• 대한기계학회논문집A
• /
• 제33권5호
• /
• pp.514-520
• /
• 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.

• 한국정밀공학회지
• /
• 제13권4호
• /
• pp.136-142
• /
• 1996

Automatic monitoring of cutting process is one of the most important technology in machining. AE sensing technology has been applied to monitoring process and proved to be effective in detecting tool abnor- malities such as tool wear and fracture. In this experimental study. AE signals were detected from the tool holder for continuous and interrupted cutting, which obtained from changing workpice material configuration, under control of constant cutting speed from CNC lathe. From statistical and frequency analysis, the AE signals were analyzed to obtaining the characteristics of continuous and interrupted cutting conditions and tool failure. The Kurtosis values decreased but RMS voltages increased as the cutting speed increased, in both continuous and interrupted cutting. RMS voltage is suddenly increased but Kurtosis value is suddenly decreased when tool failure condition. Power spectrum density of AE signals when tool failure reaches extreme value around 0.065 cycles/ .mu. m.

• 콘크리트학회지
• /
• 제10권6호
• /
• pp.203-211
• /
• 1998

고체내부의 미소파괴시 발생하는 탄성파 방출을 이용하는 음향방출기법은 구조물 또는 재료 내부의 미시적 변형기구를 이해하는데 매우 유익한 수단으로 최근 각 분야에서 다양하게 응용되고 있다. 따라서 본 연구에서는 모르타르 부재의 휨재하 시험시 부재 내부에 발생하는 미시적 손상거동 및 파괴특성을 시험시 연속적으로 모니터링한 AE 신호특성으로부터 평가하였다. 나아가 삼각법을 이용한 2차원 AE 발생원 위치추정으로부터 시험체 노치선단 주변에 대한 AE 발생원 위치를 명확히 하였으며 이들 결과로부터 미소균열의 성장 거동을 연속적으로 모니터링 하였다.

• 비파괴검사학회지
• /
• 제23권3호
• /
• pp.205-211
• /
• 2003

구조물의 안전성평가와 관련하여 비파괴평가 방법 중에서 음향방출 (acoustic emission, AE)기법에 의한 비파괴적 결함 발생 검출 기법은 다른 기법에 비해 비교적 새로운 평가기술로서 구조물의 이상 유무를 조기에 진단하고 주기적으로 감시할 수 있는 온-라인 모니터링에 적합하다. 그러나 한편, 국내에서는 아직까지 이에 대한 체계적인 연구가 미비한 실정이며, 대부분 값비싼 외국 시스템을 도입하여 사용하고 있는 실정이다 뿐 만 아니라 기존의 음향방출 시스템은 장치의 특성상 시스템이 매우 복잡하고 가격이 고가인 관계로 다양한 산업분야에서 실제 현장에 적용하기가 쉽지 않다. 따라서 본 연구에서는 기존의 음향방출 시스템이 가지고 있던 불필요한 낭비적인 요소들을 제거하고 실제 산업현장에 부합하는 위치표정 시스템을 개발하고자 하였다. 한편, 기존의 AE 시스템들은 위치표정을 위한 신호분석이 난해할 뿐만 아니라 박판 구조물의 경우는 위치표정이 매우 어렵다. 본 연구에서는 시간-주파수 해석에 널리 사용되고 있는 웨이블릿 기법을 이용하여 보다 정확한 위치표정을 진단하는 기법을 수행하였다.

• 비파괴검사학회지
• /
• 제28권3호
• /
• pp.254-262
• /
• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• 한국생산제조학회지
• /
• 제21권4호
• /
• pp.601-607
• /
• 2012

The magnetic abrasive polishing (MAP), for online monitoring with AE sensor attachment, was performed in this study. To predict the surface roughness after the magnetic abrasive polishing of NAK80, the signal data acquired from the AE sensor were analyzed. A dimensionless coefficient, which consisted of average of AErms and standard deviation of AE signal, was defined as a characteristic of the MAP and a prediction model was obtained using least square method. A neural network, which had multiple input parameters from AE signals and polishing conditions, was applied for predicting the surface roughness. As a result of this study, it was seen that there was very close correlation between the AE signal and the surface roughness in the MAP. And then on-line prediction of the surface roughness after the MAP of the NAK80 was possible by the developed prediction model.

