• 한국기계가공학회지
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• 제3권1호
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• pp.28-37
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• 2004

An on-line monitoring system of endmill failure such as weal, chipping, and fracture is developed using AE, cutting force Characteristic variations of AE and cutting force signals due to endmill failure are identified as follows. When endmill fracture occurs, AE count rate shows a rapid Increase in conjunction with a subsequent decrease while a standard deviation of the principal cutting force Increases significantly. The increase of AE count rate precedes the Increase of standard deviation of principal cutting force. Chipping results in relatively small increase and decrease of AE count rate without any significant variation of the cutting force Gradual increase of AE count rate and mean principal cutting force are Identified to be related with the wear of cutter. A cutter fracture detection algorithm is developed based on the present results. The signals me normalized to enhance the applicability of the algorithm to Wide those of fresh cutters, and qualitative characteristics of AE signals encountered at the moment of fracture are employed. It is demonstrated that the algorithm can detect the cutter fracture successfully.

• 한국생산제조학회지
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• 제25권3호
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• pp.211-216
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• 2016

Al alloys are useful materials having high specific strength and are used in machining of parts having thin-walled structures for weight reduction in aircraft, automobiles, and portable devices. In machining of thin-walled structures, it is difficult to maintain dimensional accuracy because machining deformation occurs because of cutting forces and heat in the cutting zone. Thus, cutting conditions and methods need to be investigated and cutting signals need to be analyzed to diagnose and minimize machining deformation and thereby enhance machining quality. In this study, an investigation on cutting conditions to minimize machining deformation and an analysis on characteristics of cutting signals when machining deformation occurs are conducted. Cutting signals for the process are acquired by using an accelerometer and acoustic emission (AE) sensor. Signal characteristics according to the cutting conditions and the relation between machining deformation and cutting signals are analyzed.

• 대한기계학회논문집A
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• 제33권5호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1570-1575
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• 1991

The feasibility of using an adaptive resonance network (ART2) with unsupervised learning capability for too] wear detection in turning operations is investigated. Specifically, acoustic emission (AE) and cutting force signals were measured during machining, the multichannel AR coefficients of the two signals were calculated and then presented to the network to make a decision on tool wear. If the presented features are significantly different from previously learned patterns associated with a fresh tool, the network will recognize the difference and form a new category m worn tool. The experimental results show that tool wear can be effectively detected with or without minimum prior training using the self-organization property of the ART2 network.

• 한국기계가공학회지
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• 제14권6호
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• pp.1-6
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• 2015

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

• 한국생산제조학회지
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• 제5권2호
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• pp.38-45
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• 1996

This study was focused on the prediction of the approprite tool life by clarifying the correlation between progressive drill wear and AE signal. on drilling SM45C the following results have been obtained; RMSAE, AE CUM-CNTS had a tendency to increase slowly according to wear size, at 1000rpm, 150mm/min However, these increased suddenly in the range of 0.20~0.22mm wear, about 102 holes and had a tendency to go up and down until the drilling was impossible. The sudden increase of AE signals shows that something is wrong and it is closely connected with drill wear and chipping. It also makes the working surface bad From the above results, AE signals could be used to monitor the drill's condition and to determine the right time to change tools.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2002년도 추계학술발표대회 논문집
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• pp.151-154
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• 2002

The objective of this study is to find the change of signal characteristics of matrix cracks due to the different specimen shapes. As the concept of the smart structure, monitoring of acoustic emission (AE) can be applied to inspect the fracture of the structures in operating condition using built-in sensors. To understand the characteristics of matrix crack signals, we performed tensile tests by changing the thickness and width of the specimens. This paper describes the implementation of time-frequency analysis such as wavelet transform (WT) fur the quantitative evaluation of fracture signals. The experimental result shows the distinctive signal features in frequency domain due to the different specimen shapes.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1999년도 특별강연 및 춘계학술발표대회 개요집
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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.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 하계학술대회 논문집
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• pp.645-650
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• 2000

The discrete wavelet transform is utilized as processing of neural network(NN) to identifying aging state of internal partial discharge in transformer. The discrete wavelet transform is used to produce wavelet coefficients which are used for classification. The mean values of the wavelet coefficients are input into an back-propagation neural network. The networks, after training, can decide if the test signals is aging early state or aging last state, or normal state.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 추계학술대회 논문집 전기물성,응용부문
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• pp.200-202
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• 2002

The use of ultrasound is proposed as the most economic and practical nondestructive test method for the detectin of electrical degradation in insulating materials. This paper has studied identification and characterization of partial discharge signals according to defects in GIS using AE(acoustic emission)sensor. Analysis of PD signals use $\psi-v-n$, skewness and kurtosis.