• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2004년도 춘계학술대회 논문집
• /
• pp.317-322
• /
• 2004

This paper deals with a prediction method of tool life in view of the reliability assessment. In this study, the flank wear was studied among multi-factors deciding the tool wear state. Firstly, tool lift was predicted by correlation between flank wear and cutting time, based on the extended Taylor tool life equation of turning data, including parameters of cutting speed, feed rate, and cutting depth. Secondly, each of cutting conditions of endmilling was equivalently converted to apply ball endmill data to the extended Taylor equation. The web-based reliability prediction program for tool lift is being developed as one of reliability assessment programs to for the machine tools.

• 한국생산제조학회지
• /
• 제4권2호
• /
• pp.18-24
• /
• 1995

In order to achieve the autimation and untended system of manufacturing process, it is necessary that the monitoring system check up the disorder of machine tool or the conditions of tool wear for the maximum use of cutting tool. In the metal cutting Process, AE signal is detected by AE sensor, then amplified and transmitted to an Locan-AT. The experiment was performed to SM25C and STS304 steels at uniform feedrate, cutting speed and depth of cut, The results of experimental data apparently showed emission intensity vary due to increasing of tool wear at the 165kHz, 200kHz in the SM25C and 140kHz, 165kHz, 200kHz, in the STS304 respectively Therefore, it is possible to predict the tool wear. This study is intended to suggest the way to the automation and untended system of machine tool through the system monitoring tool wear by using AE signal.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1996년도 추계학술대회 논문
• /
• pp.75-78
• /
• 1996

End Milling is avilable for machining the variable shape of products and has been widely applicated in many industries. To manufacture precise products a surface roughness has to be noticable as a improtant parameter. In end milling the research for tool wear has been insufficient because the tool shape and the cutting geometry are complicated. In this paper the pattern of endmill wear is investigated and the machinability is evaluated. As finding out the characteristics of cutting force and surface roughness the effect of endmill wear on machinability is investigated.

• 한국생산제조학회지
• /
• 제5권3호
• /
• pp.15-21
• /
• 1996

This study is focused on the prediction of appropriate tool life by clarifying the correlation between progressive tool wear and AE(Acoustic Emission) signals, while cutting stainless steel by end mill on the machining center. The results of this study were that RMSAE tends to increase linearly along with the increase of the cutting speed, and it was more sensitive to depth of cut than to the variation of feed rate at the same cutting conditions, and RMSAE increases around 0.21mm flank wear hereby AE-HIT also increases. AE signals depend upon tool wear and fracture from the above results. Therefore, the AE signals can be utilized in order to monitor the tool condition.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2008년도 춘계학술대회 논문집
• /
• pp.806-812
• /
• 2008

Many kinds of rolling-stocks that have various control methods are being operated by Seoulmetro which is a history of a city railroad rolling-stock. Seoulmetro is being faced with a wheel management comparing of other lines with a perpendicular wear of wheel and a side damage, and so on, by operating several cars at a loop line. This is causing maintenance expenses increase and deteriorating a fusibility of rolling-stock, for it has an effect on a rolling-stock using. A cutting pattern of wheel and a wear form affect the expected span of a wheel. A wheel cutting cause is classifed into cutting for reprofiling of a flange wear of wheel and for removing every kind defect which originates from wheel wear. In this study, Seoulmetro exhibit a stable rolling-stock use method and a reasonable management method of wheel, analysing wheel exchange condition and cutting management of wheel.

• 한국공작기계학회논문집
• /
• 제14권6호
• /
• pp.16-21
• /
• 2005

The cutting characteristics of hardened steel(AISI 52100) by PCBN tools is investigated with respect to cutting force, workpiece surface roughness and tool flank wear by the vision system. Hard Owning is carried out with various cutting conditions; spindle rotational speed, depth of cut and feed rate. Backpropagation neural networks(BPNs) are used for detection of tool wear. The input vectors of neural network comprise of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output is the tool wear state which is either usable or failure. The detection of the abnormal states using BPNs achieves $96.8\%$ reliability even when the spindle rotational speed and feedrate are changed.

• 한국정밀공학회지
• /
• 제12권12호
• /
• pp.120-129
• /
• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

• 한국생산제조학회지
• /
• 제4권2호
• /
• pp.31-37
• /
• 1995

This study is focused on the prediction of appropriate tool life by clarifying the correlation between progressive tool wear and AE signal. when rcutting SM45C by End mill in machining center. First of all, end mill have a problem that position of sensor sticking because it is revolution tool, but I think that it can be bained specific character according to sticking Sensor in the Vise. Consequently, the following results have been obtained; 1. Each cutting speed of feed rate over 0.1mm had a tendency to increase linearly according to the RMSAE 2. The level of AE signal at the same cutting area was more sensitive to depth of cut tharn the variation of feed rate 3. In the range of cutting duringqr about 75minqr atqr cutting speed 27m/min flankqr wear turns up aboutqr 0.21mm, aboutqr 0.29mm in the caseqr of about 65minqr at 33/min, qr hereby RMSAE increased rapidly at 0.2mm flank wear, also AE-HIT and CUM-CNTS.

• 한국정밀공학회지
• /
• 제12권3호
• /
• pp.5-14
• /
• 1995

In recent years, the automization of cutting machine tools has been developed very fast. Hance, the in-process detection of cutting condition is very important for automatic manufacturing system in factory. Acoustic Emission(AE) has been widely used in monitoring the cutting conditions, because of high sensitivity of AE signal and low cost of AE equipment. This experimental study deals with the relations between AE signal, cutting force charcteristics and tool wear in the machining of SUS304. Face milling operation is used for the analysis between tool wear and AE signal.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2000년도 춘계학술대회 논문집
• /
• pp.653-657
• /
• 2000

Machining performance in hard turning of hardened AISI M2 steel has been studied. Ceramic tools were used in the cutting tests without coolants and workpiece was prepared by heat treatment to increase its hardness up to Rc 60. Cutting state parameters such as cutting forces, temperature, and tool wear were measured in the experiments and effects of tool wear on cutting states were investigated.