• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1991년도 추계학술대회 논문집
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• pp.95-100
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• 1991

From a manufacturing standpoint it would be desirable to monitor the degradation of drawing die, that is essential for the maintenance of quality, the evaluation of product integrity and the reducing scrap. Acoustic emission is powerful method in monitoring fine wire drawing process, especially in detecting the die fracture at early stage. Experiments at so suggested that acoustic emission signals contained valuable information regarding the stage of a drawing process such as the surface appearance of products and the condition of lubrication. Using these informations makes AE monitoring techniques a possible tool in monitoring the drawing process operation. In order to approach this, this paper discusses the nature of acoustic emission signals produced in drawing process under various conditions. Experimental results are presented which illustrate the effects of wire and die material, lubricants, and drawing speed on the generation and the mean voltage level of acoustic emission signals. The results from these tests give controlling factors of acoustic emission generation.

• 대한안전경영과학회지
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• 제15권4호
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• pp.171-178
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• 2013

Workers are avoiding production/manufacturing sites due to the poor working environment and concern over safety. Small and medium-sized businesses introduce new equipment to secure safety in the production site or ensure effective process management by introducing the real-time monitoring technique for existing equipment. The importance of real-time monitoring of equipment and process in the production site can also be found in the ANSI/ISA-195 model. Note, however, that most production sites still use paper-based work slip as a process management technique. Data reliability may deteriorate because information on the present condition of the production site cannot be collected/analyzed properly due to manual data writing by the worker. This paper introduces the monitoring and process management technique based on a direct facility interface to secure safety in the field by improving the poor working environment and enhance there liability and real-time characteristics of the production data. Since the data is collected from equipment in real-time directly through the SIB-based interface and PLC-based interface, problems associated with workers' manual data input are expected to be solved; safety can also be improved by enhancing workers' attention to work by minimizing workers' injuries and disruption.

• 산업공학
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• 제20권3호
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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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.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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• pp.523-523
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• 2012

As the wafer geometric requirements continuously complicated and minutes in tens of nanometers, the expectation of real-time add-on sensors for in-situ plasma process monitoring is rapidly increasing. Various industry applications, utilizing plasma impedance monitor (PIM) and optical emission spectroscopy (OES), on etch end point detection, etch chemistry investigation, health monitoring, fault detection and classification, and advanced process control are good examples. However, process monitoring in semiconductor manufacturing industry requires non-invasiveness. The hypothesis behind the optical monitoring of plasma induced ion current is for the monitoring of plasma induced charging damage in non-invasive optical way. In plasma dielectric via etching, the bombardment of reactive ions on exposed conductor patterns may induce electrical current. Induced electrical charge can further flow down to device level, and accumulated charges in the consecutive plasma processes during back-end metallization can create plasma induced charging damage to shift the threshold voltage of device. As a preliminary research for the hypothesis, we performed two phases experiment to measure the plasma induced current in etch environmental condition. We fabricated electrical test circuits to convert induced current to flickering frequency of LED output, and the flickering frequency was measured by high speed optical plasma monitoring system (OPMS) in 10 kHz. Current-frequency calibration was done in offline by applying stepwise current increase while LED flickering was measured. Once the performance of the test circuits was evaluated, a metal pad for collecting ion bombardment during plasma etch condition was placed inside etch chamber, and the LED output frequency was measured in real-time. It was successful to acquire high speed optical emission data acquisition in 10 kHz. Offline measurement with the test circuitry was satisfactory, and we are continuously investigating the potential of real-time in-situ plasma induce current measurement via OPMS.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.212-216
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• 1996

Recently, with the development of NC and CNC machine tools and the high labor wage, the cutting process requires the high speed and automatic system which uses industrial robots and the flexible manufacturing system(FMS) that combines several machine tools. In this system, the whole system can be influenced by just one of the machin tools. So it needs to detect a problem and to solve it immediately In in-process state. The monitoring system through measuring the motor current with current sensor has been attracting the attention of lots of researchers view of its low cost and flexibility. By using the pattern discriminant with the detected three-phase-current signal, that is, $I_{RMS}$, a system which can monitor and analyze abnormal machining process condition of the workpiece during the machining will be able to be developed in this research.h.

