Estimation of surface roughness and cutting speed in CNC WEDM by artificial neural network that employed trainable activation function

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dc.authorid0000-0003-1391-172Xen_US
dc.contributor.authorGürbüz, Hüseyin
dc.date.accessioned2021-11-17T08:07:20Z
dc.date.available2021-11-17T08:07:20Z
dc.date.issued2021-02-01en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Makine Mühendisliği Bölümüen_US
dc.description.abstractActivation functions are the most significant properties of artificial neural networks (ANN) because these functions are directly related with the ability of ANN in learning or modelling a system or a function. Furthermore, another reason for the significance of the fact that determination of optimal activation function in ANN is its relationship with success level. In this experimental study, the effects of different types of wire electrodes, cooling techniques and workpiece materials on surface roughness (Ra) and cutting speed (Vc) in wire electrical discharge machining (WEDM) were investigated by using trainable activation functions (AFt) and modelling them in ANNs. So far, a number of methods have been performed according to the data set in order to optimally predict Ra and Vc results. Among these methods, randomized ANN with AFt was found to be the best one for robust prediction according to RMSE values. While the value was 0.280 for Vc, it was 0.2104 for Ra. Optimum activation functions in Ra and Vc were found at first and third degree trainable functions, respectively.en_US
dc.identifier.citationGürbüz, H. (2021). Estimation of surface roughness and cutting speed in CNC WEDM by artificial neural network that employed trainable activation function. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 235 (15). pp.2737-2735. DOI:https://dx.doi.org/10.1177/0954406221990057en_US
dc.identifier.endpage2753en_US
dc.identifier.issn0954-4062
dc.identifier.issue15en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2737en_US
dc.identifier.urihttps://dx.doi.org/10.1177/0954406221990057
dc.identifier.urihttps://hdl.handle.net/20.500.12402/4002
dc.identifier.volume235en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSAGE Journalsen_US
dc.relation.isversionof10.1177/0954406221990057en_US
dc.relation.journalProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectRandom Weight Artificial Neural Networken_US
dc.subjectTrainable Activation Functionen_US
dc.subjectArtificial Neural Networken_US
dc.subjectWEDMen_US
dc.subjectSurface Roughnessen_US
dc.subjectCutting Speeden_US
dc.titleEstimation of surface roughness and cutting speed in CNC WEDM by artificial neural network that employed trainable activation functionen_US
dc.title.alternativeEğitilebilir aktivasyon fonksiyonu kullanan yapay sinir ağı ile CNC WEDM'de yüzey pürüzlülüğü ve kesme hızı tahminien_US
dc.typeArticleen_US

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