Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이성환 | - |
dc.date.accessioned | 2019-10-30T04:37:04Z | - |
dc.date.available | 2019-10-30T04:37:04Z | - |
dc.date.issued | 2005-10 | - |
dc.identifier.citation | 공학기술논문집, v. 14, Page. 33-42 | en_US |
dc.identifier.uri | http://riet.hanyang.ac.kr/journal/172 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/111673 | - |
dc.description.abstract | Burrs formed during face milling operations can be very difficult to characterize since there exist several parameters which have complex combined effects that affect the cutting process. Many researchers have attempted to predict burr characteristics including burr size and shape, using various experimental parameters such as cutting speed, feed rate, in-plane exit angle, and number of inserts. However, the results of these studies tend to be limited to a specific process parameter range and to certain materials. In this paper, the Taguchi method, a systematic optimization method for design and analysis of experiments, is introduced to acquire optimum cutting conditions for burr minimization. In addition, an in process monitoring scheme using an artificial neural network is presented for the prediction of burr types. | en_US |
dc.language.iso | ko_KR | en_US |
dc.publisher | 한양대학교 공학기술연구소 | en_US |
dc.title | 실험계획법과 뉴럴 네트워크를 이용한 버 형상 예측 | en_US |
dc.title.alternative | Prediction of Burr Type using the Taguchi Method and Neural Network | en_US |
dc.type | Article | en_US |
dc.relation.journal | 공학기술논문집 | - |
dc.contributor.googleauthor | 마채훈 | - |
dc.contributor.googleauthor | 이성환 | - |
dc.contributor.googleauthor | 조용원 | - |
dc.relation.code | 2012210021 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DEPARTMENT OF MECHANICAL ENGINEERING | - |
dc.identifier.pid | sunglee | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.