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

Altay O.Gurgenc T.2024-07-222024-07-2220240218625Xhttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11419In 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.EnglishBoron nitrideMagnesium alloysMultilayer neural networksWear of materials'Dry' [AZ91D magnesium alloysGolden jackal optimization-multi-layer perceptronHybrid artificial neural networkMetaheuristic optimizationMultilayers perceptronsOptimisationsOptimizersWear conditionWear lossGenetic algorithmsGJO-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 OILArticle10.1142/S0218625X24500483