137 0

Full metadata record

DC FieldValueLanguage
dc.contributor.author이성환-
dc.date.accessioned2018-06-18T04:29:52Z-
dc.date.available2018-06-18T04:29:52Z-
dc.date.issued2017-06-
dc.identifier.citationINTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v. 55, No. 17, Page. 4833-4846en_US
dc.identifier.issn0020-7543-
dc.identifier.issn1366-588X-
dc.identifier.urihttps://www.tandfonline.com/doi/abs/10.1080/00207543.2016.1254355-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/72115-
dc.description.abstractIn the micro drilling of precision miniature holes, the formation of exit burrs is a topic of interest, especially for ductile materials. Because such burrs are difficult to remove, it is important to be able to predict various burr types and to employ burr minimisation schemes that consider burrs' micro-scale characteristics. In the present work, an artificial neural network (ANN) was used to predict the formation of burrs in the micro drilling of copper and brass, along with burr formation/optimisation analysis specialised for micro drills. The influence of cutting conditions, including cutting speed, feed and drill diameter, upon exit micro burr characteristics such as burr size and type was observed, analysed and classified. Based on the results, an empirical equation to predict micro burr height is proposed herein. The classification results were compared with conventional burr cases using burr control charts. Then, micro burr types were predicted by means of an ANN, using the influential parameters as input vectors. The usefulness of the proposed scheme was demonstrated by comparing the experimental and prediction/analysis results.en_US
dc.language.isoen_USen_US
dc.publisherTAYLOR & FRANCIS LTDen_US
dc.subjectmicro drillingen_US
dc.subjectdrilling burr formationen_US
dc.subjectburr type classificationen_US
dc.subjectdrilling burr predictionen_US
dc.subjectartificial neural networken_US
dc.subjectMODELen_US
dc.titleClassification and prediction of burr formation in micro drilling of ductile metalsen_US
dc.typeArticleen_US
dc.relation.no17-
dc.relation.volume55-
dc.identifier.doi10.1080/00207543.2016.1254355-
dc.relation.page4833-4846-
dc.relation.journalINTERNATIONAL JOURNAL OF PRODUCTION RESEARCH-
dc.contributor.googleauthorAhn, Yoomin-
dc.contributor.googleauthorLee, Seoung Hwan-
dc.relation.code2017002924-
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
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE