Optimizing chemistry of bulk metallic glasses for improved thermal stability

Thermo-mechanical-physical properties of bulk metallic glasses (BMGs) depend strongly on the concentrations of each of the chemical elements in a given alloy. The proposed methodology for simultaneously maximizing these multiple properties by accurately determining proper concentrations of each of the alloying elements is based on the use of computational algorithms rather than on traditional experimentation, expert experience, and intuition. Specifically, the proposed BMG design method combines an advanced stochastic multi-objective evolutionary optimization algorithm based on self-adapting response surface methodology and an existing data base of experimentally evaluated BMG properties. During the iterative computational design procedure, a relatively small number of new BMGs need to be manufactured and experimentally evaluated for their properties in order to continuously verify the accuracy of the entire design methodology. Concentrations of most important alloying elements can be predicted so that new BMGs have the best multiple properties. This design concept was verified for superalloys using strictly experimental data. The number of required experimental evaluations of the candidate BMGs with this optimization strategy is expected to be less than 150.