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

Abstract

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 powdermetallurgy 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.

Performance Assessment of Different Sustainable Energy Systems Using Multiple-Criteria Decision-Making Model and Self-Organizing Maps

“…They addressed a singleobjective problem using GA and ABC, treating it as a maximization challenge. Both optimization algorithms for the input process parameters [24].…”

“…Five criteria are used to find optimal power plants, including availability of energy resources, efficiency, greenhouse gas emissions, energy generation, and cost, in order to achieve sustainable power generation. The categorical values assigned to every power plant on certain criteria are very high, high, between high and medium, medium, between medium and low, and low [24,27]. The power plant prediction depends on the results obtained from multiple-criteria decision analysis (MCDA).…”