Neural surface roughness models of CNC machined glass fibre reinforced composites

Alexandrakis, S; Benardos, P; Vosniakos, G-C; Tsouvalis, N

dc.contributor.author	Alexandrakis, S	en
dc.contributor.author	Benardos, P	en
dc.contributor.author	Vosniakos, G-C	en
dc.contributor.author	Tsouvalis, N	en
dc.date.accessioned	2014-03-01T01:28:51Z
dc.date.available	2014-03-01T01:28:51Z
dc.date.issued	2008	en
dc.identifier.issn	0268-1900	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18994
dc.subject	ANN	en
dc.subject	CNC	en
dc.subject	GRFC	en
dc.subject	Image analysis	en
dc.subject	Surface roughness	en
dc.subject	Taguchi	en
dc.subject.classification	Materials Science, Multidisciplinary	en
dc.subject.other	Epoxy resins	en
dc.subject.other	Fiber reinforced plastics	en
dc.subject.other	Friction	en
dc.subject.other	Glass	en
dc.subject.other	Glass fibers	en
dc.subject.other	Painting	en
dc.subject.other	Resins	en
dc.subject.other	Steel analysis	en
dc.subject.other	Surface properties	en
dc.subject.other	Surface roughness	en
dc.subject.other	(1 1 0) surface	en
dc.subject.other	Artificial Neural Network (ANN) models	en
dc.subject.other	Cnc machining	en
dc.subject.other	Computational tools	en
dc.subject.other	cutting conditions	en
dc.subject.other	Damaging effects	en
dc.subject.other	Designed experiments (DoE)	en
dc.subject.other	Fibre reinforced	en
dc.subject.other	Roughness models	en
dc.subject.other	Stabiliser	en
dc.subject.other	Surface qualities	en
dc.subject.other	Taguchi	en
dc.subject.other	Woven roving	en
dc.subject.other	Neural networks	en
dc.title	Neural surface roughness models of CNC machined glass fibre reinforced composites	en
heal.type	journalArticle	en
heal.identifier.primary	10.1504/IJMPT.2008.018986	en
heal.identifier.secondary	http://dx.doi.org/10.1504/IJMPT.2008.018986	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	CNC machining of parts from pre-made Glass Fibre Reinforced Composites (GFRCs) blocks started gaining ground. However, wrong cutting conditions result in poor surface quality, delaminations or other damaging effects. In this work, a computational tool is developed to help improve machinability of these parts by accounting for surface quality. Artificial Neural Network models trained with data obtained through Taguchi-style designed experiments predict surface roughness obtained. GFRC blocks made from D.E.R.321 epoxy resin, CHEM.93-1-74, PC12 stabiliser and Woven Roving (500 gr/m2 and 800 gr/m2) were CNC machined. Microscopy and image analysis studies enrich the ANN models with machined material macro-structural characteristics. Copyright © 2008 Inderscience Enterprises Ltd.	en
heal.publisher	INDERSCIENCE ENTERPRISES LTD	en
heal.journalName	International Journal of Materials and Product Technology	en
dc.identifier.doi	10.1504/IJMPT.2008.018986	en
dc.identifier.isi	ISI:000256711800012	en
dc.identifier.volume	32	en
dc.identifier.issue	2-3	en
dc.identifier.spage	276	en
dc.identifier.epage	294	en