• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.103-111
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• 2002

Out of all metal-cutting process, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by computer vision, On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.211-214
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• 2002

The detection of tool wear is very important in an automated manufacturing system. This paper presents a tool condition monitoring system based on the wavelet transform analysis of the AC servo motor current in a milling process. The current measurement is relatively simple and does not affect machining operations. The discrete wavelet transform was used to decompose the current of a spindle AC servo motor in the time and frequency domain. The feature vectors were extracted from the decomposed signals and compared to clarity normal and wear conditions. The results show the feasibility of the wavelet transform analysis for the tool condition monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.4
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• pp.339-344
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• 2015

Machine tools are one of the energy-intensive equipment used in the manufacturing industry. The importance of energy has increased and the machine tools are required to be energy-efficient. The servo systems of the machine tool consume electrical power to rotate a spindle and to feed a tool during machining. Servo system consumes a lot of energy when the machine tool is operated. The energy consumption pattern of each axis needs to be investigated in order to optimize the machining process with regard to energy cost. In this paper, an energy monitoring system is developed considering various measuring points of servo system in order to grasp the energy consumption pattern of each axis.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.334-339
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• 2004

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for state monitoring is also presented in the paper.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.454-459
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• 2000

The high hardened materials that are remarkable in aspects of durability have been used for die and mold industry. As the high hardened materials are hard to machine, the high-speed machining is essential to manufacture these materials. Currently, in the general turning and milling, experiments to the tool wear monitoring have studied, but those have not applied in high-speed machining. In this study, the cutting mechanism was analysed by the cutting force according to cutting conditions, and the parameters to monitor the tool wear were selected from the tendency of the cutting force and acceleration according to cutting length in the high-speed machining of the high hardened materials(STD11).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.101-102
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• 2010

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1682-1687
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• 2003

Die polishing technology is very critical to determine quality and performance of the final products. Generally, the rotation-type tool is used most widely in the polishing process. However it is difficult to make the mirror surface, because the method using the rotation-type tool causes a lot of tiny scratch on the polished surface. This paper proposes a new method using the oscillation-type tool that reduces the scratch and improves the surface roughness. As result. the mirror surface was able to obtain by using the oscillation-type tool. AE is known to be closely related to material removal rate(MRR). As the surface is rougher, MRR gets larger and AE increase. The surface roughness can be indirectly estimated using the AE signal measured during automatic die polishing process. In this study. an AE sensor based monitoring system was developed to investigate the relation the level of AE RMS with the surface roughness during polishing process.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.10a
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• pp.164-169
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• 1998

This paper deals with condition monitoring for tool wear during tuning operation. To develop economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique are proposed. To identify noise sources of tool wear and reject background noise, noise rejection methodology is proposed. features to represent condition of tool wear are obtained through analysis using adaptive filter and FFT in time and frequency domain. By using fuzzy pattern recognition, we extract features, which are sensitive to condition of tool wear, from several features and make a decision on tool wear. The validity of the proposed system is condirmed through the large number of cutting tests in two cutting conditions.