Artificial neural networks modeling of surface finish in electro-discharge machining of tool steels

Markopoulos, A; Vaxevanidis, NM; Petropoulos, G; Manolakos, DE

dc.contributor.author	Markopoulos, A	en
dc.contributor.author	Vaxevanidis, NM	en
dc.contributor.author	Petropoulos, G	en
dc.contributor.author	Manolakos, DE	en
dc.date.accessioned	2014-03-01T02:50:19Z
dc.date.available	2014-03-01T02:50:19Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35051
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-33845748242&partnerID=40&md5=0518260b564166c476531d615db3ebff	en
dc.subject.other	Computer simulation	en
dc.subject.other	Electric discharge machining	en
dc.subject.other	Finishing	en
dc.subject.other	Melting	en
dc.subject.other	Neural networks	en
dc.subject.other	Complex metal removal mechanism	en
dc.subject.other	Levenberg-Marquardt algorithm	en
dc.subject.other	Plasma channels	en
dc.subject.other	Workpiece electrodes	en
dc.subject.other	Tool steel	en
dc.title	Artificial neural networks modeling of surface finish in electro-discharge machining of tool steels	en
heal.type	conferenceItem	en
heal.publicationDate	2006	en
heal.abstract	Electro-Discharge machining (EDM) is a thermal process with a complex metal removal mechanism that involves the formation of a plasma channel between the tool and the workpiece electrodes and the melting and evaporation of material resulted thus in the generation of a rough surface consisting of a large number of randomly overlapping craters and no preferential direction. EDM is considered especially suitable for machining complex contours, with high accuracy and for materials that are not amenable to conventional removal methods. However, certain phenomena negatively affecting the surface integrity of EDMed workpieces, constrain the expanded application of the technology. Accordingly, it has been difficult to establish models that correlate accurately the operational variables and the performance towards the optimization of the process. In recent years, artificial neural networks (ANN) have emerged as a novel modeling technique that is able to provide reliable results and it can be integrated into a great number of technological areas including various aspects of manufacturing. In this paper ANN models for the prediction of the surface roughness of electro-discharge machined surfaces are presented. A feed-forward artificial ANN trained with the Levenberg-Marquardt algorithm was finally selected. The proposed neural network takes into consideration the pulse current and the pulse-on time as EDM process variables, for three different tool steels in order to determine the center-line average (Ra) and the maximum height of the profile (Rt) surface roughness parameters. Copyright © 2006 by ASME.	en
heal.journalName	Proceedings of 8th Biennial ASME Conference on Engineering Systems Design and Analysis, ESDA2006	en
dc.identifier.volume	2006	en