• 대한용접접합학회:학술대회논문집
• /
• 대한용접접합학회 1995년도 특별강연 및 추계학술발표 개요집
• /
• pp.3-20
• /
• 1995

Development of Real-Time Quality Evaluation of Friction Welding by Acousitc Emission : Report 1 ABSTRACT : According as the friction welding has been increasingly applied in manufacturing various machine components because of its significant economic and technical advantages, one of the important concerns is the reliable quality monitoring method for a good weld quality with both joint strength and toughness in the process of its production. However no reliable nondestructive test method is available at present to determine the weld quality particularly in process of production. So this paper presents an experimental examination and quantitative analysis for the real-time evaluation of friction weld quality by acoustic emission, as a new approach which attempts finally to develop an on-line quality monitoring system design for friction welds using AE techniques. As one of the important results, it was confirmed, through this study, that AE techniques can be reliably applied to evaluating the friction weld qualify with 100% joint strength, as the cumulative AE counts occurring during welding period were quantitatively correlated with reliability at 95% confidence level to the joint strength of welds. Real-Time Evaluation of Automatic Production Quality Control for Friction Welding Machine : Report 2 Abstract : Both in-process quality control and high reliability of the weld is one of the major concerns in applying friction welding to the economical and qualified mass-production. No reliable nondestructive monitoring method is available at present to determine the real-time evaluation of automatic production quality control for friction welding machine. This paper, so that, presents the experimental examinations and statistical quantitative analysis of the correlation between the initial cumulative counts of acoustic emission(AE) occurring during plastic deformation period of the welding and the tensile strength of the welded joints as well as the various welding variables, as a new approach which attempts finally to develop an on-line (or real-time) quality monitoring system and a program for the process of real-time friction welding quality evaluation by initial AE cumulative counts. As one of the important results, it was well confirmed that the initial AE cumulative counts were quantitatively and cubically correlated with reliability of 95% confidence level to the joint strength of the welds, bar-to-bar (SCM4 to SUM31, SCM4 to SUM24L) and that an AE technique using initial AE counts can be reliably applied to real-time strength evaluation of the welded joints, and that such a program of the system was well developed resulting in practical possibility of real-time quality control more than 100% joint efficiency showing good weld with no micro-structural defects.

• 한국생산제조학회지
• /
• 제7권6호
• /
• pp.64-71
• /
• 1998

This paper investigates the relationship between cutting conditions and Acoustic Emission(AE) signals; $AE_{avg}$, $AE_{rms}$, $AE_{mode}$$AE_{avg}$ and $AE_{rms}$ are increased as the increasing of cutting velocity and depth of cut respectively. The new parameters, derived from $AE_{avg}$ and $AE_{rms}$, which may be used for the in-process detection of tool wear is discussed. It is also known that $AE_{mode}$

• 대한기계학회논문집A
• /
• 제24권6호
• /
• pp.1491-1498
• /
• 2000

In recent years, CFRP composite materials have been increasingly used in various fields of engineering because of a high specific strength and stiffness properties. Drilling is one of the most impo rtant cutting processes that are generally carried out on CFRP materials owing to the need for the structural integration. However, delamination are often occurred as one of the drilling damages. Therefore, there are needs studying for the relationships between CFRP drilling and delamination in order to avoid low strength of the structures and inaccuracies of the integration. In this study, AE signals and thrust forces were used for the evaluations of the delamination from a drilling process in [0/900]s CFRP materials. And the drilling damage processes were observed and measured by a real time monitoring technique with a video camera. From the results, we found that the relationships between the delamination from drilling and AE characteristics and drill thrust forces for [0/900]s CFRP composites. Also, we proposed the monitoring method for a visual analysis of drilling damages.

• 터널과지하공간
• /
• 제18권1호
• /
• pp.1-9
• /
• 2008

미소파괴음(Acoustic Emission, AE)과 미소지진음(Microseismic event, MS event)은 응력의 재분배에 의한 균열이 생성될 때 나오는, 순간적인 에너지 방출에 의한 탄성파이다. AE/MS 이벤트는 일반적으로 대규모의 파괴에 앞서 그 발생이 현저해지는 경향이 있다. 이들은 계측영역의 주파수 대역에 따라 구분되며, MS이벤트에 비해 상대적으로 고주파의 AE 신호는 보다 미세한 파괴를 검출할 수 있다. 일반적으로 암반구조물은 파괴되기까지 작은 변형이 발생하여 종래에 사용되고 있는 변위계측으로는 그 전조현상을 포착하기 어렵기 때문에 국부적인 파괴나 갑작스러운 파괴에 대한 사전예측이 어려운 현실이다. 그러나 AE/MS 이벤트의 파형을 측정할 수 있는 경우 암반구조물의 파괴를 사전에 예측할 수 있으며, 초동이 명확한 경우 미세한 파괴위치지점과 함께 파괴메커니즘의 규명도 가능하다. 본 보고에서는 AE/MS 이벤트에 대한 기본이론과 함께 이들 활용한 계측기술 개발현황과 적용사례 등을 소개한다.