Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot

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dc.contributor.authorAyyıldız, Mustafa
dc.date.accessioned2020-04-30T23:19:15Z
dc.date.available2020-04-30T23:19:15Z
dc.date.issued2019
dc.departmentDÜ, Teknik Eğitim Fakültesi, Makine Eğitimi Bölümüen_US
dc.descriptionWOS: 000482447100011en_US
dc.description.abstractPurpose This paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot. Design/methodology/approach In ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R-2) for the experimental data were determined based on conjugate gradient back propagation, Levenberg-Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagation feed forward back propagation training algorithm with logistic transfer function. Findings In the ANN architecture established for the surface roughness (Ra), three neurons [cutting speed (V), feed rate (f) and depth of cut (a)] were contained in the input layer, five neurons were included in its hidden layer and one neuron was contained in the output layer (3-5-1).Trials showed that LM learning algorithm was the best learning algorithm for the surface roughness. The ANN model obtained with the LM learning algorithm yielded estimation training values R-2 (97.5 per cent) and testing values R-2 (99 per cent). The R-2 for multiple regressions was obtained as 96.1 per cent. Originality/value The result of the surface roughness estimation model showed that the equation obtained from the multiple regressions with quadratic model had an acceptable estimation capacity. The ANN model showed a more dependable estimation when compared with the multiple regression models. Hereby, these models can be used to effectively control the milling process to reach a satisfactory surface quality.en_US
dc.identifier.doi10.1108/SR-02-2019-0051en_US
dc.identifier.endpage723en_US
dc.identifier.issn0260-2288
dc.identifier.issn1758-6828
dc.identifier.issue5en_US
dc.identifier.startpage716en_US
dc.identifier.urihttps://doi.org/10.1108/SR-02-2019-0051
dc.identifier.urihttps://hdl.handle.net/20.500.12684/3702
dc.identifier.volume39en_US
dc.identifier.wosWOS:000482447100011en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherEmerald Group Publishing Ltden_US
dc.relation.ispartofSensor Reviewen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMillingen_US
dc.subjectArtificial neural networken_US
dc.subjectSurface roughnessen_US
dc.subjectMultiple regressionen_US
dc.titleModeling for prediction of surface roughness in milling medium density fiberboard with a parallel roboten_US
dc.typeArticleen_US

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