• 한국공작기계학회논문집
• /
• 제14권3호
• /
• pp.1-7
• /
• 2005

The prediction for tool life is one of the most important factors for increasing reliability, stability, and productivity of manufacturing system. This paper deals with a tool life prediction method in view of reliability assessment for cutting tools. In this study, flank wear was focused among multi-factors deciding the tool wear state. First, tool life was predicted by correlation between flank wear and cutting time, based on the extended Taylor tool life equation of turning, including parameters of cutting speed, feed rate, and cutting depth. Second, each of cutting conditions of end-milling was equivalently converted to apply ball end-mill data to the extended Taylor equation. The web-based prediction program for tool life was developed as one of reliability assessment programs for machine tools.

• 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.

• 한국윤활학회:학술대회논문집
• /
• 한국윤활학회 1997년도 제25회 춘계학술대회
• /
• pp.25-32
• /
• 1997

The neural network was applied to identify wear debris generated from the lubricated machine moving surface. The wear test was carried out under different experimental conditions. In order to describe characteristics of debris of various shapes and sizes. The four parameter(50% volumetric diameter, aspect, roundness and reflec- tivity) of wear debris are used as inputs to the network and learned the friction condition of five values (material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network.

• Tribology and Lubricants
• /
• 제12권3호
• /
• pp.6-11
• /
• 1996

There has been a growing concern for the use of mineral oil based lubricants because of the worldwide interest in environmental issues. This has prompted the use of vegetable oils as more environmentally acceptable base fluids. In view of this, four-ball wear test was carried out to investigate the tribological behavior of selected vegetable oils blended with ZDDP, TCP and DBP under high speed and temperate conditions. Of the additive evaluated, the new additive, DBP has provided antiwear performance superior to the two other additives more commonly used. This superior performance by DBP is probably caused by different wear mechanism. This wear mechanism has been evidenced by the surface analysis of worn balls conducted using optical microscope and EDAX.

• 한국윤활학회:학술대회논문집
• /
• 한국윤활학회 1998년도 제27회 춘계학술대회
• /
• pp.180-189
• /
• 1998

This paper was undertaken to do shape analysis of wear debris on oiliness agent and extreme pressure agent. The lubricating wear test was performed under different experimental conditions using the wear test device was made in our laboratory and were- specimens of the pin on disk type was rubbed in paraffine series base oil by materials, varying applied load, sliding distance, oil additives such as stearine acid, DBDS, TCP. The four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) on a kind of the additives are different on applied load and sliding distance and Its are affected by absorbed film and reaction film. DBDS and TCP have a role of extreme pressure agent but a role of absorbed film of stearic acid decrease in high load. The maximum wear volume on applied load be in existence in three kinds of the specimens because of reaction characteristics of the additives.

• 한국생산제조학회지
• /
• 제20권1호
• /
• pp.53-59
• /
• 2011

End milling is available for machining the various shape of products and has been widely applied in many manufacturing industries. The quality of products depends on a machine tool performance and machining conditions. Recognition characteristics of the cutting condition is becoming a critical requirement for improving the utilization and flexibility of present-day CNC machine tools. The measurement of tool wear would be performed by coordinate-measuring machine(CMM). However, the usage of CMM requires much time and cost. In order to overcome the difficulties, on-line measurement(OLM) system was applied for a tool wear measurement. This study shows a reliable technique for the reduction of machining error components by developing a system using a CCD camera and machine vision to be able to precisely measure the size of tool wear in flat end milling for CNC machining. The CCD camera and machine vision attached to a CNC machine can determine tool wear quickly and easily.

• 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.

• 한국공작기계학회논문집
• /
• 제17권1호
• /
• pp.77-85
• /
• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2000년도 춘계학술대회 논문집
• /
• pp.613-616
• /
• 2000

This paper deals with monitoring of grinding wheel wear in surface grinding process. A laser scanning micrometer is used to measure the circumferential shape as well as the axial shape of grinding wheel. The monitoring system is applied to two kinds of grinding methods: plunge and traverse grinding. Through experiments, it is found that measurement of grinding wheel wear reveals information of roughness of ground surface and the adequate dressing time. In addition, monitoring of grinding wheel wear makes it possible to identify abnormal grinding conditions.

• Journal of Mechanical Science and Technology
• /
• 제20권5호
• /
• pp.583-590
• /
• 2006

It is hard to inspect the state of lubrication of an automobile transmission visually. Thus, it is necessary to develop a new inspection method. Wear debris can be collected from the lubricants of an operating transmission of an automobile, and its morphology will be directly related to the friction condition of the interacting materials from which the wear debris originated in the lubricated transmission. In this study, wear debris in lubricating oil are extracted by membrane filter $(0.45{\mu}m)$, and the quantitative values of shape parameters of wear debris are calculated by digital image processing. These shape parameters are studied and identified by an artificial neural network algorithm. The results of the study may be applicable to the prediction and diagnosis of the operating condition of transmission gear.