Alloys-by-design is a term used to describe new alloy development techniques based on numerical simulation. These approaches are extensively used for nickel-base superalloys to increase the chance of success in alloy development. During alloy production of numerically optimized compositions, unavoidable scattering of the element concentrations occurs. In the present paper, we investigate the effect of this scatter on the alloy properties. In particular, we describe routes to identify alloy compositions by numerical simulations that are more robust than other compositions. In our previously developed alloy development program package MultOpt, we introduced a sensitivity parameter that represents the influence of alloying variations on the final alloy properties in the post-optimization process, because the established sensitivity calculations require high computational effort. In this work, we derive a regression-based model for calculating the sensitivity that only requires one-time calculation of the regression coefficients. The model can be applied to any function with nearly linear behavior within the uncertainty range. The model is then successfully applied to the computational alloys-by-design work flow to facilitate alloy selection using the sensitivity of a composition owing to the inaccuracies in the manufacturing process as an additional minimization goal.

MultOpt++ is a an alloy design tool based on numerical optimization. The concept is similar to approaches in the literature termed ‘Alloy by design’ and usually dealing with multi-objective optimization problem resulting in a Pareto front with a set of optimal compositions. Unpreventable scattering of element concentrations during the alloy production process causes property deviations of an optimal alloy composition resulting in unfeasible solutions. The violation fractions of such compositions should be taken into account during the optimization process to be able to determine optimal and feasible alloys. Established models for violation fractions require a high computational effort due to a high number of necessary calculations. In this work, we derive a fast model for the constraint violation fraction based on a regression analysis of the mean variation width of alloy properties. We apply this model to nickel-base superalloy properties predicted with the CALPHAD approach. The model allows to select alloys with a lower violation fraction of targeted constraints.

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