Artificial Neural Network-Based Multiobjective Optimization of Mechanical Alloying Process for Synthesizing of Metal Matrix Nanocomposite Powder

Abstract

The aim of this article was to optimize the mechanical alloying process for synthesizing of Al-8vol%SiC nanocomposite powders through an artificial neural network based on multiobjective optimization procedure. First, a suitable trained multi-layer perceptron (MLP) neural network was established for modeling purpose. Process variables as inputs of the network included milling time, milling speed, and balls to powders weight ratio. Parameters of the nanocomposite as outputs of the network were the crystallite size and the lattice strain of the aluminum matrix. The optimization was carried out by using two methods: gradient descent and pattern search. The aim of the optimization was to determine the minimum crystallite size and the maximum lattice strain of the aluminum matrix that could be obtained by regulating the mechanical alloying process variables. The response surfaces and the contour plots showed that the combination of the artificial neural network (ANN) and the optimization procedure were able to optimize the mechanical alloying process to synthesize Al-8vol%SiC nanocomposite.