• 한국정밀공학회지
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• 제12권1호
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• pp.48-57
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• 1995

연삭가공중에 발생하는 트러블의 인식과 처리는 공학적 원리에 입각한 방법과 현장 숙련기술자의 경험적 지식을 바탕으로한 방법이있다. 그러나, 연삭가공은 관계되는 가공변수가 많아, 이들 상호간의 관계를 정량적으로 규명할 수 없어 대부분이 숙련자의 지식에 의존하는 것이 현실정이다. 본 논문은 이와같은 점에 착안하여 원통연삭을 대상으로 현장숙련자가 갖고 있는 경험적이고도 정성적인 지식의 호과적인 활용을 위해 계층분석으로 도입하여 이들이 갖고 있는 노하우를 정량화하고, 아울러 공학적원리를 가미한 연삭가공을 트러블 진단. 처리 시스템을 구축하였다. 또한 시스템 구성에 신뢰성을 높이기 위해 폴트 진단 모델을 도입하였다.

• 한국정밀공학회지
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• 제13권9호
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• pp.149-155
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• 1996

Recent researches in the trouble monitoring system of grinding process have emphasized the use of deep knowledge. Such works include the monitoring and diagnostic systems for cylindrical grinding using sensors on chatter vibration and grinding burn during the process. But, since grinding operations are especially related with a lalrge amount of ambique parameters, it is effectively difficult to detect the grinding troubles occuring during the grinding process. In this paper, monitoring system for grinding utilizes the neural networks based on grinding power signatures. The monitoring system of grinding operations, which makes use of PDP neural networks, is presented. Then, the implementation results by computer simulations and experimental data with respect to chatter vibration and grinding burn are compared.

• 한국정밀공학회지
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• 제15권2호
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• pp.162-169
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• 1998

Lacking for the skilled grinding operator possessed of the experiential knowledges in machine shop, there is the just requirement which includes the establishment of the optimal grinding conditions. Accordingly, we attemt to develope the selection system of optimal grinding conditions such as workpiece velocity, depth of cut and wheel velocity and to add the trouble shooting system by means of the neural network. Those systems are robust to the each machine error and environmental unstable state. In addition. we produce the loaming process that is progressed with additional data modified by skilled operators, and excluding is advanced to similarity of input data.

• 동력기계공학회지
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• 제4권2호
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• pp.25-30
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• 2000

The grinding process is very complex and relates many parameters to control the process. As this reason, a theoretical analysis and a quantitative estimation of the grinding process has not been well established. In this study, the in-process monitoring system was suggested by applying the neural network for monitoring and shooting the malfunction of cylindrical plunge grinding process. This system used the power signals from the electric power meter. This neural network was composed of processing elements [4-(5-5)-3] with 4 identified power parameters. Because sensitivity is blunted some minute vibration components, the simulation result of this system has appeared about 10% erroneous recognition in the uncertain pattern and the average success rate of the trouble recognition was about 90%. Consequently, the developed system, which applied to the power signals, can be recognize enough to monitor the grinding process as in-process.

• 한국정밀공학회지
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• 제13권11호
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• pp.57-63
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• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

• 한국생산제조학회지
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• 제9권4호
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• pp.56-61
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• 2000

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skilful engineers. Grinding operations include a large number of functional parameters, since there are several ways of coping with grinding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workship, the other is the quantitative method which utilizes the experimental data obtained by a sensor. But, they are all difficult to accomplish from the grinding trouble-shooting system. The reason is that grinding troubles are now easily controlled in the quantitative method, and therefore, trouble-shooting has mainly relied on the knowledge of skilful engineers. Thus, there is an important issue of how a grinding trouble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper, basic strategy to develop the grinding database of rule-based model, which is strongly depended upon experience and intuition , is described.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.492-497
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• 1999

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skillful engineers. Grinding operations include a large number of functional parameters, since there are several ways of coping with grinding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workshop, the other is the quantitative method which utilizes the experimental data obtained by sensor. But, they are all difficult to accomplish from the grinding trouble-shooting system. The reason is that grinding troubles are not easily controlled in the quantitative method, and therefore, trouble-shooting has mainly relied on the knowledge of skilful engineers. Thus, there is an important issue of how a grinding trouble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper, basic strategy to develop the grinding database of rule-based rule, which is strongly depended upon experience and intuition, is described.

• 한국정밀공학회지
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• 제17권8호
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• pp.106-112
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• 2000

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skiful engineers. grinding operations include a large number of functional parameters since there are several ways of coping with ginding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workshop the other is the quantitative method which utilizes the experimental data obtained by sensor. But they are all difficult to accomplish from the grinding trouble-shooting system The reason is that grinding troubles are not accomplish from the grinding trouble-shooting system,. The rason is that grinding troubles are not easily controlled in the quantitative method and therefore trouble-shooting has mainly relied on the knoledge of skiful engineers. Thus there is an important issue of how a grinding touble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper basic strategy to develop the grinding database by taking rule-based model which is strongly depended upon experience and intuition is described.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.113-117
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• 1995

Grinding operations is accomplished by rotating a gfinding wheel with lots of random abrasive at high speed, and its object is generally obtained the fanal workpiece surface of high quality as well as the maximization of workpiece removal rate. But, especiallysince grinding operations is related with a large amount of functional parameter, it is actually difficult to therapy that the grinding trouble occurs during the grinding process. Therefore, we trytodesign grinding trouble-shooting system utilizing the back-propagation model of neural network. The conceptual method is produced byidentifying the four parameters derived from the grinding power, and we are design te to the grinding trouble-shooting system on the basis of their data. In this paper, cognition and therapy method tothe grinding trouble which utilizes neural network based four identified models are suggested, and implementation results of computer simulation with respect to the grinding burn and chatter vibration is presented.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.131-137
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• 1994

Detection of grinding trouble occuring during the grinding process is classified into two types, i.e, based on the quantitative and qualitative knowledge. But, since the grinding operation is especially related with a large amount of functional parameters, it is actually defficult to cope with the grinding troubles occuring during process. Therefore, grinding trouble-shooting has difficulty in satisfying the requirement from the user. To cope with the grinding troubles occuring during the process, the application of neural network is on effective way. In this study, we identify the four parameters derived from the AE(Acoustic Emission) signals and present the grinding trouble-shooting system utilizing a back-propagation model of the neural network.