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 optimizing 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 multiobjective evolutionary optimization algorithm based on self-adapting response surface methodology and an existing database 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 the most important alloying elements can be predicted so that new BMGs have multiple properties optimized in a Pareto sense. This design concept was verified for superalloys using strictly experimental data. Thus, the key innovation here lies in arriving at the BMG compositions which will have the highest glass forming ability by utilizing an advanced multi-objective optimization algorithm while requiring a minimum number of BMGs to be manufactured and tested in order to verify the predicted performance of the predicted BMG compositions.