Design of a nickel-base superalloy using a neural network

TL;DR

This paper introduces a neural network-based computational tool for designing nickel-base superalloys that meet multiple physical criteria, successfully predicting and experimentally validating an alloy with superior properties.

Contribution

It presents a novel neural network approach to optimize complex alloy compositions considering multiple criteria simultaneously.

Findings

The neural network accurately predicts alloy properties based on composition and heat treatment.

The designed alloy exceeds existing alloys in oxidation resistance and yield stress.

Experimental validation confirms the computational predictions.

Abstract

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.