Proceedings of the Korean Society of Precision Engineering Conference (한국정밀공학회:학술대회논문집)
- 2005.06a
- /
- Pages.452-455
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- 2005
- /
- 2005-8446(pISSN)
A Study on the Cutting Characteristics and Detection of the Abnormal Tool State in Hard Turning
고경도강 선삭시 절삭특성 및 공구 이상상태 검출에 관한 연구
- Lee S.J. (Graduate School, Chonbuk Nat'l Univ.) ;
- Shin H.G. (Graduate School, Chonbuk Nat'l Univ.) ;
-
Kim M.H.
(Graduate School, Chonbuk Nat'l Univ.)
;
- Kim J.T. (Graduate School, Chonbuk Nat'l Univ.) ;
- Lee H.K. (Graduate School, Chonbuk Nat'l Univ.) ;
- Kim T.Y. (Chonbuk Nat'l Univ.)
- 이상진 (전북대 대학원) ;
- 신형곤 (전북대 대학원) ;
-
김민호
(전북대 대학원)
;
- 김종택 (전북대 대학원) ;
- 이한교 (전북대 대학원) ;
- 김태영 (전북대 기계공학부, 자동차신기술 연구소)
- Published : 2005.06.01
Abstract
The cutting characteristics of hardened steel by a PCBN tool is investigated with respect to workpiece surface roughness, cutting force and tool flank wear of the vision system. Backpropagation neural networks (BPNs) were used for detection of tool wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output was the tool wear state which was either usable or failure. Hard turning experiments with various spindle rotational speed and feed rates were carried out. The learning process was performed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.
Keywords