Computational predictions of complex property trajectories in compositionally graded alloys

• CALPHAD-based ICME framework predicts property trajectories in IN718, MK500, and INV36 gradients, revealing nonlinear transitions. • Property variability along gradients is captured and linked to phase behavior. • Variance-based sensitivity analysis identifies key elements driving variability, enabling the control of variability via terminal alloy composition. Additive manufacturing enables net-shaped compositionally graded components that satisfy conflicting property requirements through spatial variations in alloy chemistry and microstructure. Although current path-planning methods for compositionally graded alloys emphasize avoiding deleterious phases, property evolution along compositional gradients is equally important because abrupt property changes can degrade structural integrity. In light of this concern, this study integrates high-throughput calculation of phase diagrams (CALPHAD)-based integrated computational materials science (ICME) simulations with variance-based global sensitivity analysis to introduce a framework for designing smoother property transitions. Thermophysical and mechanical properties along binary gradients between pairs of Inconel 718, Monel K-500, and Invar 36 were computed, revealing strongly nonlinear property transitions. Using the thermal expansion coefficient as an example, a sensitivity analysis identified aluminum as a key driver of variability in the properties along these transitions, and this variability can be reduced by tailoring the composition in the terminal alloys. This framework can be used for similar identification and variability tailoring for various properties for optimal component-level performance.