A holistic approach to the exploitation of simulation in solid investment casting

Pagratis, N; Karagiannis, N; Vosniakos, G-C; Pantelis, D; Benardos, P

dc.contributor.author	Pagratis, N	en
dc.contributor.author	Karagiannis, N	en
dc.contributor.author	Vosniakos, G-C	en
dc.contributor.author	Pantelis, D	en
dc.contributor.author	Benardos, P	en
dc.date.accessioned	2014-03-01T01:25:41Z
dc.date.available	2014-03-01T01:25:41Z
dc.date.issued	2007	en
dc.identifier.issn	0954-4054	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17722
dc.subject	Heat transfer coefficient	en
dc.subject	Investment casting	en
dc.subject	Simulation	en
dc.subject	Temperature optimization	en
dc.subject.classification	Engineering, Manufacturing	en
dc.subject.classification	Engineering, Mechanical	en
dc.subject.other	Computer simulation	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Heat transfer coefficients	en
dc.subject.other	Neural networks	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Temperature distribution	en
dc.subject.other	Commercial casting simulation	en
dc.subject.other	Heat transfer coefficient	en
dc.subject.other	Temperature optimization	en
dc.subject.other	Investment casting	en
dc.title	A holistic approach to the exploitation of simulation in solid investment casting	en
heal.type	journalArticle	en
heal.identifier.primary	10.1243/09544054JEM465	en
heal.identifier.secondary	http://dx.doi.org/10.1243/09544054JEM465	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	The ultimate aim of casting simulation is to recommend process parameter values that result in the best possible casting quality. However, commercial casting simulation is currently used as a trial-and-error tool, mostly comparing between possible scenarios with no guarantee of realism. Three techniques are presented to overcome these discrepancies for solid investment casting of jewellery as a test bed. The first technique consists in recording temperature versus time profiles by thermocouples embedded in the casting. These profiles are used as calibration reference regarding the overall heat transfer coefficient at the melt-mould interface in simulation. The second technique consists in metallographic validation of simulation findings in terms of correlating microstructure and defects of the casting with simulation results concerning temperature distribution and porosity. The third technique consists in introducing genetic algorithms as an optimization tool. A small number of simulation sample runs link process parameters of interest, such as mould temperature and melt temperature, with results characterizing casting quality, e.g. porosity. These samples are used to train a neural network, thereby creating a generalized meta-model of the process, which is directly used as the fitness function of the genetic algorithm. Combining the three techniques described above caters for realistic and practical casting process planning using commercially available casting simulation software as a tool. © 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	10.1243/09544054JEM465	en
dc.identifier.isi	ISI:000248632000004	en
dc.identifier.volume	221	en
dc.identifier.issue	6	en
dc.identifier.spage	967	en
dc.identifier.epage	979	en