Prediction of mechanical properties in magnesia based refractory materials using ANN

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dc.contributor.authorKoksal N.S.
dc.date.accessioned2024-07-22T08:21:26Z
dc.date.available2024-07-22T08:21:26Z
dc.date.issued2009
dc.description.abstractRefractory materials are heterogeneous materials having complex microstructures with different constituent's properties. The mechanical properties of these materials change depending on their chemical composition and temperature. Therefore, it is important to select a refractory material, which is suitable for working conditions and is fit to place of use. Artificial neural network (ANN) model is established to investigate the relationship among processing parameters (chemical composition, temperature) and mechanical properties (bending strength, Young's modulus) in magnesia based refractory materials. The mechanical properties of magnesia based refractory materials having four different chemical compositions were investigated using three point bending test at temperatures of 25, 400, 500, 600, 700, 800, 900, 1000 and 1400 °C. The bending strength (σ) and Young's modulus (E) were theoretically calculated by ANN method and theoretical results were compared with experimental values for each temperature. There were insignificant differences between experimental values and ANN results meaning that ANN results can be used instead of experimental values. Thus, mechanical properties of refractory materials having different chemical composition can be predicted by using ANN method regardless of the treatment temperature. © 2009 Elsevier B.V. All rights reserved.
dc.identifier.DOI-ID10.1016/j.commatsci.2009.06.018
dc.identifier.issn09270256
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18616
dc.language.isoEnglish
dc.subjectBackpropagation
dc.subjectBending strength
dc.subjectElastic moduli
dc.subjectElasticity
dc.subjectMagnesia
dc.subjectMagnesia refractories
dc.subjectMaterials properties
dc.subjectNeural networks
dc.subjectThermal shock
dc.subjectArtificial neural network models
dc.subjectChemical compositions
dc.subjectComplex microstructures
dc.subjectExperimental values
dc.subjectHeterogeneous materials
dc.subjectPrediction of mechanical properties
dc.subjectProcessing parameters
dc.subjectTheoretical result
dc.subjectThree-point bending test
dc.subjectTreatment temperature
dc.subjectWorking conditions
dc.subjectYoung's Modulus
dc.subjectMechanical properties
dc.titlePrediction of mechanical properties in magnesia based refractory materials using ANN
dc.typeArticle

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