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실험계획법과 뉴럴 네트워크를 이용한 밀링 버 형상 예측

Title
실험계획법과 뉴럴 네트워크를 이용한 밀링 버 형상 예측
Other Titles
Prediction of Burr Types using the Taguchi Method and an Artificial Neural Network
Author
이성환
Keywords
Taguchi method(다구찌 방법); Neural network(신경망); Milling(밀링); Burr(버); Non-dimensionalization(무차원화)
Issue Date
2006-06
Publisher
한국생산제조학회
Citation
한국공작기계학회 논문집, v. 15, No. 3, Page. 45 - 52
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.
URI
http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE00925819&language=ko_KRhttps://repository.hanyang.ac.kr/handle/20.500.11754/108165
ISSN
2508-5093
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MECHANICAL ENGINEERING(기계공학과) > Articles
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