Application of a linearly decreasing weight particle swarm to optimize the process conditions of al matrix nanocomposites

In this paper, SiC nanoparticles were added into the commercial casting Al–Si aluminum alloy to fabricate metal matrix nanocomposites (MMNCs) with uniform reinforcement distribution. Experimental results revealed that the presence of nano-SiC reinforcement led to significant improvement in hardness and UTS while the ductility of the aluminum matrix is retained. An integrated optimization approach using an artificial neural network and a modified particle swarm is proposed to solve a process parameter design problem in casting. The artificial neural network is used to obtain the relationships between decision variables and the performance measures of interest, while the particle swarm is used to perform the optimization with multiple objectives. The results showed that the particle swarm is an effective method for solving multi-objective optimization problems, and that an integrated approach using an artificial neural network and a modified particle swarm can be used to solve complex process parameter design problems.