GJO-MLP: A NOVEL METHOD FOR HYBRID METAHEURISTICS MULTI-LAYER PERCEPTRON AND A NEW APPROACH FOR PREDICTION OF WEAR LOSS OF AZ91D MAGNESIUM ALLOY WORN AT DRY, OIL, AND h-BN NANOADDITIVE OIL

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dc.contributor.authorAltay O.
dc.contributor.authorGurgenc T.
dc.date.accessioned2024-07-22T08:01:22Z
dc.date.available2024-07-22T08:01:22Z
dc.date.issued2024
dc.description.abstractIn this study, the AZ91D magnesium alloy was worn at different wear conditions (dry, oil, and h-BN nanoadditive oil), loads (10–60 N), sliding speeds (50–150 mm/s) and sliding distances (100–1000 m). Wear losses increased with the increase of applied load, sliding speed, and sliding distance. Wear losses were decreased in the h-BN nanoadditive oil conditions. For the first time, the wear losses were predicted using the hybrid golden jackal optimizer-multi-layer perceptron (GJO-MLP) method proposed in this study, using the experimentally obtained data. In addition, the performance of the proposed method was compared with the whale optimization-MLP (WOA-MLP), genetic algorithm-MLP (GA-MLP) and ant lion optimization-MLP (ALO-MLP) methods, which are widely used in the literature. The results showed that GJO-MLP outperformed other methods with a performance of 0.9784 in R2 value. © World Scientific Publishing Company.
dc.identifier.DOI-ID10.1142/S0218625X24500483
dc.identifier.issn0218625X
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11419
dc.language.isoEnglish
dc.publisherWorld Scientific
dc.subjectBoron nitride
dc.subjectMagnesium alloys
dc.subjectMultilayer neural networks
dc.subjectWear of materials
dc.subject'Dry' [
dc.subjectAZ91D magnesium alloys
dc.subjectGolden jackal optimization-multi-layer perceptron
dc.subjectHybrid artificial neural network
dc.subjectMetaheuristic optimization
dc.subjectMultilayers perceptrons
dc.subjectOptimisations
dc.subjectOptimizers
dc.subjectWear condition
dc.subjectWear loss
dc.subjectGenetic algorithms
dc.titleGJO-MLP: A NOVEL METHOD FOR HYBRID METAHEURISTICS MULTI-LAYER PERCEPTRON AND A NEW APPROACH FOR PREDICTION OF WEAR LOSS OF AZ91D MAGNESIUM ALLOY WORN AT DRY, OIL, AND h-BN NANOADDITIVE OIL
dc.typeArticle

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