• 한국정밀공학회지
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• 제28권6호
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• pp.709-715
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• 2011

Chattering in cutting operations are usually a cumbersome part of the manufacturing process in mechanical. Particular, machining performance such as that of the boring process is limited by cutting condition at the movable components. Among various sources of chatter vibration, detrimental point in cutting condition is found a mechanical condition on overhang. It limits cutting speed, depth, surface roughness and tool wear failure as result because the all properties are varying with the metal removal process. In this case, we have to observe the resonance frequencies of a boring bar for continuous cutting. In the established research, boring bar vibration of cutting system has been measured with the aid of accelerometer. However, the inherent parameters of internal turning operations are severely limit for the real time monitoring on accelerometers. At this point, this paper is proposed other method for real time monitoring during continuous cutting with optical fiber at the inside of boring bar. This method has been used a plastic fiber in the special jig on boring bar by based on experimental modal analysis. In this study, improvement of monitoring system on continuous internal cutting was attempted using optical fiber sensor of inside type because usually chattering is investigated experimentally measuring the variation in chip thickness. It is demonstrated that the optical fiber sensor is possibility to measure of chattering with real time in boring process.

• 대한기계학회논문집
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• 제17권9호
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• pp.2214-2222
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• 1993

With the use of the NC machine tool, the unmanned production system has been growing recently in the manufacturing field. This there are problems with monitoring adequate tool fracture during the cutting process efficiently. This study was planned and carried out to discover a way of monitoring tool condition in NO-LINE systems during the cutting process. The acquisition of data in cutting force and tool wear has been made in the section examined, to extract the RMS value of the cutting force as specific factors in the cutting process. The fluctuation of the RMS characteristics. From the results, it has been shown that the fluctuation of the RMS values for the cutting force has a close relation to flank wear.

• 대한기계학회논문집A
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• 제34권1호
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• pp.11-18
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• 2010

공구상태 감시기술은 지능형 생산시스템 구축을 위하여 중요한 요소 기술이다. 다양한 생산 공정 분야에 걸쳐 연구가 진행되었지만 기어 세이빙 공정에서 공구파손을 검출하는 연구가 발표된 바가 없다. 본 연구에서는 기어 세이빙 공정 중에 세이빙 공구의 상태를 검출하기 위하여 베타확률분포를 활용하는 통계적 기법을 제안하였다. 신뢰성 있는 공구상태 감시를 위하여 선행되어야 할 특징값 추출을 위하여 공정 중에 발생하는 진동 신호를 베타확률분포로 모델링하였다. 신호의 양봉 분포를 단봉 분포로 변환한 후 모멘트법을 사용하여 베타확률분포의 파라미터들을 추정함으로써 특징값들을 추출하였다. 특징값들의 유효성을 평가 결과, 베타분포 모델의 파라미터 중 모드가 우수한 세이빙 공구상태 감시 성능을 갖고 있음을 확인하였다.

• 한국공작기계학회논문집
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• 제17권1호
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• pp.98-105
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• 2008

In this paper, the system for monitoring process of aluminum laser welding was developed using the light signal emitted from the plasma which comes from interaction between material and laser. Photodiode for monitoring system was selected based on the spectrum analysis of light from plasma and keyhole. Behavior of plasma and keyhole was analyzed through the sensor signals. Value of sensor signal represented the light intensity and fluctuation of signal indicated the stability of plasma and keyhole. For the relation between welding condition and sensor signals, the input power and weld geometry greatly effected on the average of each sensor signals. Using the feature values of signals, estimation model for tensile strength of weld was formulated with neural network algorithm. Performance of this model was verified through coefficient of determination and average error rate.