Evolution of the size distribution of Al–B4C nano-composite powders during mechanical milling: a comparison of experimental results with artificial neural networks and multiple linear regression models

Abstract

In the present study, two three-layer feed-forward artificial neural networks (ANNs) and multiple linear regression (MLR) models were developed for modeling the effects of material and process parameters on the powder particle size characteristics generated during high-energy ball milling of Al and B4C powders. The investigated process parameters included aluminum particle size, B4C size and its content as well as milling time. The median particle size (D 50) and the extent of size distribution (D 90–D 10) were considered as target values for modeling. The developed ANN and MLR models could reasonably predict the experimentally determined characteristics of powders during mechanical milling.