The best conditions for minimizing the synthesis time of nanocomposites during high energy ball milling: Modeling and optimizing

Abstract

The present study is an accurate estimate of the milling parameters in order to maximize the energy transferred to the synthesized nanopowders. The maximum energy represents the leading stage for minimizing the synthesis time of nanocomposites during high energy ball milling. Accordingly, 271 dataset were collected from the literature and then by a modeling algorithm called gene expression programing (GEP), a mathematical relation between the grain size and milling parameters is developed. Afterwards by an optimization algorithm called Artificial Bee Colony (ABC), the milling parameters including amount of reinforcement, type and amount of process control agent (PCA), type of mill, type of vial, type of ball, vial spinning rate, BPR, milling atmosphere and milling time were optimized in order to achieve minimum grain size. Minimizing the mean grain size is equal to maximize the energy transferred to the nanopowders during high energy ball milling. Experiments were performed at the optimized parameters to proof the validity of the analysis. Given the broad range of the parameters used, it was found that our analysis and model is fully functional to accurately estimate the optimal conditions for ball milling experiments which shows the potential application of these calculations and analysis in materials science and engineering.