Parameter optimization of Al-SiC metal matrix composites produced using powder-based process

Aluminium-based metal matrix composites (MMC) are very popularly used in aircraft, automotive and armaments industry because of their high young´s modulus, specific strength and enhanced wear properties. It is to be noted that there are many methods available for the production of aluminium-based MMCs. The present paper aims at optimization of process parameters related to the powder metallurgy-based process of producing Al-SiC MMCs with the help of two non-traditional optimization algorithms, namely genetic algorithm (GA) and artificial bee colony (ABC) algorithms. It is important to note that the input process parameters related to the powder-metallurgy process, such as percentage of reinforcement, sintering temperature, compacting pressure and sintering time are considered as inputs and the properties of the composite produced, namely sintering density and micro-hardness are treated as outputs. The non-linear regression equations related to the sintering density and micro-hardness in terms of input process parameters have been developed after utilizing the experimental data available in the literature. The two objectives (that is, sintering density and micro-hardness) in this process are combined to form a single objective and the problem has been solved as a maximization problem with the help of GA and ABC. It has been observed that the optimal values of the input process parameters obtained by the two optimization algorithms are comparable.