• Journal of Korea Foundry Society
• /
• v.27 no.5
• /
• pp.217-220
• /
• 2007

절삭특성은 재료를 원하는 형상으로 가공하기 위해 재료의 불필요한 부분을 제거할 경우 그 가공성이 쉽거나 어려움에 대한 정도로 전의될 수 있는데 동합금 소재의 절삭특성은 절삭시 발생된 칩의 형상이나 길이를 측정하거나 또는 공구계에 부착된 토오크 미터에 의해 절삭력을 간접적으로 측정하는 방법 등이 사용되어 오고 있다. 상기의 평가방 법은 절삭특성의 간접적인 평가방법이라는 한계와 정확도에 문제가 있는 실정이다. 본 연구에서는 압전형 공구동력계(Piezoelectric Tool Dynamometer)를 쾌삭인청동합금 피절삭물에 직접 부착하여 절삭가공시 절삭력은 정량적으로 직접 측정하고자 하였다. 쾌삭인청동합금의 소둔 열처리 시간이 증가할수록 결정립의 성장에 의한 연화현상과 납입자의 군집화(Clustering)는 관찰되었으나 그로 인한 절삭력 및 절삭에 필요한 에너지의 변화는 뚜렷하지 않았다.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1992.10a
• /
• pp.344-348
• /
• 1992

This paper presents a new methodology for on-line tool breakage detection by sensor fusion concept of an acoustic-emission (AE) sensor. A built-in piezoelectric force sensor was used to measure cutting force instead of a tool dynamometer to preserve the machine tool dynamics. he sensor was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. When a tool is broken, the explicit changes of signals' pattern take place. A burst-type AE signal increases abruptly. Followingly, a cutting force drops significantly. Therefore a burst of AE signal is used as a triggering signal to inspect the following cutting force. Significant drop of cutting force is utilized to detect tool breakage. The algorithm was implemented in a DSP board for in-process tool breakage detection. The proposed monitoring system was capable of a good applicable tool breakage detection.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.3
• /
• pp.130-143
• /
• 1995

This paper presents a new methodology for on-line tool breadage detection by sensor fusion of an acoustic emission (AE) sensor and a built-in force sensor. A built-in piezoelectric force sensor, instead of a tool dynamometer, was used to measure the cutting force without altering the machine tool dynamics. The sensor was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. A burst of AE signal was used as a triggering signal to inspect the cutting force. A sighificant drop of cutting force was utilized to detect tool breakage. The algorithm was implemented on a DSP board for in-process tool breakage detection. Experiental works showed an excellent monitoring capability of the proposed tool breakage detection system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.10a
• /
• pp.40-45
• /
• 1999

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc.In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA (parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.9 no.5
• /
• pp.144-149
• /
• 2000

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.8
• /
• pp.115-121
• /
• 1997

The in-process detection of the tool wear is one of the most important technologies in completely auto- matic operation of machine tool. In this research, using the tools having flank wear, the dynamic compo- nent of cutting forces is considered to be available for identifying the cutting process. In order to investi- gate this relation in detail, the cutting forces in turning of workpiece made of aluminum were measured by dynamometer of piezoelectric type, and the dynamic components of cutting force were analyzed. The fre- quency analysis, probability density analysis and RMS analysis of the dynamic components were carried out independently. Through the experiments, the characteristics of the tool system have a large effect on the dynamic component of cutting forces. As a result, it is shown that the dynamic cutting force was able to detect flank wear accurately.

• Journal of the Korean Society for Precision Engineering
• /
• v.7 no.2
• /
• pp.85-94
• /
• 1990

In automatic metal cuttings, the chip control is one of the serious problems. So the automatic detection of chip forms is essential to the chip control in automatic metal cuttings. Cutting experiments were carried out under the variety of cutting conditions (cutting speed, feed, depth of cut and tool geometry) and with workpiece made of steel (S45C), and cutting forces were measured in-processing by using a piezoelectric type Tool Dynamometer. In this report, the frequency analysis of dynamic components, the upper frequency distributions, the ratio of RMS values, the numbers of null point and the probability density were calculated from the dynamic componeents of cutting forces filtered through various band pass filters. Experimental results showed that computer chip form monitoring system based on the cutting forces was designed and simulated and that 6 type of chip forms could be detected while in-process machining.

• International Journal of Precision Engineering and Manufacturing-Green Technology
• /
• v.5 no.5
• /
• pp.571-581
• /
• 2018

In the present study, monitoring of elliptical vibration cutting process by utilizing internal data in the ultrasonic elliptical vibration device without external sensors such as a dynamometer and displacement sensor is investigated. The internal data utilized here is the change of excitation frequency, i.e. resonant frequency of the device, voltages applied to the piezoelectric actuators composing the device, and electric currents flowing through the actuators. These internal data change automatically in the elliptical vibration control system in order to keep a constant elliptical vibration against the change of the cutting process. Correlativity between the process and the internal data is described by using a vibration model of ultrasonic elliptical vibration cutting and verified by several experiments, i.e. planing and mirror surface finishing of hardened die steel carried out with single crystalline diamond tools. As a result, it is proved that it is possible to estimate the elements of elliptical vibration cutting process, e.g. tool wear and machining load, which are important for stable cutting in such precision machining.