• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 춘계학술대회 논문집
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• pp.245-250
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• 2002

A successful on-line monitoring system for conventional machining operations has the potential to reduce cost, guarantee consistency of product quality, improve productivity and provide a safer environment for the operator. In fee-shape machining, typical signs of tool problems such as vibration, noise, chip flow characteristics and visual signs are almost unnoticeable without the use of special equipment. These characteristics increase the importance of automatic monitoring in fine-shape machining; however, sensing and interpretation of signals are more complex. In addition, the shafts of the micro-tools break before the typical extensive cutting edge of the tool gets damaged. In this study, the existence of a relationship between the characteristics of the cutting force and tool usage was investigated, and tool breakage detection algorithm was developed and the fellowing results are obtained. In data analysis, didn't use a relative error compare which mainly used in established experiment and investigated tool breakage detection algorithm in time domain which can detect AE and cutting force signals more effective and accurate.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2001년도 추계학술대회(한국공작기계학회)
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• pp.14-18
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• 2001

Rise in cutting force causes tool damage and worsens product quality resulting in machining accuracy deterioration. Especially, fragile material cutting brings about breakage of material and worsens product surface quality. In this study, we trace the locus of cutting force and examine the machined surface corresponding to the cutting force loci. and build up a monitoring system for deciding normal operation or not of cutting process.

• 한국기계가공학회지
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• 제3권3호
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• pp.24-31
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• 2004

A successful on-line monitoring system for conventional machining operations has the potential to reduce cost, guarantee consistency of product quality, improve productivity and provide a safer environment for the operator. In fine-shape machining, typical signs of tool problems such as vibration, noise, chip flow characteristics and visual signs are almost unnoticeable without the use of special equipment. These characteristics increase the importance of automatic monitoring in fine-shape machining, however, sensing and interpretation of signals ar more complex. In addition, the shafts of the mini-tools break before the typical extensive cutting edge of the tool gets damaged. In this study, the existence of a relationship between the characteristics of the cutting force and tool usage was investigated, and tool breakage detection algorithm by LabVIEW was developed and the following results are obtained. It was possible to use a relative error compare which mainly used in established experiment and investigated tool breakage detection algorithm in time domain which can detect AE and cutting force signals more effective and accurate.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.40-45
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• 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.

• 한국정밀공학회지
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• 제15권11호
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• pp.9-20
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• 1998

In recent manufacturing process, the increase of productivity has been attempted by reducing machining time with the increase of cutting force. However, the excessive increase of cutting force can cause tool breakage and have a bad effect on both manufacturing machine and workpiece. Thus, it is necessary to estimate and control the cutting force in real time during the process. In this study, use of disturbance observer is proposed for the indirect cutting force estimation. The estimated cutting force is used for the real-time control of feedrate, making the actual cutting force follow the reference force command. Since the suggested method does not need an expensive sensor like a dynamometer, the method is expected to be used practically. Since the actual cutting force follow the reference force, resulting the reducing of the machining time the increase of productivity are also expected, and the quality of cutting surface has been improved due to the adjusted feedrate. Besides, an actual constant cutting force guarantees the prevention of tool breakage. To show the effectiveness of the suggested cutting force control method, an experimental setup has been made without sensor and applied to several workpieces. Experiments show that the suggested method is effective to cutting force control of a CNC machining center.

• 한국정밀공학회지
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• 제16권9호
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• pp.62-67
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• 1999

In milling, cutting tool ins directly attached to spindle and this tells that spindle can provide very useful information on the cutting tool condition such as wear or breakage. Since spindle is rotating at a high speed, measuring spindle velocity using a noncontacting measurement system gives the best information which can be obtained. Due to the force applied to spindle through cutting tool, velocity of spindle changes. And any change in cutting tool condition affects cutting force and consequently spindle vibration. With the intent of continuously monitoring cutting tool condition in intermittent machining operations in a benign manner, a noncontacting velocity measurement system using a laser Doppler velocimeter was assembled to measure spindle torsional vibration. Spindle vibration was measured and analysis of it in the frequency domain yielded a measure which corresponded to amount of cutting tool wear in milling.

• 대한기계학회논문집
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• 제18권8호
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• pp.1939-1951
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• 1994

This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.660-663
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• 1997

In recent manufacturing process, the increase of productivity is required by reducing machining time with the increase of cutting force. However, the excessive increase of cutting force can cause tool breakage, and have a bad effect on both the manufacturing machine and the workpiece. Thus, it is necessary to estimate and control cutting force in real time during the process. In this study, use of disturbance observer is proposed for the indirect cutting force estimation. The estimated cutting force is used for the real time control of feedrate, making the actual cutting force follow the reference force command. Since the suggested method does not need an expensive sensor like a dynamometer, the method is expected to be used practically. By reducing the machining time resulting from making the actual cutting force follow the reference force, the increase of productivity are also expected, and the quality of cutting surface has been improved due to the adjusted feedrate. Besides, an actual constant cutting force guarantees the prevention of tool breakage. To show the effectiveness of the suggested cutting force control method, an experimental setup has been made without the force sensor, applied to several workpieces. Experiments show that the suggested method is superior to the conventional method operated by constant feedrate.

• 한국생산제조학회지
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• 제9권5호
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• pp.144-149
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• 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.

• 한국정밀공학회지
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• 제14권12호
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• pp.48-55
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• 1997

A useful method to detect tool breakage suing neural network of cutting force signal is porposed and implemented in a basic cutting process. Cutting signal is gathered by tool dynamometer and normalized as a preprocessing. The cutting force signal level is continually monitored and compared with the predefined level. The neural network has been trained normalized sample data of the normal operation and cata-strophic tool failure using backpropagation learning process. The develop[ed system is verified to be very effective in real-time usage with minor modification in conventional cutting processes.