Artificial neural network models for the prediction of surface roughness in electrical discharge machining

Markopoulos, AP; Manolakos, DE; Vaxevanidis, NM

dc.contributor.author	Markopoulos, AP	en
dc.contributor.author	Manolakos, DE	en
dc.contributor.author	Vaxevanidis, NM	en
dc.date.accessioned	2014-03-01T01:27:57Z
dc.date.available	2014-03-01T01:27:57Z
dc.date.issued	2008	en
dc.identifier.issn	0956-5515	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18650
dc.subject	Artificial neural networks	en
dc.subject	Electrical discharge machining (EDM)	en
dc.subject	Modeling	en
dc.subject	Surface roughness	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Manufacturing	en
dc.subject.other	Electric discharges	en
dc.subject.other	Machining	en
dc.subject.other	Mathematical models	en
dc.subject.other	Surface roughness	en
dc.subject.other	Input parameters	en
dc.subject.other	Pulse current	en
dc.subject.other	Pulse duration	en
dc.subject.other	Neural networks	en
dc.title	Artificial neural network models for the prediction of surface roughness in electrical discharge machining	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s10845-008-0081-9	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s10845-008-0081-9	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	In the present paper Artificial Neural Networks (ANNs) models are proposed for the prediction of surface roughness in Electrical Discharge Machining (EDM). For this purpose two well-known programs, namely Matlab® with associated toolboxes, as well as Netlab®, were emplo- yed. Training of the models was performed with data from an extensive series of EDM experiments on steel grades; the proposed models use the pulse current, the pulse duration, and the processed material as input parameters. The reported results indicate that the proposed ANNs models can satisfactorily predict the surface roughness in EDM. Moreover, they can be considered as valuable tools for the process planning for EDMachining. © 2008 Springer Science+Business Media, LLC.	en
heal.publisher	SPRINGER	en
heal.journalName	Journal of Intelligent Manufacturing	en
dc.identifier.doi	10.1007/s10845-008-0081-9	en
dc.identifier.isi	ISI:000256080900004	en
dc.identifier.volume	19	en
dc.identifier.issue	3	en
dc.identifier.spage	283	en
dc.identifier.epage	292	en