BROWSE

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Jung, Mooyoung
Ulsan National Institute of Science and Technology
Research Interests
  • Manufacturing Systems Engineering
  • Energy Management System - Applications of Fractal Concept

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ONLINE MONITORING OF TOOL BREAKAGE IN FACE MILLING USING A SELF-ORGANIZED NEURAL-NETWORK

DC Field Value Language
dc.contributor.author KO, TJ ko
dc.contributor.author CHO, DW ko
dc.contributor.author Jung, Mooyoung ko
dc.date.available 2014-09-11T07:02:26Z -
dc.date.created 2014-09-04 ko
dc.date.issued 1995 ko
dc.identifier.citation JOURNAL OF MANUFACTURING SYSTEMS, v.14, no.2, pp.80 - 90 ko
dc.identifier.issn 0278-6125 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/5930 -
dc.description.abstract This study introduces a new tool breakage monitoring methodology consisting of an unsupervised neural network combined with an adaptive time-series modeling algorithm. Cutting force signals are modeled by a discrete autoregressive model in which parameters are estimated recursively at each sampling instant using a parameter-adaptation algorithm based on a recursive least square. The experiment shows that monitoring the evolution of autoregressive parameters during milling is effective for detecting tool breakage. An adaptive resonance network based on Grossberg's adaptive resonance theory (ART 2) is employed for clustering tool states using model parameters, and this network has unsupervised learning capability. This system subsequently operates successfully with a fast monitoring time in a wide range of cutting conditions without a priori knowledge of the cutting process. ko
dc.description.statementofresponsibility close -
dc.language 영어 ko
dc.publisher ELSEVIER SCI LTD ko
dc.title ONLINE MONITORING OF TOOL BREAKAGE IN FACE MILLING USING A SELF-ORGANIZED NEURAL-NETWORK ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-0011757673 ko
dc.identifier.wosid A1995RB47600002 ko
dc.type.rims ART ko
dc.description.wostc 17 *
dc.description.scopustc 26 *
dc.date.tcdate 2015-05-06 *
dc.date.scptcdate 2014-09-04 *
dc.identifier.doi 10.1016/0278-6125(95)98889-E ko
dc.identifier.url http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=0011757673 ko
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