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dc.contributor.author이성환-
dc.date.accessioned2020-12-21T01:12:21Z-
dc.date.available2020-12-21T01:12:21Z-
dc.date.issued2003-10-
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/20.500.11754/156369-
dc.description.abstracthe 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 predicting 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.publisher한국생명공학회en_US
dc.subjectMicro-drilling (마이크로드릴링)en_US
dc.subjectexit burr (줄구 버)en_US
dc.subjectdeburring (버 제거)en_US
dc.subjectburr type (버 형태)en_US
dc.subjectcutting condition (절삭 조건)en_US
dc.subjectartificial neural network (인공지능신경망)en_US
dc.title마이크로 드릴가공 시 버 발생에 대한 연구en_US
dc.title.alternativePrediction of Burr Size in Micro-drillingen_US
dc.typeArticleen_US
dc.relation.journal공학기술논문집-
dc.relation.code2012210021-
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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