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

Krimpenis, A; Vosniakos, GC

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
heal.journalName	JOURNAL OF INTELLIGENT MANUFACTURING	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