• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.211-212
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• 2006

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.123-124
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• 2012

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.135-136
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• 2012

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.433-434
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.285-286
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• 2007

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.163-164
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.137-138
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• 2010

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.601-607
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• 2012

The magnetic abrasive polishing (MAP), for online monitoring with AE sensor attachment, was performed in this study. To predict the surface roughness after the magnetic abrasive polishing of NAK80, the signal data acquired from the AE sensor were analyzed. A dimensionless coefficient, which consisted of average of AErms and standard deviation of AE signal, was defined as a characteristic of the MAP and a prediction model was obtained using least square method. A neural network, which had multiple input parameters from AE signals and polishing conditions, was applied for predicting the surface roughness. As a result of this study, it was seen that there was very close correlation between the AE signal and the surface roughness in the MAP. And then on-line prediction of the surface roughness after the MAP of the NAK80 was possible by the developed prediction model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.5
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• pp.514-520
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• 2009

In configuring an automated polishing system, a monitoring scheme to estimate the surface roughness is necessary. In this study, a precision polishing process, magnetic abrasive finishing (MAF), along with an in-process monitoring setup was investigated. A magnetic tooling is connected to a CNC machining to polish the surface of stavax(S136) die steel workpieces. During finishing experiments, both AE signals and force signals were sampled and analysed. The finishing results show that MAF has nano scale finishing capability (upto 8nm in surface roughness) and the sensor signals have strong correlations with the parameters such as gap between the tool and workpiece, feed rate and abrasive size. In addition, the signals were utilized as the input parameters of artificial neural networks to predict generated surface roughness. Among the three networks constructed -AE rms input, force input, AE+force input- the ANN with sensor fusion (AE+force) produced most stable results. From above, it has been shown that the proposed sensor fusion scheme is appropriate for the monitoring and prediction of the nano scale precision finishing process.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.4 no.3
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• pp.31-38
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• 1995

Magnetic-electrolytic-abrasive polishing(MEAP) system was newly developed and the finishing characteristics of Cr-coated roller was analyzed. The paper describes the operational principle of MEAP system and magnetic field effect on the MEAP process by experimental results. The finishing characteristics and optimal finishing condition for Cr-coated roller was experimented and analyzed.