NDLI: Rough milling optimisation for parts with sculptured surfaces using genetic algorithms in a Stackelberg game

Content Provider	Springer Nature Link
Author	Krimpenis, A. Vosniakos, G. C.
Copyright Year	2008
Abstract	In rough milling of sculptured surface parts, decisions on process parameters concern feedrate, spindle speed, cutting speed, width of cut, raster pattern angle and number of machining slices of variable thickness. In this paper three rough milling objectives are considered: minimum machining time, maximum removed material and maximum uniformity of the remaining volume at the end of roughing. Owing to the complexity of the modelled problem and the large number of parameters, typical genetic algorithms cannot achieve global optima without defining case-dependent constraints. Therefore, to achieve generality, a hierarchical game similar to a Stackelberg game is implemented in which a typical Genetic Algorithm functions as the leader and micro-Genetic Algorithms as followers. In this game, one of the leader’s parameters is responsible for creating a follower’s population and for triggering the optimisation. After properly weighing the three objectives, the follower performs single-objective optimization in steps and feeds the leader back with the objective values as they appear prior to weighing. Micro-Genetic Algorithm (follower) chromosome consists of the distribution of machining slice thickness, while the typical Genetic Algorithm (leader) consists of the milling parameters. The methodology is tested on sculptured surface parts with different properties, and a representative case is presented here.
Starting Page	447
Ending Page	461
Page Count	15
File Format	PDF
ISSN	09565515
Journal	Journal of Intelligent Manufacturing
Volume Number	20
Issue Number	4
e-ISSN	15728145
Language	English
Publisher	Springer US
Publisher Date	2008-07-12
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Rough machining Genetic algorithms Microgenetic algorithms Optimisation Manufacturing, Machines, Tools Production/Logistics Control , Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Software

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