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

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dc.contributor.author Krimpenis, A en
dc.contributor.author Vosniakos, GC en
dc.date.accessioned 2014-03-01T01:31:49Z
dc.date.available 2014-03-01T01:31:49Z
dc.date.issued 2009 en
dc.identifier.issn 0956-5515 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/19944
dc.subject Rough machining en
dc.subject Genetic algorithms en
dc.subject Microgenetic algorithms en
dc.subject Optimisation en
dc.subject.classification Computer Science, Artificial Intelligence en
dc.subject.classification Engineering, Manufacturing en
dc.subject.other CUTTING CONDITIONS en
dc.subject.other NEURAL-NETWORK en
dc.subject.other OPERATIONS en
dc.subject.other SELECTION en
dc.title Rough milling optimisation for parts with sculptured surfaces using genetic algorithms in a Stackelberg game en
heal.type journalArticle en
heal.identifier.primary 10.1007/s10845-008-0147-8 en
heal.identifier.secondary http://dx.doi.org/10.1007/s10845-008-0147-8 en
heal.language English en
heal.publicationDate 2009 en
heal.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. en
heal.publisher SPRINGER en
dc.identifier.doi 10.1007/s10845-008-0147-8 en
dc.identifier.isi ISI:000268103100009 en
dc.identifier.volume 20 en
dc.identifier.issue 4 en
dc.identifier.spage 447 en
dc.identifier.epage 461 en

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