• 한국정보통신학회논문지
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• 제20권11호
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• pp.2100-2106
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• 2016

자동화시스템은 PLC를 사용하여 생산제품의 제조 공정상태를 감시하고 센서에서 전달되는 센서정보를 처리한다. 본 연구에서는 자동화시스템에 사용되고 있는 제어용 계전기의 상태를 감시하는 원격감시시스템을 제안한다. 제안한 시스템은 제어 릴레이모듈, 원칩프로세서 모듈, 컴퓨터 모니터링시스템과 제어용 계전기의 내역을 입력하고 관리하는 데이터베이스시스템으로 구성한다. 컴퓨터 모니터링시스템은 제어용 계전기의 작동상태와 수명을 모터링할 수 있도록 구성하였고, 데이터베이스는 제어용 계전기의 투입 일자를 입력하고 수정이 가능하게 구성하였으며, 제어용 계전기의 작동상태의 정보를 자동으로 프린트할 수 있도록 구성하였다. 원격감시시스템에서 제어용 계전기의 고장상태를 실시간으로 파악하고, 고장부품을 신속하게 교체하여 정상가동에 소요되는 시간을 최소화할 수 있도록 하였다.

• Smart Structures and Systems
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• 제3권1호
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• pp.1-22
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• 2007

The energy efficiency of a self-powered wireless sensing system for pressure monitoring in injection molding is analyzed using Bond graph models. The sensing system, located within the mold cavity, consists of an energy converter, an energy modulator, and a ultrasonic signal transmitter. Pressure variation in the mold cavity is extracted by the energy converter and transmitted through the mold steel to a signal receiver located outside of the mold, in the form of ultrasound pulse trains. Through Bond graph models, the energy efficiency of the sensing system is characterized as a function of the configuration of a piezoceramic stack within the energy converter, the pulsing cycle of the energy modulator, and the thicknesses of the various layers that make up the ultrasonic signal transmitter. The obtained energy models are subsequently utilized to identify the minimum level of signal intensity required to ensure successful detection of the ultrasound pulse trains by the signal receiver. The Bond graph models established have shown to be useful in optimizing the design of the various constituent components within the sensing system to achieve high energy conversion efficiency under a compact size, which are critical to successful embedment within the mold structure.

• 한국정밀공학회지
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• 제12권3호
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• pp.5-14
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• 1995

In recent years, the automization of cutting machine tools has been developed very fast. Hance, the in-process detection of cutting condition is very important for automatic manufacturing system in factory. Acoustic Emission(AE) has been widely used in monitoring the cutting conditions, because of high sensitivity of AE signal and low cost of AE equipment. This experimental study deals with the relations between AE signal, cutting force charcteristics and tool wear in the machining of SUS304. Face milling operation is used for the analysis between tool wear and AE signal.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.300-306
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• 2005

This paper presents the methodology for measuring eccentricity of the machined cylindrical part using CCS(cylindrical capacitance spindle sensor) signal in the CNC turning process. We use capacitance type sensor to take full advantage of averaging effect by using large capacitance area to encompass the whole side of a sensor. The intentionally proposed initial eccentricity is applied to the experimental testpieces, and their resultant relationships between CCS orbits and eccentricities are investigated. As a result, the possibility as a automatic detection apparatus for the CNC lathe is considered based on the linearities of CCS signal and magnitude of eccentricity of machined cylindrical surfaces.

• Design & Manufacturing
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• 제16권4호
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• pp.46-51
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• 2022

The piercing process of creating holes in sheet metals for mechanical fastening generates high shear force. Real-time monitoring technology could predict tool damage and product defects due to this severe condition, but there are few applications for piercing high-strength aluminum. In this study, we analyzed the load signal to predict the punch's wear level during the process with a piezoelectric sensor installed piercing tool. Experiments were conducted on Al6061 T6 with a thickness of 3.0 mm using piercing punches whose edge angle was controlled by reflecting the wear level. The piercing load increases proportionally with the level of tool wear. For example, the maximum piercing load of the wear-shaped punch with the tip angle controlled at 6 degrees increased by 14% compared to the normal-shaped punch under the typical clearance of 6.7% of the aluminum piercing tool. In addition, the tool wear level increased compression during the down-stroke, which is caused by lateral force due to the decrease in the diameter of pierced holes. Our study showed the predictability of the wear level of punches through the recognition of changes in characteristic elements of the load signal during the piercing process.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Structural Engineering and Mechanics
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• 제75권3호
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• pp.369-376
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• 2020

