Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network

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dc.contributor.authorSamtaş, Gürcan
dc.date.accessioned2020-04-30T23:19:08Z
dc.date.available2020-04-30T23:19:08Z
dc.date.issued2014
dc.departmentDÜ, Mühendislik Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.descriptionWOS: 000338031500029en_US
dc.description.abstractThe traditional devices, used to measure the surface roughness, are very sensitive, and they are obtained by scratching the surface of materials. Therefore, the optic systems are used as alternatives to these devices to avoid the unwanted processes that damage the surface. In this study, face milling process was applied to American Iron and Steel Institute (AISI) 1040 carbon steel and aluminium alloy 5083 materials using the different tools, cutting speeds and depth of cuts. After these processes, surface roughness values were obtained by the surface roughness tester, and the machined surface images were taken using a polarise microscope. The obtained images were converted into binary images, and the images were used as input data to train network using the MATLAB neural network toolbox. For the training networks, log-sigmoid function was selected as transfer function, scaled conjugate gradient (SCG) algorithm was used as training algorithm, and performance of the trained networks was achieved as an average of 99.926 % for aluminium alloy (AA) 5083 aluminium and as an average of 99.932 % for AISI 1040 steel. At the end of the study, a prediction programme for optical surface roughness values using MATLAB m-file and GUI programming was developed. Then, the prediction programme and neural network performance were tested by the trial experiments. After the trial experiments, surface roughness values obtained with stylus technique for the carbon steel and aluminium alloy materials were compared with the developed programme values. When the developed programme values were compared with the experimental results, the results were confirmed each other at a rate of 99.999 %.en_US
dc.description.sponsorshipDuzce University Research FundDuzce University [2012.06.06.120]; Department of Scientific Research Projects, Duzce University; Duzce, TurkeyDuzce Universityen_US
dc.description.sponsorshipThis project is supported by Duzce University Research Fund Project Number 2012.06.06.120. The author would like to thank the Department of Scientific Research Projects, Duzce University; Duzce, Turkey, for financially supporting this research.en_US
dc.identifier.doi10.1007/s00170-014-5828-1en_US
dc.identifier.endpage364en_US
dc.identifier.issn0268-3768
dc.identifier.issn1433-3015
dc.identifier.issue01.Apren_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage353en_US
dc.identifier.urihttps://doi.org/10.1007/s00170-014-5828-1
dc.identifier.urihttps://hdl.handle.net/20.500.12684/3659
dc.identifier.volume73en_US
dc.identifier.wosWOS:000338031500029en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofInternational Journal Of Advanced Manufacturing Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSurface roughness predictionen_US
dc.subjectOptical techniqueen_US
dc.subjectStylus techniqueen_US
dc.subjectImage analysisen_US
dc.titleMeasurement and evaluation of surface roughness based on optic system using image processing and artificial neural networken_US
dc.typeArticleen_US

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