ANFIS-BASED WEAR PREDICTION MODEL FOR ALUMINIUM HYBRID COMPOSITES
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AlMg1SiCu alloy hybrid composites which were reinforced with 10% Silicon carbide particles (SiC) together with weight fractions of 3%, 6% and 9% of self-lubricant Molybdenum disulfide particles (MoS2) through melt-stir casting. The wear behaviour of the hybrid composite samples was evaluated based on Box-Behnken Design (BBD) on pin-on-disc tribometer without lubrication.The mathematical model was developed to predict dry sliding weight loss of hybrid composites using Response surface methodology. Statistical analysis were performed using Analysis of variance (ANOVA). The output response weight...
AlMg1SiCu alloy hybrid composites which were reinforced with 10% Silicon carbide particles (SiC) together with weight fractions of 3%, 6% and 9% of self-lubricant Molybdenum disulfide particles (MoS2) through melt-stir casting. The wear behaviour of the hybrid composite samples was evaluated based on Box-Behnken Design (BBD) on pin-on-disc tribometer without lubrication.The mathematical model was developed to predict dry sliding weight loss of hybrid composites using Response surface methodology. Statistical analysis were performed using Analysis of variance (ANOVA). The output response weight loss was employed to train the neural network model in ANFIS back-propagation algorithm.
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