• 한국기계가공학회지
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• 제11권2호
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• pp.7-12
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• 2012

In order to cope with the requirements of smaller patterns, larger surfaces and lower costs in the fields of displays, optics and energy, greater attentions are now being paid to the development of micro-pattern machining technology. Compared with flat moulds, large drums with micro patterns (roll moulds) have the advantages of short delivery, ease of manufacturing larger surfaces, and continuous moulding. This paper introduced the machining process technology of the roll moulds for display industry. The environmental effects were discussed and the importance of temperature maintenance was experimentally emphasized. The real time monitoring system for micro machining was introduced. A commercial solution was used to simulate the micro grooving and a deformation model of micro machined pattern was finally introduced.

• 한국생산제조학회지
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• 제4권2호
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• pp.38-45
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• 1995

The diagnosis and monitoring system of abnormal cutting condition is necessary to realize precision machining proces and factory automation, which are final goal of metal cutting in order to develop this system, theimage processing technique has been investigated in machining process. In theis paper, the measurement system of tool wear using computer vision is designed to detect the wear pattern by non-contact and direct method and get the realiable wear information about cutting tool. We measured the area of the side and front part of the diamond core dril which is used in 40kHz ultrasonic vibration machine.

• 한국기계가공학회지
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• 제19권4호
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• pp.16-23
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• 2020

An electric vehicle operates at high currents and requires real-time monitoring of the entire system for ensuring efficiency and safety of the vehicle. Current sensors are applied to drive the motors, inverters, and battery control systems, and are the key components to ensure constant monitoring of the magnitude and waveforms of the operating current. In this study, a heat treatment process condition to influence the performance of Permalloy current sensors was developed; the correlation between the output capacity, low-temperature characteristics, and high-temperature characteristics of the current sensor was studied; and the process was optimized to meet the required output accuracy and temperature characteristics.

• Journal of Welding and Joining
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• 제28권4호
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• pp.81-86
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• 2010

In resistance spot welding, regardless of the optimal condition, bad weld quality was still produced due to complicated manufacturing processes such as electrode wear, misalignment between the electrode and workpiece, poor part fit-up, and etc.. Therefore, the goal of this study was to measure the process signal which contains weld quality information, and to develop the process fault monitoring system. Welding force signal obtained through variety experimental conditions was analyzed and divided into three categories: good, shunt, and poor fit-up group. And then a monitoring algorithm made up of an artificial neural network that could estimate the process fault of each different category based on pattern was developed.

• 한국정밀공학회지
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• 제34권1호
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• pp.19-22
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• 2017

This paper describes the efficient equipment maintenance that can offer the exact time for repair and change of component in root industry. A conventional method offered the fixed time for repair and change of component because the method is based on early guarantee specification of the component. However the operating condition of manufacturing field is often under worse condition than early guarantee condition for high productivity. So, most components can't use until early guarantee time due to the operation of various different condition. Therefore we suggest the useful method for efficient equipment-maintenance by manufacturing-field analysis and feedback database. For this, the classification of root industry and related equipment is performed and then the detail classification of the process and component for equipment maintenance. And the monitoring module is also designed to gather data for feedback process and the environment is basically implemented for aging and reliability test.

• 한국생산제조학회지
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• 제18권3호
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• pp.261-269
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• 2009

Micro end-milling process is applied to fabricate precision mechanical parts cost-effectively. It is a complex and time-consuming job to select optimal process conditions with high productivity and quality. To improve the productivity and quality of precision mechanical parts, micro end-mill wear and cutting force characteristics should be studied carefully. In this paper, high speed machining experiments are studied to construct the optimum process design as well as the mathematical modeling of tool wear and cutting force related to cutting parameters in micro ball end-milling processes. Cutting force and wear characteristics under various cutting conditions are investigated through the condition monitoring system and the design of experiment. In order to construct the cutting database, mathematical models for the flank wear and cutting force gradient are derived from the response surface method. Optimal milling conditions are extracted from the developed experimental models.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.148-152
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• 2000

Detection of tool wear is very important in automated manufacturing. This paper presents tool condition monitoring system based on the wavelet analysis of the AC servo motro current in drilling and milling process. The current measurement system is relatively simple and its mounting will not affect machining operations. The discrete wavelet transform was used to decompose the current signal of a spindle AC servo motor in time - frequency domain. The feature vectors were extracted from the decomposed signals and compared for normal and wear condition. The results show the possibility for the effective application of wavelet analysis to tool condition monitoring.

• 한국기계가공학회지
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• 제9권5호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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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.

• 한국기계가공학회지
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• 제16권6호
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• pp.81-86
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• 2017

The high-pressure resin transfer molding (HP-RTM) process has a very effective for the mass production of carbon fiber reinforced plastic (CFRP) for light weight in the automotive industry. In developing robust equipment, new process and fast cure matrix systems reduces significantly the cycle time less than 5 minutes in recent years. This paper describes the cavity pressure, temperature and molding characteristics of the HP-RTM process. The HP-RTM mold was equipped with two cavity pressure sensors and three temperature sensors. The cavity pressure characteristics of the HP-RTM injection, pressurization, and curing processes were studied. This experiment was conducted with selected process parameters such as mold cap size, maximum press force, and injection volume. Consequently, this monitoring method provides correlations between the selected process parameters and final forming characteristics in this work.