Prediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network

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dc.authoridERKAN, Omer/0000-0002-9428-4299
dc.authoridKARA, Fuat/0000-0002-3811-3081
dc.authoridCicek, Adem/0000-0002-9510-3242
dc.contributor.authorErkan, Omer
dc.contributor.authorIsik, Birhan
dc.contributor.authorCicek, Adem
dc.contributor.authorKara, Fuat
dc.date.accessioned2024-09-29T15:51:12Z
dc.date.available2024-09-29T15:51:12Z
dc.date.issued2013
dc.departmentKarabük Üniversitesien_US
dc.description.abstractGlass fibre reinforced plastic (GFRP) composites are an economic alternative to engineering materials because of their superior properties. Some damages on the surface occur due to their complex cutting mechanics in cutting process. Minimisation of the damages is fairly important in terms of product quality. In this study, a GFRP composite material was milled to experimentally minimise the damages on the machined surfaces, using two, three and four flute end mills at different combinations of cutting parameters. Experimental results showed that the damage factor increased with increasing cutting speed and feed rate, on the other hand, it was found that the damage factor decreased with increasing depth of cut and number of the flutes. In addition, analysis of variance (ANOVA) results clearly revealed that the feed rate was the most influential parameter affecting the damage factor in end milling of GFRP composites. Also, in present study, Artificial Neural Network (ANN) models with five learning algorithms were used in predicting the damage factor to reduce number of expensive and time-consuming experiments. The highest performance was obtained by 4-10-1 network structure with LM learning algorithm. ANN was notably successful in predicting the damage factor due to higher R-2 and lower RMSE and MEP.en_US
dc.identifier.doi10.1007/s10443-012-9286-3
dc.identifier.endpage536en_US
dc.identifier.issn0929-189X
dc.identifier.issn1573-4897
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-84881318266en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage517en_US
dc.identifier.urihttps://doi.org/10.1007/s10443-012-9286-3
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3924
dc.identifier.volume20en_US
dc.identifier.wosWOS:000322706000012en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofApplied Composite Materialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGlass fibre reinforced plastic compositesen_US
dc.subjectEnd millingen_US
dc.subjectDamage factoren_US
dc.subjectANNen_US
dc.titlePrediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Networken_US
dc.typeArticleen_US

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