• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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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.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.194-199
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• 1996

Grinding burn formed on the ground surface is related to the maximum temperature of workpiece surface and wheel temperature in the grinding process. The thermal characteristics of workpiece and grinding conditions on the surface temperature of the oxidation growing layer after get out of contact with the grinding wheel. The assumption used in grinding power signatures leads to the local temperature distribution between grinding wheel and workpiece, i.e., a single curve determines temperatures anywhere within the grinding wheel at anytime. This information is useful in the study of the grinding bum penetration into the wheel and thus provides an presentation of grinding trouble monitoring for the burning. On the basis of grinding power signatures in the wheel, thermally optimum conditions are defined and controlled. To cope with grinding burn, the use of grinding power signatures is an effective monitoring systems when occurring the grinding process. In this paper, the identified parameters suggested in this study which are derived from the grinding power signatures are presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.157-164
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• 2004

Trouble prevention of facilities in operation process plays an important role for improving facilities productivity as production systemization is installed with development of facilities automatization. Condition monitoring predicts machine's internal changes by periodically recording vibration occurred in the machine. This article considers a method of establishing statistical criteria for facilities troubles by utilizing machine condition evaluation and operation limit standards of ISO 10816-3.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.78-82
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• 2001

Electronic Weighting Machine is used an electronic scale which has many trouble because of broken load cells. In this paper, we propose an intelligent Diagnosis System will for an electronic weighting machine using fuzzy logic. It's purpose be detect of the load cell's trouble. The electronic circuit of system, which call 'junction box', will be connected resistances in a series at circuit of Wheatstone Bridge for monitoring the condition of load cells.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.18-26
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• 1997

Grinding burn formed on the ground surface is related to the maximum temperature of workpiece surface and wheel tempertaure in the grinding process. The thermal characteristics of workpiece and grinding conditions on the surface tempertaure of the oxidation growing layer after get out of contact with the grinding wheel. The assumption used in grinding power signatures leads to the local temperature distribution between grinding wheel and workpiece, i.e., a single curve determines temperatures anywhere within the grinding wheel at anytime. This information is useful in the study of the grinding burn penetration into the wheel and thus provides an presentation of grinding trouble monitoring for the burning. On the basis of grinding power signatures in the wheel, thermally optimum grinding conditions are defined and controlled. To cope with grinding burn, the use of grinding power signatures is an effective monitoring systems when occurring the grinding process. In this paper, the identified parameters suggested in this study which are derived from the grinding power signatures are presented, and prediction model by grinding power utilized a linear regression algorithm is applied.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.807-810
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• 2001

Election Weighting Machine is used an electronic scale which has many trouble because of broken load cells. In this paper, we propose an Intelligent Diagnosis System will for an electronic weighting machine using fuzzy logic. It s purpose be detect of the load cell s trouble. The electronic circuit of system, which call junction box , will be connected resistance in a series at circuit of Wheatstone Bridge for monitoring the condition of load cells.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1089-1094
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• 2004

Diseases in the gastro-intestinal track are on an increasing trend. In order to diagnose a patient, the various signals of the digestive organ, such as temperature, pH, and pressure, can offer the helpful information. Among the above mentioned signals, we choose the pressure variation as a monitoring signal. The variation of a pressure signal of the gastro-intestinal track can offer the information of a digestive trouble or some clues of the diseases. In this paper, a pressure monitoring system for the digestive organs of a living pig is presented. This system concept is to transmit the measured biomedical signals from a transmitter in a living pig to wireless receiver that is positioned out of body. The integrated solution includes the following parts: (1) the swallow type pressure capsule, (2) the receiving set consisting of a receiver, decoder box, and PC. The merit of the proposed system if that the monitoring system can supply the precise and repeatable pressure in the gastro-intestinal track. In addition, the design of low power consumption enables it to keep sending reliable signals while the pressure capsule is working in the digestive organ. The subject of the study for the pressure monitoring system is in-vivo experiments for a living pig. We achieved the pressure tracings in digestive organs and verified the validity of system after several in-vivo tests using pressure monitoring system. As a result, we found each organ has its own characterized pressure fluctuation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.539-544
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• 1998

Generally, welding gap is a serious factor of a falling-off in weld quality among various kind of weld defect. Welding gap is created between two work piece in GMAW(Gas Metal Arc Welding) of horizontal fillet weld because surface of workpiece is not flat by cutting process. In these days, there were many attempts to detect welding gap. though we prevalently use the vision sensor or arc sensor in welding process, it is difficult to detect welding gap for improvement of welding quality. But we have a trouble to find relationship between welding gap and many welding parameters due to non-linearity of welding process. As mentioned about the various difficult problem, we can detect welding gap precisely using neural networks which are able to model non-linear function. Also, this paper was proposed real-time monitoring of weld bead shape to find effect of welding gap and to estimate weld quality. Monitoring of weld bead shape examined the correlation of welding parameters with bead eometry using learning ability of neural networks. Finally, the developed system, welding gap detecting system and bead shape monitoring system, is expected to the successful capability of automation of welding process by result of simulation.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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• pp.15-22
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• 2000

Long ranged groundwater-level data often have the missing intervals because of the trouble of monitoring systems at the national groundwater monitoring wells. Geostatistical methods are very useful for the supplement of the missing data. Ordinary kriging was applied for the interpolation of the missing groundwater-level data with a smooth sinusoidal variation. Conditional simulation was used for the reproduction of the missing data with high fluctuations. Two geostatistical methods produced the very accurate estimates at the missing intervals and reproduced their original variations. This fact is proved by the cross validation test and graphical method, respectively.