DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jun, CH | - |
dc.contributor.author | Suh, SH | - |
dc.date.accessioned | 2016-03-31T13:39:58Z | - |
dc.date.available | 2016-03-31T13:39:58Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 1999-11 | - |
dc.identifier.issn | 0890-6955 | - |
dc.identifier.other | 1999-OAK-0000000852 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/20333 | - |
dc.description.abstract | We develop a vibration sensor-based tool breakage detection system for NC milling operations. The system obtains the time-domain vibration signal from the sensor attached on the spindle bracket of our CNC machine and declares tool failures through the on-line monitoring schemes. For the on-line detection, our approach is to use the statistical process control methods where control limits or thresholds are automatically calculated independently of cutting conditions. The main thrust of this paper is to compare the performance of the proposed statistical process monitoring methods including the X-bar control scheme, the exponentially weighted moving average (EWMA) scheme, and the adaptive EWMA scheme. The performance of the control schemes are compared in terms of the type I and II errors calculated from the experiment data. (C) 1999 Elsevier Science Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | FAILURE-DETECTION | - |
dc.subject | OPERATIONS | - |
dc.subject | SYSTEM | - |
dc.title | Statistical tool breakage detection schemes based on vibration signals in NC milling | - |
dc.type | Article | - |
dc.contributor.college | 산업경영공학과 | - |
dc.identifier.doi | 10.1016/S0890-6955(99)00028-0 | - |
dc.author.google | Jun, CH | - |
dc.author.google | Suh, SH | - |
dc.relation.volume | 39 | - |
dc.relation.issue | 11 | - |
dc.relation.startpage | 1733 | - |
dc.relation.lastpage | 1746 | - |
dc.contributor.id | 10070936 | - |
dc.relation.journal | INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, v.39, no.11, pp.1733 - 1746 | - |
dc.identifier.wosid | 000081855400005 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 1746 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1733 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE | - |
dc.citation.volume | 39 | - |
dc.contributor.affiliatedAuthor | Jun, CH | - |
dc.contributor.affiliatedAuthor | Suh, SH | - |
dc.identifier.scopusid | 2-s2.0-0033216665 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 13 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | FAILURE-DETECTION | - |
dc.subject.keywordPlus | OPERATIONS | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.relation.journalWebOfScienceCategory | Engineering, Manufacturing | - |
dc.relation.journalWebOfScienceCategory | Engineering, Mechanical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
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