Abstract
The productivity and the efficiency of machining centers are influenced inherently by the cutting conditions and the quality of NC programs. This paper proposes a methodology for incorporating classic and heuristic technique to analyze cutting conditions during the optimization process. The problem of selecting optimal cutting conditions, where the formulation involves the use of empirical relations, is considered. Optimal cutting conditions are determined by machining parameters during the optimization processes. Classic (non-linear) and heuristic (genetic and fuzzy) technique associated with empirical relations make the optimization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carpenter I., Maropoulos P. (2000), Automatic tool selection for milling operations Partl. Cutting data generation; Journal of Engineering Manufacture, V214, 271–282
Rao S., Chen L. (1999), Determination of optimal machining conditions: A coupled uncertainty model; Journal of Manufacturing Science and Engineering, V121, 306–314
Stori J., Wright P. (1999), Integration of process simulation in machining parameter optimization, Journal of Manufacturing Science and Engineering, V121, 134–143
Wang W., Brunn P. (2000), An effective genetic algorithm for job shop scheduling; J Eng. Manufacture, V214, 293–300
Goldberg D. (1989), Genetic algorithms in Search, Optimization Machine learning; Addition - Wesley, USA, 407
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Wien
About this paper
Cite this paper
Gecevska, V., Pavlovski, V. (2002). Selection of Optimal Cutting Condition in Computer Aided Machining by Genetic Algorithms. In: Kulianic, E. (eds) AMST’02 Advanced Manufacturing Systems and Technology. International Centre for Mechanical Sciences, vol 437. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2555-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-7091-2555-7_21
Publisher Name: Springer, Vienna
Print ISBN: 978-3-7091-2557-1
Online ISBN: 978-3-7091-2555-7
eBook Packages: Springer Book Archive