AE; 음향방출; 표면거칠기; 자성연마입자 피니싱; 가공력 센서; 인공지능신경망; Acoustic Emission; Surface Roughness; Magnetic Abrasive Finishing; Force Sensor; Artificial Neural Networks
Issue Date
2009-05
Publisher
대한기계학회
Citation
대한기계학회논문집 A, v. 33, NO. 5, Page. 514-520
Abstract
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 roughenss) 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 netwoks 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.