• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.387-390
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• 1997

Grinding process takes a long time that grinding machine is setted properly. It is difficult for user to judge correctly the abnormal states generated in grinding process. Air grinding has to be reduced for the improvement of productivity. In addition, it is important to monitor the dressing and the grinding process so that the grinding working maintains optimal grinding conditions. In this study, the monitoring system using the acoustic emission is developed to monitor these processes continuously. This system was able to reduce the preparation as well as the machine setting time in grinding operation.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.1
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• pp.81-86
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• 2001

This paper deals with the monitoring of grinding wheel wear in surface grinding process. A monitoring system, which makes use of a laser scanning micrometer, is developed to measure the circumferential shape as well as the axial profile of grinding wheel. The monitoring system is applied to surface grinding processes. The experimental results show that the developed monitoring system is useful not only for monitoring the amount of wear in grinding wheel but also for evaluation the quality of ground surface and determining proper derssing time for the grinding wheel.

• 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 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.30 no.12
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• pp.1245-1251
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• 2013

This study has focused on development of ELID monitoring system and its application to ELID grinding of structural ceramics. ELID monitoring system was consisted of grinding equipment, ELID power supply, grinding wheel, electrode and monitoring program. It can give a real time data to check spindle grinding resistance, wheel revolution, dressing current and voltage in ELID grinding process. The performance of developed system was evaluated by applying to grinding of structural ceramics, silicon carbide and alumina. As the results of experiments, monitored data for spindle resistance and ELID dressing current was useful to check steady-state ELID grinding process. From the comparison of spindle resistance between ELID grinding and conventional grinding process according to change of depth of cut, it could be confirmed that the spindle resistance in ELID grinding was lower than conventional grinding process.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.108-115
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• 1999

Since grinding is a more complicated process than any other machining process, it is hard for operators to setup a grinding machine properly and to find out correctly abnormal grinding states resulting in damages to products. Abnormalities would be caused by improper setup, improper dressing/grinding conditions which are likely to be occurred without skilled operators' attention. In this study, an AE monitoring unit is developed to help operators conduct with ease setup, and set properly dressing/grinding conditions. AErms(root-mean-square) signal being monitored, on-going process states during grinding and dressing is visualized for machine operators to judge whether the processes are in good condition. Evaluation tests are carried out on centerless grinding machines-both cylindrical and internal. The developed AE monitoring system is verified to be useful to check grinding/dressing states in process even in the centerless grinding of which process is most unknown among various grinding methods because of the complex structure.

• Journal of Power System Engineering
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• v.4 no.2
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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.

• 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 Society for Precision Engineering
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• v.17 no.12
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• pp.82-87
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• 2000

This paper deals with monitoring of grinding wheel wear in grinding process. A monitoring system is developed in which a laser scanning micrometer is used to measure the circumferential shape as well as the axial shape of grinding wheel. The monitoring system is applied to grinding machines. The experiment results show that the monitoring system is useful not only for monitoring the amount of wear in grinding wheel but also for measuring the apparent diameter of the grinding wheel.