MONITORING AND NEURAL NETWORK MODELING OF CUTTING TEMPERATURE DURING TURNING HARD STEEL

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dc.citation.epage2614
dc.citation.spage2605
dc.citation.volume22
dc.contributor.authorTARIĆ, Mirfad R.
dc.contributor.authorKOVAČ, Pavel P.
dc.contributor.authorNEDIĆ, Bogdan P.
dc.contributor.authorRODIĆ, Dragan Dj.
dc.contributor.authorJEŠIĆ, Dušan D.
dc.date.accessioned2023-08-17T09:59:59Z
dc.date.available2023-08-17T09:59:59Z
dc.date.issued2018
dc.description.abstractIn this study, cutting tools average temperature was investigated by using thermal imaging camera of FLIR E50-type. CBN inserts with zero and negative rake angles were taken as cutting tools and round bar of EN 90MnCrV8 hardened steel was used as the workpiece. Since the life of the cutting tool material strongly depends upon cutting temperature, it is important to predict heat generation in the tool with intelligent techniques. This paper proposes a method for the identification of cutting parameters using neural network. The model for determining the cutting temperature of hard steel, was trained and tested by using the experimental data. The test results showed that the proposed neural network model can be used successfully for machinability data selection. The effect on the cutting temperature of machining parameters and their interactions in machining were analyzed in detail and presented in this study. Key words: cutting temperature, turning, hard steel, neural network
dc.identifier.doi10.2298/TSCI170606210T
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/525
dc.language.isoen
dc.publisherVinča Nuclear Research Institute, Belgrade, Srbija
dc.sourceThermal Science
dc.subjectcutting temperature, turning, hard steel, neural network
dc.titleMONITORING AND NEURAL NETWORK MODELING OF CUTTING TEMPERATURE DURING TURNING HARD STEEL
dc.typeArticle
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Машински факултет [Научни радови] / Faculty of Mechanical Engineering [Scientific papers]