• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.1
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• pp.31-36
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• 2009

Micro-machining technology has been adopted for shape accuracy of micrometer and sub-micrometer scale, surface roughness of tens nanometer in industries. In micro-machining process the quality of machined surface is derived from machining condition and tooling. This paper investigates AE(acoustic emission) and cutting force signals according to machined surface quality related to machining condition. Machined surface quality was analyzed by the AE and cutting force parameter which reflect surface morphology. The characteristics of signal were extracted for process optimization by monitoring both the tool condition and the machined surface texture in micro end milling process.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.189-198
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• 1998

Recently, monitoring of tool life is a matter of common interesting because tool life affects precision, productivity and cost in machining process. Especially flank wear has a direct effect on cutting mechanism, so the various pattern of cutting force is obtained experimentally according to variation of wear condition. By investigating cutting force signal, AR(Autoregressive) modeling and correlation dimension analysis is conducted in turning operation. In this modeling and analysis, we extract features through 6th AR model, correlation integral and normalized correlation integral. After the back-propagation model of the neural network is utilized to monitor tool life according to flank wear. As a result. a very reliable classification of tool life was obtained.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.149-156
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• 1995

This paper presents a method of detecting chatter vibration in end-mill process. The detecting system consists of an adaptive signal processing scheme which uses an autore- gressive time-series model and a neural network is proposed and is verified its effectiveness by using acceleration and cutting force signals recorded during slotting in end-mill operations. Expeerimental results indicate that the proposed system provides excellent detection when chatter is occured within the ranges of cutting conditions considered in this study and an effectiveness of the integration of signals is confirmed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.259-264
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• 1995

Recently, in order to achieve high flexibilty, monitoring and control strategies of a new type have been developed. This paper investigates the fesability of using scoustic emission signal analysis for the detection of built-up edge during machining. Results for maching SM45C steel show that the presence of a built-up edge can significantil affect the generation of acoustic emission in metal cutting. When the cutting speed comes to the conditions conducive to development of built-up edge, it is shown that the slope of curve-fitted AErms signal undergoes a change. The fesability of utilizing AErms in built-up edge sensing is sugested.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.26-33
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• 1999

Automatic monitoring of cutting process is one of the most important technologies for increasing the stability and the reliability of unmanned manufacturing system. In this study, basic methods which use the acoustic emission (AE) signals and cutting forces were proposed to monitor flank wear (width of flank wear) quantiatively. First, in order to detect flank wear, it was investigated that the influence of cutting conditions, that is, cutting velocity, feed and depth of cut, on AE signals (${AE_rms}$) and cutting forces. Furthermore, the relation between flank wear and the measured signals (${AE_rms}$, cutting force) was discussed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.108-114
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• 2007

A new cutting force estimating approach and machining state monitoring examples are presented which uses a cylindrical displacement sensor built into the spindle. To identify the tool-spindle system dynamics with frequency up to 2 kHz, a home-built electro-magnetic exciter is used. The result is used to build an algorithm to extract the dynamic cutting force signal from the spindle error motion; because the built-in spindle sensor signal contains both spindle-tool dynamics and tool-workpiece interactions. This sensor is very sensitive and can measure broadband signal without affecting the system dynamics. The main characteristic is that it is designed so that the measurement is irrelevant to the geometric errors by covering the entire circumferential area between the target and sensor. It is also very simple to be installed. Usually the spindle front cover part is copied and replaced with a new one with this sensor added. It gives valuable information about the operating condition of the spindle at any time. It can be used to monitor cutting force and chatter vibration, to predict roughness and to compensate the form error by overriding spindle speed or feed rate. This approach is particularly useful in monitoring a high speed machining process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.9
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• pp.839-849
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• 2015

This paper deals with an experimental technique for monitoring machining conditions by analyzing cutting-force vibration measured at a milling machine. This technique is based on the relationship of the cutting-force vibrations with the feed rate and cutting depth as reported earlier. The measurement system consists of dynamic force transducers and a signal amplifier. The analysis system includes an oscilloscope and a computer with a LabVIEW program. Experiments were carried out at various feed rates and cutting depths, while the rotating speed was kept constant. The magnitude of the cutting force vibration component corresponding to the number of cutting edges multiplied by the frequency of rotation was linearly correlated with the machining conditions. When one condition of machining is known, another condition can be identified by analyzing the cutting-force vibration.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.817-821
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• 1997

Ever since the nonlinearity of machine tool dynamics was established, researchers attempted to make use of this fact to devise better monitoring, diagnostics and system, which were hitherto based on linear models. Theory of chaos, which explains many nonlinear phenomena comes handy for furthering the analysis using nonlinear model. In this study, measuring system will be constructed using multi-sensor (Tool Dynamometer, Acoustic Emission) in end millingprocess. Then, it will be verified that cutting force is low-dimensional deterministic chaos calculating Lyapunov exponents, Fractal dimension, Embedding dimension. Aen it will be investigated that the relations between characteristic parameter caculated form sensor signal and tool wear.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.300-306
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• 2005

This paper presents the methodology for measuring eccentricity of the machined cylindrical part using CCS(cylindrical capacitance spindle sensor) signal in the CNC turning process. We use capacitance type sensor to take full advantage of averaging effect by using large capacitance area to encompass the whole side of a sensor. The intentionally proposed initial eccentricity is applied to the experimental testpieces, and their resultant relationships between CCS orbits and eccentricities are investigated. As a result, the possibility as a automatic detection apparatus for the CNC lathe is considered based on the linearities of CCS signal and magnitude of eccentricity of machined cylindrical surfaces.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.5
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• pp.1-6
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• 2010

The wavelet transform is a popular tool for studying intermittent and localized phenomena in signals. In this study the wavelet transform of cutting force signals was conducted for the detection of a tool failure in turning process. We used the Daubechies wavelet analyzing function to detect a sudden change in cutting signal level. A preliminary stepped workpiece which had intentionally a hard condition was cut by the inserted cermet tool and a tool dynamometer obtained cutting force signals. From the results of the wavelet transform, the obtained signals were divided into approximation terms and detailed terms. At tool failure, the approximation signals were suddenly increased and the detailed signals were extremely oscillated just before tool failure.