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http://dx.doi.org/10.7735/ksmte.2012.21.4.601

Surface Condition Monitoring in Magnetic Abrasive Polishing of NAK80 Using AE Sensor and Neural Network  

Kim, Kwang-Heui (부경대학교 기계자동차공학과)
Shin, Chang-Min (부경대학교 기계공학과)
Kim, Tae-Wan (부경대학교 기계공학과)
Kwak, Jae-Seob (부경대학교 기계공학과)
Publication Information
Journal of the Korean Society of Manufacturing Technology Engineers / v.21, no.4, 2012 , pp. 601-607 More about this Journal
Abstract
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.
Keywords
Acoustic emission; Dimensionless coefficient; Magnetic abrasive polishing; Neural network; Process parameter; Surface roughness;
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Times Cited By KSCI : 1  (Citation Analysis)
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