CUTTING STATE MONITORING IN MILLING BY A NEURAL-NETWORK
SCIE
SCOPUS
- Title
- CUTTING STATE MONITORING IN MILLING BY A NEURAL-NETWORK
- Authors
- CHO, DW; KO, TJ
- Date Issued
- 1994-07
- Publisher
- ELSEVIER SCI LTD
- Abstract
- The application of a neural network to cutting state monitoring in face milling was introduced and evaluated on multiple sensor data such as cutting forces and vibrations. This monitoring system consists of a statistically based adaptive preprocessor (autoregressive (AR) time series modeling) for generating features from each sensor, followed by a highly parallel neural network for associating the preprocessor outputs (sensor fusion) with the appropriate decisions. AR model parameters were used as features, and the cutting states (normal, unstable and tool life end) were successfully detected by monitoring the evolution of model parameters during face milling. The proposed system offers fast operation through recursive preprocessing and highly parallel association, and a data-driven training scheme without explicit rules or a priori statistics. It appears proven on limited experimental data.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/21948
- DOI
- 10.1016/0890-6955(94)90050-7
- ISSN
- 0890-6955
- Article Type
- Article
- Citation
- INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, vol. 34, no. 5, page. 659 - 676, 1994-07
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.