Design of a nickel-base superalloy using a neural network. (5th October 2017)

• Record Type: Journal Article
• Authors: Conduit, B.D.; Jones, N.G.; Stone, H.J.; Conduit, G.J.
• Is Part Of: Materials & design. Volume 131(2017)
• Page Start: 358
• Page End: 365
• Publication Date: 2017-10-05
• Subjects: Neural network -- Materials design -- Nickel-base superalloy; Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2017.06.007
• Languages: English
• ISSNs: 0264-1275

Abstract

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, γ′ 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. Graphical Abstract: Highlights: Develop a neural network tool that can design new materials Design the composition and heat treatments to optimize eleven physical properties of a nickel-base superalloy Experimentally verify that the proposed nickel-base superalloy fulfills the predicted properties