Metal matrix composite is made of non-metallic reinforcements (usually ceramic) in metal matrices that are widely used in various industries, including aerospace and automotive. Two main components of metal matrix composite are the matrix (base metal) and the reinforcing particles that tend to increase the hardness of the workpart. The production and machining of such materials are hard and costly. However, due to their excellent mechanical properties such as high strength to weight ratio, high hardness and rigidity, corrosion resistance, abrasion resistance, and low thermal coefficient, their applications are still growing in various aspects. One major division of metal matrix composite is aluminum metal matrix composite with ceramics particulate reinforcement such as silicon carbide and alumina. According to review of literature, a low volume of information was found in terms of machinability of specific grades of aluminum composite (A356-10% silicon carbide) under various lubrication modes. Therefore, in the course of this study, several blocks of aluminum metal matrix composite (A356) reinforced with 10% silicon carbide elements were used under dry, minimum quantity lubrication and wet milling operation. The maximum flank wear, tool wear modes, as well as the average surface roughness were recorded and were subsequently studied as the machining performance attributes. The use of lubricants in both minimum quantity lubrication and wet modes led to reduced tool wear as compared with readings made under dry mode. However, under similar experimental conditions, no significant improvement was observed on the average surface roughness values.
COMPARISON OF PERFORMANCES OF FUZZY LOGIC WITH ECHOSTATE NEURAL NETWORK FOR DIAGNOSIS AND PROGNOSIS OF SINGLE POINT TOOL WEAR ESTIMATION USING HYBRID ALUMINIUM SILICON CARBIDE METAL MATRIX COMPOSITE