The life of conventional steel plastic injection molds is long but manufacturing cost and time are prohibitive for using these molds for producing prototypes of products in limited numbers. Commonly used 3D printers and rapid prototyping methods are capable of directly converting the digital models of three-dimensional solid objects into solid physical parts. Depending on the 3D printer, the final product can be made from different material, such as polymer or metal. Rapid prototyping of parts with the polymeric material is typically cheaper, faster and convenient. However, the life of a polymer mold can be less than a hundred parts. Failure of a polymeric mold during the injection molding process can result in serious safety issues considering very large forces and temperatures are involved. In this study, the feasibility of the inspection of 3D printed molds with the surface response to excitation (SuRE) method was investigated. The SuRE method was originally developed for structural health monitoring and load monitoring in thin-walled plate-like structures. In this study, first, the SuRE method was used to evaluate if the variation of the strain could be monitored when loads were applied to the center of the 3D printed molds. After the successful results were obtained, the SuRE method was used to monitor the artifact (artificial damage) created at the 3D printed mold. The results showed that the SuRE method is a cost effective and robust approach for monitoring the condition of the 3D printed molds.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1995년도 특별강연 및 추계학술발표 개요집
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• pp.3-20
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• 1995

Development of Real-Time Quality Evaluation of Friction Welding by Acousitc Emission : Report 1 ABSTRACT : According as the friction welding has been increasingly applied in manufacturing various machine components because of its significant economic and technical advantages, one of the important concerns is the reliable quality monitoring method for a good weld quality with both joint strength and toughness in the process of its production. However no reliable nondestructive test method is available at present to determine the weld quality particularly in process of production. So this paper presents an experimental examination and quantitative analysis for the real-time evaluation of friction weld quality by acoustic emission, as a new approach which attempts finally to develop an on-line quality monitoring system design for friction welds using AE techniques. As one of the important results, it was confirmed, through this study, that AE techniques can be reliably applied to evaluating the friction weld qualify with 100% joint strength, as the cumulative AE counts occurring during welding period were quantitatively correlated with reliability at 95% confidence level to the joint strength of welds. Real-Time Evaluation of Automatic Production Quality Control for Friction Welding Machine : Report 2 Abstract : Both in-process quality control and high reliability of the weld is one of the major concerns in applying friction welding to the economical and qualified mass-production. No reliable nondestructive monitoring method is available at present to determine the real-time evaluation of automatic production quality control for friction welding machine. This paper, so that, presents the experimental examinations and statistical quantitative analysis of the correlation between the initial cumulative counts of acoustic emission(AE) occurring during plastic deformation period of the welding and the tensile strength of the welded joints as well as the various welding variables, as a new approach which attempts finally to develop an on-line (or real-time) quality monitoring system and a program for the process of real-time friction welding quality evaluation by initial AE cumulative counts. As one of the important results, it was well confirmed that the initial AE cumulative counts were quantitatively and cubically correlated with reliability of 95% confidence level to the joint strength of the welds, bar-to-bar (SCM4 to SUM31, SCM4 to SUM24L) and that an AE technique using initial AE counts can be reliably applied to real-time strength evaluation of the welded joints, and that such a program of the system was well developed resulting in practical possibility of real-time quality control more than 100% joint efficiency showing good weld with no micro-structural defects.

• 한국기계가공학회지
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• 제20권11호
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• pp.36-42
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• 2021

This study was conducted to analyze the effect of the gear specification and gear quality corresponding to the macro geometry on the gear transmission error. The two pairs of gears with large and small transmission errors were selected for calculation, and two pairs of gears were manufactured with different gear quality. The test gears were manufactured by two different gear specifications with ISO 5 and 8 gear quality, respectively. The transmission error measurement system consists of an input motor, reducer, encoders, gearbox, torque meter, and powder brake. To confirm the repeatability of the test results, repeatability was confirmed by performing three repetitions under all conditions, and the average value was used to compare the transmission error results. The transmission errors of the gears were analyzed and compared with the test results. When the gear quality was high, the transmission error was generally low depending on the load, and the load at which the decreasing transmission error phenomenon was completed was also lower. Even when the design transmission error according to the gear specification was different, the difference of the minimum transmission error was not large. The transmission error at the load larger than the minimum transmission error load increased to a slope similar to the slope of the analysis result.

• 한국인터넷방송통신학회논문지
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• 제20권2호
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• pp.261-269
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• 2020

본 논문은 스마트 팩토리의 효율적인 오류 탐지를 위한 설비 시뮬레이션 시스템을 소개한다. 공장 설비들의 관계를 분석하여 설비오류 탐지시 오류를 자율적으로 추론하고 해결할 수 있는 설비 시뮬레이션 시스템은 높은 생산성을 가진 스마트 팩토리를 구성하는 데 중요한 기술 중 하나이다. 이러한 자율 제어 시스템 구현을 위해서는 공장 설비의 데이터를 통해 설비의 상태를 파악하고 설비 간 관계를 분석할 수 있어야 한다. 따라서 본 논문은 정의된 설비 상태를 이용하여 공정 시나리오를 기반으로 오류 발생시 공정 오류의 원인이 되는 설비를 쉽게 탐지할 수 있는 프로그램을 설계하고 개발한다. 시뮬레이션을 통해 공정 Map과 설비 상태 기반의 오류 추론 과정이 일반적인 오류 추론 과정보다 효율적임을 보였다. 본 시뮬레이션 프로그램을 통해 설비 오류 발생에 따른 추론 및 해결 과정을 직관적으로 볼 수 있도록 한다.