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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Date
2024
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Abstract
In 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.
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Boron nitride , Magnesium alloys , Multilayer neural networks , Wear of materials , 'Dry' [ , AZ91D magnesium alloys , Golden jackal optimization-multi-layer perceptron , Hybrid artificial neural network , Metaheuristic optimization , Multilayers perceptrons , Optimisations , Optimizers , Wear condition , Wear loss , Genetic algorithms