Design of a nickel-base superalloy using a neural network
This work addresses the need for more efficient alloy design in materials science, though it is incremental as it applies an existing neural network method to a new domain.
The researchers tackled the problem of designing a new nickel-base superalloy by developing a neural network tool to optimize up to eleven physical criteria, resulting in an alloy with properties like oxidation resistance and yield stress that exceed existing commercial alloys.
A new computational tool has been developed to model, discover, and optimize new alloys that simultaneously satisfy up to eleven physical criteria. An artificial neural network is trained from pre-existing materials data that enables the prediction of individual material properties both as a function of composition and heat treatment routine, which allows it to optimize the material properties to search for the material with properties most likely to exceed a target criteria. We design a new polycrystalline nickel-base superalloy with the optimal combination of cost, density, gamma' phase content and solvus, phase stability, fatigue life, yield stress, ultimate tensile strength, stress rupture, oxidation resistance, and tensile elongation. Experimental data demonstrates that the proposed alloy fulfills the computational predictions, possessing multiple physical properties, particularly oxidation resistance and yield stress, that exceed existing commercially available alloys.