• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.95.5-95
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• 2002

Tool condition monitoring is recognized important in CNC machining processes since the excessive wear or breakage of tool has to be noticed immediately in an automated manufacturing system to keep the quality and productivity. In this research, as an economic way of detecting the status of tool change, the wavelet transform has been applied to the measurement of spindle motor current. The energy of a specific level shows the difference between a normal tool and worn one. By setting a limit on the change of energy, it is possible to notify the time to inspect the tool.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.82-87
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• 1997

In order to make unmanned machining systems with satisfactory performances, it is necessary to incorporate appropriate condition monitoring systems in the machining workstation to provide the required intelligence of the expert. This paper deals with condition monitoring for tool wear and fracture during turning operation. Developing economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique are proposed. The validity of the proposed system is confirmed through the large number of cutting tests.

• 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 Precision Engineering Conference
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• 2001.04a
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• pp.84-87
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• 2001

The high speed machining which cam improve the production and quality has been remarkable in die/mold industry with the growth of parts and materials industries. The speed of machine tool increases, but on the other hand, the response of sensors I not being improved. Therefore, the condition monitoring techniques for the machine too, tool and workpiece in high speed machining are incomplete. In this study, characteristics of the tool edge roughness were verified from the high frequency components of cutting force signals acquired by the high speed dynamometer. Also, the experimental evaluation technique for the machinability and condition monitoring in high speed machining was established by analyzing the cutting force, acceleration and surface roughness.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.3
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• pp.13-19
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• 2003

The high speed machining technology has been improved remarkably in die/mold industry with the growth of parts and materials industries. Though the spindle speed of machine tool increases, the condition monitoring techniques of the machine tool, tool and workpiece in high speed machining ate incomplete. In tins study, efficient sensing technology in high speed machining is suggested by observing the characteristics of cutting force, gap sensor and accelerometer signal also, machinability of high-speed machining is experimentally evaluated sensing technique to monitor the machine tool and machining conditions was performed.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.54-60
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• 2000

Research about tool condition monitoring has been done until now for product automation and unmaned system. But it is hard to apply it to the industrial field due to its cost and reliability. This paper presents the new method of tool wear measurement using Marpos gauge. This is a kind of touch sensor, so its cost is lower than vision system. And it is not affected by dust and illumination, which are important in vision system. This proposed method use tool clearance angle to measure flank wear. Experimental results compared with vision system shows that this method is available for tool condition monitoring system.

• 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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.27-37
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• 1993

In unmanned machining, One of the most essential issue is the tool management system which includes controlling. identification, presetting and monitoring of cutting tools. Especially the monitoring of tool wear and fracture may be the heart of the system. In this study a computer vision based tool monitoring system is developed. Also an algorithm which can determine the tool condition using this system is presented. In order to enhance practical adaptability the vision system through which two modes of images are taken is located over the rake face of a tool insert. And they are analysed quantitatively and qualitatively with image processing technique. In fact the morphologies of tool fracture or wear are occurred so variously that it is difficult to predict them. For the purpose of this problem the pattern recognition is introduced to classify the modes of the tool such as fracture, crater, chipping and flank wear. The experimental results performed in the CNC turning machine have proved the effectiveness of the proposed system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.162-167
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• 2005

Recently, the advanced industries using micro parts are rapidly growing. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro to micro parts. 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 study deals with condition monitoring using acoustic emission(AE) signal in the micro-grooving. First, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by using the fuzzy C-means algorithm, which is one of the methods to recognize data patterns. These result is effective monitoring method of micro endmill state by the AE sensing techniques which can be expected to be applicable to micro machining processes in the future.