditions were determined: rotating speed (n) = 2000 rpm, heating pressure (HP) = 35 MPa, upsetting pressure (UP) = 70 MPa, heating time (HT) = 1 s, and upsetting time (UT) = 5 s, for a metal loss (Mo) of 10.2 mm. The hardness distribution of the base metal (BM) showed HV55. All of the BM parts showed levels of hardness that were approximately similar to friction welded materials. The weld interface of the friction welded parts was strongly mixed, which showed a well-combined structure of macro-particles without particle growth or any defects. In addition, an acoustic emission (AE) technique was applied to derive the optimum condition for friction welding the Mg alloy nondestructively. The AE count and energy parameters were useful for evaluating the relationship between the tensile strength and AE parameters based on the friction welding conditions.

The detection of cavitation in hydraulic machines by use of ultrasonic signal analysis

  • Gruber, P.;Farhat, M.;Odermatt, P.;Etterlin, M.;Lerch, T.;Frei, M.
    • International Journal of Fluid Machinery and Systems
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    • v.8 no.4
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    • pp.264-273
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    • 2015
  • This presentation describes an experimental approach for the detection of cavitation in hydraulic machines by use of ultrasonic signal analysis. Instead of using the high frequency pulses (typically 1MHz) only for transit time measurement different other signal characteristics are extracted from the individual signals and its correlation function with reference signals in order to gain knowledge of the water conditions. As the pulse repetition rate is high (typically 100Hz), statistical parameters can be extracted of the signals. The idea is to find patterns in the parameters by a classifier that can distinguish between the different water states. This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and two Francis model test turbines all at LMH in Lausanne. From the signal raw data several statistical parameters in the time and frequency domain as well as from the correlation function with reference signals have been determined. As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest. It is shown that two to three signal characteristics, two from the signal itself and one from the correlation function are in many cases sufficient for the detection capability. The final goal is to combine these results with operating point, vibration, acoustic emission and dynamic pressure information such that a distinction between dangerous and not dangerous cavitation is possible.

Nondestructive Evaluation on Strength Characteristic and Damage Behavior of Al 7075/CFRP Sandwich Composite (Al 7075/CFRP 샌드위치 복합재료의 강도 및 손상특성에 대한 비파괴 평가)

  • Lee, Jin-Kyung;Yoon, Han-Ki;Lee, Joon-Hyun
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.26 no.11
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    • pp.2328-2335
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    • 2002
  • A hybrid composite material has many potential usage due to the high specific strength and the resistance to fatigue, when compared to other composite materials such as fiber reinforced plastic(FRP) and metal matrix composite(MMC). However, the fracture mechanism of hybrid composite material is extremely complicated because of the bonding structure of metals and FRP. In this study, Al 7075 sheets and carbon epoxy preprags were used to fabricate the hybrid composite. Recently, nondestructive technique has been used to evaluate the fracture mechanism of these composite materials. AE technique was used to clarify the microscopic damage behavior and failure mechanism of A17075/CFRP hybrid composite. It was found that AE paralneters such as AE event, energy and amplitude were effective to evaluate the failure process of Al 7075/CFRP composite. In addition, the relationship between the AE signal and the characteristics of fracture surface using optical microscope was discussed.