Abstract
An artificial-neural-network (ANN) model was developed to estimate the crystalline size of ZnO nanopowder as a function on the planetary milling parameters such as balls to powder ratio and rotation speed. This nanopowder was synthesized by 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). It was found that artificial neural network was very effective providing a perfect agreement between the outcomes of ANN modeling and experimental results. An optimization model is then developed to find the best milling parameters (rotation speed and balls to powder ratio) producing the minimal average crystalline size.