Modelling of surface roughness performance of coated cemented carbide groove cutting tool via Artificial Neural Networks

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dc.contributor.authorPinar A.M.
dc.date.accessioned2024-07-22T08:20:21Z
dc.date.available2024-07-22T08:20:21Z
dc.date.issued2011
dc.description.abstractThe objective of the presented study is to model the effects of cutting speed, feed rate and depth of cut on the surface roughness (roughness average, Ra) in the turning process carried out by the grooving cutting tool by using Artificial Neural Network (ANN). To realize this aim, twenty seven specimens are machined at the cutting speeds of 100, 140 and 180m/min, feed rates of 0.05, 0.15 and 0.25mm/rev, and cutting depth of 0.6, 1.3 and 2mm in wet conditions. Data from these experiments are used in the training of ANN. When we compare the experimental results with the ANN ones, it is observed that proposed method is applied with an error rate of 8.14% successfully.
dc.identifier.issn21471762
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18113
dc.language.isoEnglish
dc.publisherGazi Universitesi
dc.subjectCarbide cutting tools
dc.subjectCarbides
dc.subjectCutting
dc.subjectNeural networks
dc.subjectTurning
dc.subjectCemented carbides
dc.subjectCutting speed
dc.subjectDepth of cut
dc.subjectFeedrate
dc.subjectGroove cutting tool
dc.subjectModeling
dc.subjectModeling of surface roughness
dc.subjectPerformance
dc.subjectRoughness averages
dc.subjectSpeed-fed
dc.subjectSurface roughness
dc.titleModelling of surface roughness performance of coated cemented carbide groove cutting tool via Artificial Neural Networks
dc.typeArticle

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