• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.4
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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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.372-373
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• 2012

• The monthly packaging world
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• s.233
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• pp.136-143
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• 2012

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.6
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• pp.1014-1025
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• 1987

This paper proposes an approach of building an expert system for designing gear drives. In terms of gear drives the relationship between expert systems and data base systems is explained and a data base system for design, failure detection, and trouble-shooting of gear drives is built on a personal computer by the data manipulation language of dBASE III. This paper proposes a method how to build a data base system and what to consider to build a data base system for gear drives and two kinds of data base systems of design and failure detection of gear drives and trouble-shooting for noise and vibration of gear drives are built. The two data base systems are very effective for the retrieval of information for the gear design, and they will be considerably helpful for the gear design.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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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.

• Korean Journal of Cognitive Science
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• v.9 no.1
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• pp.31-39
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• 1998

This paper describes an electronic performance support system called HELPS(Handler Electronic Learning Performence Support) for semiconductor testing e equipment. The purpose of this system is to improve productivity of operators by providing just-in-time, on-the-job, mutimedia-based system information for operational support, training, and knowledge-based trouble shooting and repair. HELPS is composed of a operation module and a trouble shooting module. The operation module uses multimedia and hypermedia to provide the detailed and easily accessible information about equipment to users. Multimedia incorporate multiple. media forms including still and video images. animations 'texts' graphics. and audio. Hypermedia a are provided through a hierarchical information structure which offers not only specific information which is needed to perform a task to experienced operators. but detailed system guidance and information to novice operators. The trouble shooting module is composed of an integrated mutimedia-supported expert system which assists operators in trouble shooting and equipment repair. After diagnosis through the use of the expert system. multimedia advice is presented to the user in either still images with text or motion sequences with sound HELPS is evaluated in term of training time and trouble shooting and repair time. It improved productivity by saving more than 30% of the total time used without the system. This s system has the potential to improve productivity when it is used with ICAIOntellignet Computer Aided Instruction) and virtual reality.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.26 no.4
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• pp.387-397
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• 1990