Ball milling process variables optimization for high-entropy alloy development using design of experiment and genetic algorithm

Abstract

Besides developing new high-entropy alloys (HEAs) from vast compositional space, developing HEA powders with good control of grain- and- particle sizes (GPS) for improved flowability/sinterability in additive-manufacturing and sintering processes are of growing interest. Using a mechanical alloying process, the effect of parameters—process control agent (PCA) type and amount, and milling speed and time—on solid-solution onset and GPS of a model HEA (AlCoCrFe) is systematically investigated. The optimum PCA-type is first experimentally determined; followed by: a Taguchi design of experiment to plan and study the effect of parameters on the response variables—GPS, regression model to develop predictive process models, and model multi-objective optimization using genetic algorithm. Milling speed and PCA amount have the most effect on the grain- and particle- sizes, respectively. Two mathematical models for predicting mechanically-alloyed HEA GPS are developed. This work develops a framework for appropriate processing parameter selection for HEA powder development.