The scope of artificial neural network metamodels for precision casting process planning

Vosniakos, GC; Galiotou, V; Pantelis, D; Benardos, P; Pavlou, P

dc.contributor.author	Vosniakos, GC	en
dc.contributor.author	Galiotou, V	en
dc.contributor.author	Pantelis, D	en
dc.contributor.author	Benardos, P	en
dc.contributor.author	Pavlou, P	en
dc.date.accessioned	2014-03-01T01:32:11Z
dc.date.available	2014-03-01T01:32:11Z
dc.date.issued	2009	en
dc.identifier.issn	07365845	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20068
dc.subject	Casting parameters	en
dc.subject	Investment casting	en
dc.subject	Neural networks	en
dc.subject.other	Area ratios	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Casting conditions	en
dc.subject.other	Casting parameters	en
dc.subject.other	Casting simulation software	en
dc.subject.other	Expandability	en
dc.subject.other	Feed-forward artificial neural networks	en
dc.subject.other	Feeding points	en
dc.subject.other	Generalisation	en
dc.subject.other	Input parameter	en
dc.subject.other	Melting temperatures	en
dc.subject.other	Meta model	en
dc.subject.other	Mould temperature	en
dc.subject.other	Part geometry	en
dc.subject.other	Predictive capabilities	en
dc.subject.other	Product quality	en
dc.subject.other	Simulation result	en
dc.subject.other	Software simulation	en
dc.subject.other	Surface area	en
dc.subject.other	User friendly interface	en
dc.subject.other	Work pieces	en
dc.subject.other	Backpropagation	en
dc.subject.other	Computer software	en
dc.subject.other	Investment casting	en
dc.subject.other	Melting point	en
dc.subject.other	Neural networks	en
dc.subject.other	Precision casting	en
dc.subject.other	Process control	en
dc.subject.other	Process engineering	en
dc.subject.other	Simulators	en
dc.subject.other	Investments	en
dc.title	The scope of artificial neural network metamodels for precision casting process planning	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.rcim.2009.04.018	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.rcim.2009.04.018	en
heal.publicationDate	2009	en
heal.abstract	Precision investment casting process planning has been tackled in the past according to experience. Recently, casting simulation software is being increasingly used to predict product quality by implementing 'what-if' scenarios. Input parameters include relatively simple factors such as mould temperature, melting temperature, casting material. They also include factors whose influence is more complex to quantify, such number and location of feeding points, diameter and length of inflow channels, angle of channel with respect to the main sprue axis. Simulation results cannot help the engineer for workpieces other than the one simulated. In this paper a series of feedforward artificial neural network (ANN) models is presented aiming at such generalisation. To achieve this, a large number of software simulation runs were conducted for a number of different small parts, with varying runner geometry and casting conditions. The parameters characterising part geometry have been chosen to be surface area and volume-to-area ratio. The different ANN models predictive capabilities are reflected to the respective training and generalisation errors. A user-friendly interface has been conducted for model execution in a complete application, whose main virtue is expandability. © 2009 Elsevier Ltd. All rights reserved.	en
heal.journalName	Robotics and Computer-Integrated Manufacturing	en
dc.identifier.doi	10.1016/j.rcim.2009.04.018	en
dc.identifier.volume	25	en
dc.identifier.issue	6	en
dc.identifier.spage	909	en
dc.identifier.epage	916	en