Chatter Monitoring of Machining Center Using Head Stock Structural Vibration Analyzed with a 1D Convolutional Neural Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Kwanghun | - |
dc.contributor.author | Seong, Yeonuk | - |
dc.contributor.author | Jeon, Jonghoon | - |
dc.contributor.author | Moon, Seongjun | - |
dc.contributor.author | Park, Junhong | - |
dc.date.accessioned | 2022-09-19T13:37:13Z | - |
dc.date.available | 2022-09-19T13:37:13Z | - |
dc.date.created | 2022-09-08 | - |
dc.date.issued | 2022-07 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/171579 | - |
dc.description.abstract | Real-time chatter detection is crucial for the milling process to maintain the workpiece surface quality and minimize the generation of defective parts. In this study, we propose a new methodology based on the measurement of machine head stock structural vibration. A short-pass lifter was applied to the cepstrum to effectively remove components resulting from spindle rotations and to extract structural vibration modal components of the machine. The vibration modal components include information about the wave propagation from the cutter impact to the head stock. The force excitation from the interactions between the cutter and workpiece induces structural vibrations of the head stock. The vibration magnitude for the rigid body modes was smaller in the chatter state compared to that in the stable state. The opposite variation was observed for the bending modes. The liftered spectrum was used to obtain this dependence of vibration on the cutting states. The one-dimensional convolutional neural network extracted the required features from the liftered spectrum for pattern recognition. The classified features allowed demarcation between the stable and chatter states. The chatter detection efficiency was demonstrated by application to the machining process using different cutting parameters. The classification performance of the proposed method was verified with comparison between different classifiers. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Chatter Monitoring of Machining Center Using Head Stock Structural Vibration Analyzed with a 1D Convolutional Neural Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Junhong | - |
dc.identifier.doi | 10.3390/s22145432 | - |
dc.identifier.scopusid | 2-s2.0-85135105204 | - |
dc.identifier.wosid | 000831988200001 | - |
dc.identifier.bibliographicCitation | SENSORS, v.22, no.14, pp.1 - 19 | - |
dc.relation.isPartOf | SENSORS | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 14 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 19 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | CEPSTRUM | - |
dc.subject.keywordAuthor | chatter detection | - |
dc.subject.keywordAuthor | structural vibration | - |
dc.subject.keywordAuthor | cepstral analysis | - |
dc.subject.keywordAuthor | modal analysis | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/22/14/5432 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.