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Prediction of optimal stability states in inward-turning operation using neurogenetic algorithms.
- Source :
- International Journal of Advanced Manufacturing Technology; Dec2009, Vol. 45 Issue 7/8, p679-689, 11p, 1 Color Photograph, 1 Diagram, 3 Charts, 19 Graphs
- Publication Year :
- 2009
-
Abstract
- This paper proposes a neurogenetic-based optimization scheme for predicting localized stable cutting parameters in inward turning operation. A set of cutting experiments are performed in inward orthogonal turning operation. The cutting forces, surface roughness, and critical chatter locations are predicted as a function of operating variables including tool overhang length. Radial basis function neural network is employed to develop the generalization models. Optimum cutting parameters are predicted from the model using binary-coded genetic algorithms. Results are illustrated with the data corresponding to four work materials, i.e., EN8 steel, EN24 steel, mild steel, and aluminum operated over a high speed steel tool. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02683768
- Volume :
- 45
- Issue :
- 7/8
- Database :
- Complementary Index
- Journal :
- International Journal of Advanced Manufacturing Technology
- Publication Type :
- Academic Journal
- Accession number :
- 44917629
- Full Text :
- https://doi.org/10.1007/s00170-009-2007-x