Abstract
An artificial-neural-network (ANN) model was developed to estimate the crystalline size of ZnO nanopowder as a function on the milling parameters such as milling times and balls to powder ratio. This nanopowder was synthesized by high energy mechanical milling and the required data for training were collected from the experimental results. The synthesized ZnO nanoparticles are characterized by X-ray diffraction (XRD) and scanning electron microcopy (SEM). It was found that artificial neural network was very effective providing a perfect agreement between the outcomes of ANN modeling and experimental results with an error by far better than multiple linear regressions. An optimization model and this experimental validation of the ball milling process for producing the nanopowder ZnO are carried out.