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Prediction of Dry Sliding Wear Response of AlMg1SiCu/Silicon Carbide/Molybdenum Disulphide Hybrid Composites Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Response Surface Methodology (RSM)

K. Ragupathy, C. Velmurugan, D. S. Ebenezer Jacob Dhas, N. Senthilkumar, K. Leo Dev Wins

2021Arabian Journal for Science and Engineering36 citationsDOI

Topics & Concepts

Materials scienceComposite materialSilicon carbideComposite numberResponse surface methodologyLubricantScanning electron microscopeAdaptive neuro fuzzy inference systemTribometerMolybdenumAlloyUltimate tensile strengthDry lubricantTribologyMetallurgyFuzzy logicFuzzy control systemComputer scienceMachine learningArtificial intelligenceAluminum Alloys Composites PropertiesTribology and Wear AnalysisAdvanced materials and composites
Prediction of Dry Sliding Wear Response of AlMg1SiCu/Silicon Carbide/Molybdenum Disulphide Hybrid Composites Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Response Surface Methodology (RSM) | Litcius