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Selective Magnetic Abrasive Finishing of Nano-Thickness IZO-Coated Pyrex Glass Using Acoustic Emission Monitoring and Artificial Neural Network

Title
Selective Magnetic Abrasive Finishing of Nano-Thickness IZO-Coated Pyrex Glass Using Acoustic Emission Monitoring and Artificial Neural Network
Author
김정선
Keywords
Acoustic emission monitoring; artificial neural network; coating-substrate boundary; magnetic abrasive finishing; selective nano finishing; surface roughness
Issue Date
2019-09
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE ACCESS, v. 7, Page. 136783-136791
Abstract
In this study, a novel setup for a nanoscale finishing process - magnetic abrasive finishing (MAF) - was investigated together with in-process monitoring using acoustic emissions (AE). A specially fabricated direction control piece with a neodymium magnet was attached to an MAF setup to perform surface finishing of thin-film (IZO) coated Pyrex glass workpieces within a selective area. For the selective finishing experiments, design of experiment (DOE) was applied to optimize the surface roughness of the workpieces. In addition, an acoustic emission (AE) sensor, which can effectively monitor surface roughness and process states during ultraprecision machining/polishing of nanoscale workpieces, was adopted to detect the depth of the polished surface during MAF. The experimental results show that the proposed MAF setup produces uniform surfaces with nano-level surface roughness in a confined (target) area. Moreover, AE monitoring appears to have strong correlations with process states and sufficient sensitivity to detect the critical thickness (the end point of the coating layer). The processed AE signals were utilized as input parameters for an artificial neural network (ANN) to determine whether the polishing was reached to the coating-substrate (Pyrex) boundary. With the proposed polishing and monitoring scheme, controlled nanofinishing of a thin film coated material are feasible in a selective area within specific thickness/layer.
URI
https://ieeexplore.ieee.org/document/8845590http://repository.hanyang.ac.kr/handle/20.500.11754/122029
ISSN
2169-3536
DOI
10.1109/ACCESS.2019.2942689
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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