Experimental investigations on the solid lubricated wear behaviour of aluminium hybrid composites

Abstract

newline AlMg1SiCu alloy plays an important role in the fabrication of hybrid aluminium Metal Matrix Composites (MMCs) and it is widely utilized in all types of manufacturing and research fields owing to their high strength-to-weight ratio, high stiffness, good wear resistance and low coefficient of thermal expansion. AlMg1SiCu alloy could be reinforced with ceramics particles in order to improve its specific physical properties. newlineThe continuous phase used for the hybrid MMCs in the present work is aluminium alloy AlMg1SiCu. These cast alloys are mostly used in aerospace and structural applications due to their corrosion resistance and cast-ability. Silicon carbide (SiC) and Molybdenum disulfide (MoS2) particles were added into the matrix material to increase the hardness, wear resistance and lubricity. Silicon carbide has been used to improve the mechanical and tribological properties of aluminum and its alloys. AlMg1SiCu alloy hybrid composites reinforced with 10% Silicon carbide particles were used in this study (SiC). Addition of soft particulate reinforcements such as MoS2 or graphite with hard ceramic reinforcements, decreases coefficient of friction and improves the machining performance. In this study, AlMg1SiCu alloy hybrid reinforced with MoS2 were used. Stir casting method was chosen for the fabrication of the Aluminium hybrid composites. 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 al