Litcius/Paper detail

Advanced machine learning techniques for predicting wear performance in graphene oxide particulate interpenetrating polymer network composites

Eastus Russel, S. Madhu, S Judy, Edwin Geo Varuvel, Goroginam Santhi, G. Suresh, J.S. Femilda Josephin, Mohammed F. Albeshr, Farzad Kiani

2025Engineering Applications of Artificial Intelligence13 citationsDOIOpen Access PDF

Topics & Concepts

Materials scienceGrapheneMachine learningComposite materialDecision treeCoefficient of determinationComputer scienceArtificial intelligenceOxideMean squared errorSupervised learningWoven fabricAlgorithmPower (physics)Predictive powerRandom forestFiberNatural fiberParticulatesSupport vector machineLinear regressionComposite numberArtificial neural networkPolymerGlass fiberResponse surface methodologyTribology and Wear AnalysisConducting polymers and applicationsPolymer Nanocomposites and Properties
Advanced machine learning techniques for predicting wear performance in graphene oxide particulate interpenetrating polymer network composites | Litcius