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

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

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.7
• /
• pp.981-988
• /
• 2022

The intelligent production plant called smart factories that apply information and communication technology (ICT) are collecting data in real time through various sensors. Recently, researches that effectively applying to these collected data have gained a lot of attention. This paper proposes a method for the tool condition monitoring based on the sound signal generated in machining process. First, it not only detects a fault tool, but also presents various tool states according to idle and active operation. The second, it's to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. Finally, the contrast-enhanced PSD image obtained is diagnosed by using CNN. The results of the experiments demonstrate the high discrimination potential afforded by the proposed sound PSD image + CNN and show high diagnostic results according to the tool status.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.11
• /
• pp.47-54
• /
• 2003

Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing process. STD11 has been known as difficult-to-cut materials. The micro-grooving machine was developed for this study and the experiments were performed using CBN blade for machining STD11. Evaluating the machining conditions, frequency spectrum analysis of acoustic emission (AE) signals according to each conditions were applied. Fuzzy clustering method for associating the preprocessor outputs with the appropriate decisions was followed by frequency spectrum analysis. FFT is used to decompose AE signal into different frequency bands in time domain, the root mean square (RMS) values extracted from the decomposed signal of each frequency band were used as features.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 2002.10b
• /
• pp.315-316
• /
• 2002

Electrochemical discharge machining is a very recent technique for non-conducting materials such as ceramics and glasses. ECDM is conducted in the NaOH solution and the cathode electrode is separated from the solution by $H_2$ gas bubble. Then the discharge is appeared and the non-conductive material is removed by spark and some chemical reactions. In the ECDM technology, the $H_2$ bubble control is the most important factor to stabilize the discharging condition. In this paper, we proposed the discharge peak monitoring/ discharging duty feedback algorithms for the discharge stabilization and the feasibility of this algorithm is verified by various pattern machining in the constant preload conditions for the cathode electrode.

• KSTLE International Journal
• /
• v.3 no.2
• /
• pp.95-100
• /
• 2002

Electrochemical discharge machining is a very recent technique for non-conducting materials such as ceramics and glasses. ECDM is conducted in the NaOH solution and the cathode electrode is separated from the solution by H$_2$ gas bubble. Then the discharge is appeared and the non-conductive material is removed by spark and some chemical reactions. In the ECDM technology, the H$_2$ bubble control is the most important factor to stabilize the discharging condition. In this paper we proposed the discharge peak monitoring/discharging duty feedback algorithms fur the discharge stabilization and the feasibility of this algorithm is verified by various pattern machining in the constant preload conditions for the cathode electrode.

• Journal of the Korean Society for Precision Engineering
• /
• v.18 no.10
• /
• pp.208-213
• /
• 2001

High speed milling(HSM) is one of the emerging cutting process having tremendous potential not only in increased metal removal rates but also in improved surface finish, burr free edge, dimensional accuracy and a virtually stress free component after machining. The High efficiency and accuracy in machining of die/mold materials can be obtained in high speed machining, so it is necessary to analytic the mechanism of high speed cutting process : cutting force, acoustic emission signal.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2009.06a
• /
• pp.1059-1060
• /
• 2009

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.7
• /
• pp.104-118
• /
• 1998

In this paper, the motion of ballscrew and shape of workpiece are the main objective variables varying with load conditions. To verify feed mechanism in NC lathe, the monitoring system is designed and cutting condition variables are spindle speed depth of cut and feed. During machining, rotation number of ballscrew motion of ballscrew in direction to gravity center and cutting force are measured. After machining, the roughness of workpiece is measured.

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