A method for optimizing process parameters in layer-based rapid prototyping

Vosniakos, G-C; Maroulis, T; Pantelis, D

dc.contributor.author	Vosniakos, G-C	en
dc.contributor.author	Maroulis, T	en
dc.contributor.author	Pantelis, D	en
dc.date.accessioned	2014-03-01T01:25:42Z
dc.date.available	2014-03-01T01:25:42Z
dc.date.issued	2007	en
dc.identifier.issn	0954-4054	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17730
dc.subject	Genetic algorithms	en
dc.subject	Neural networks	en
dc.subject	Optimization	en
dc.subject	Rapid prototyping	en
dc.subject.classification	Engineering, Manufacturing	en
dc.subject.classification	Engineering, Mechanical	en
dc.subject.other	Approximation theory	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Neural networks	en
dc.subject.other	Numerical methods	en
dc.subject.other	Optimization	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Layer-based prototyping	en
dc.subject.other	Optimization problem	en
dc.subject.other	Optimizing process parameters	en
dc.subject.other	Process parameters	en
dc.subject.other	Rapid prototyping	en
dc.title	A method for optimizing process parameters in layer-based rapid prototyping	en
heal.type	journalArticle	en
heal.identifier.primary	10.1243/09544054JEM815	en
heal.identifier.secondary	http://dx.doi.org/10.1243/09544054JEM815	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	In layer-based rapid prototyping, a volumetric object is approximated as a pile of slices with vertical walls. Process parameter selection in layer-based prototyping is a multicriteria multiparameter optimization problem. A number of criteria may be used for assessing the prototype's quality. Volumetric accuracy of shape approximation and building time are just two criteria taken in this work as an example. Criteria depend on process parameters, most commonly in a mutually contradictory manner. Model orientation and slice thickness constitute the minimum of process parameters to be considered, but others may also be added. For this reason, a neural network is used, trained by a number of input-output vectors, when analytical formulae representing the dependency of criteria on process parameters are not possible to develop and/or available numerical models take too long to execute. Neural network meta-models are used in the evaluation (cost) function of a genetic algorithm, each representing a particular criterion, and criteria are weighted according to the user's particular view. A case study is presented, referring to a wax model prototyping machine in which a particular tree for investment casting is built. A new criterion for assessing the quality of shape approximation is introduced, namely the local volumetric error per slice. © IMechE 2007.	en
heal.publisher	PROFESSIONAL ENGINEERING PUBLISHING LTD	en
heal.journalName	Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture	en
dc.identifier.doi	10.1243/09544054JEM815	en
dc.identifier.isi	ISI:000249899600006	en
dc.identifier.volume	221	en
dc.identifier.issue	8	en
dc.identifier.spage	1329	en
dc.identifier.epage	1340	en