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dc.contributor.author이성환-
dc.date.accessioned2020-12-23T07:03:17Z-
dc.date.available2020-12-23T07:03:17Z-
dc.date.issued2003-11-
dc.identifier.citation한국정밀공학회지, v. 20, no. 11, page. 71-78en_US
dc.identifier.issn1225-9071-
dc.identifier.urihttp://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE00854266-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/156468-
dc.description.abstractThe exit burrs in the micro-drilling of precision miniature holes are of interest, especially for ductile materials. As burrs from this process can be difficult to remove, it is important to acquire the way of prediction burr types as well as optimal cutting conditions which minimize the burrs. In this paper, an artificial neural network was used for the prediction of burr formation in micro-drilling. First, the influence of cutting conditions including cutting speed, feed and drill diameter on the exit burr characteristics, such as burr size and type, were observed and analyzed. Then, the burr types were classified by using the influential experimental data as input parameters to the neural nets.en_US
dc.language.isoko_KRen_US
dc.publisher한국정밀공학회en_US
dc.subjectMicro-drillingen_US
dc.subjectexit burren_US
dc.subjectdeburringen_US
dc.subjectburr typeen_US
dc.subjectcutting conditionen_US
dc.subjectartificial neural networken_US
dc.subject마이크로드릴링en_US
dc.subject출구 버en_US
dc.subject버 제거en_US
dc.subject버 형태en_US
dc.subject절삭 조건en_US
dc.subject인공지능신경망en_US
dc.title마이크로 드릴 가공 시 버 크기의 예측en_US
dc.title.alternativePrediction of Burr Size in Micro-drillingen_US
dc.typeArticleen_US
dc.relation.journal한국정밀공학회지-
dc.contributor.googleauthor이성환-
dc.contributor.googleauthor권성용-
dc.relation.code2012101691-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF ENGINEERING SCIENCES[E]-
dc.sector.departmentDEPARTMENT OF MECHANICAL ENGINEERING-
dc.identifier.pidsunglee-
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MECHANICAL ENGINEERING(기계공학과) > Articles
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