Berlin 2018 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 56: Topical Session (Symposium MM): Big Data in Materials Science - Managing and exploiting the raw material of the 21st century
MM 56.2: Talk
Thursday, March 15, 2018, 12:15–12:30, H 0107
Probabilistic neural network design of an alloy for direct laser deposition — Bryce Conduit1, Trevor Illston2, Divya Vadgadde Duggappa3, Scarlett Baker1, Steve Harding4, Howard Stone5, and •Gareth Conduit6 — 1Rolls-Royce plc, PO Box 31, Derby, DE24 8BJ, United Kingdom — 2Materials Solutions, Worcester, WR4 9GN, United Kingdom — 3Rolls-Royce plc, Bangalore, India — 4Rolls-Royce plc, PO Box 3, Bristol, BS34 7QE, United Kingdom — 5Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge, CB3 0FS, United Kingdom — 6Cavendish Laboratory, University of Cambridge, J.J. Thomson Avenue, Cambridge, CB3 0HE, United Kingdom
A neural network tool was used to discover and characterize the new nickel-base alloy for direct laser deposition most likely to simultaneously satisfy targets of processibility, cost, density, phase stability, creep resistance, oxidation, and resistance to thermal stresses. Experimental testing confirms that the physical properties of the proposed alloy exceed those of other commercially available Ni-base alloys for combustor liner applications.