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Progress toward autonomous experimental systems for alloy development

Published online by Cambridge University Press:  09 April 2019

Brad L. Boyce  and
Michael D. Uchic
Brad L. Boyce
Affiliation:
Materials, Physical, and Chemical Sciences Center, Sandia National Laboratories, USA; blboyce@sandia.gov
Michael D. Uchic
Affiliation:
Air Force Research Laboratory, Materials & Manufacturing Directorate, Wright-Patterson Air Force Base, USA; Michael.Uchic@wpafb.af.mil
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Abstract

Historically, the advent of robotics has important roots in metallurgy. The first industrial robot, Unimate, was used by General Motors to handle hot metal—transporting die castings and welding them to an automotive body. Now, nearly 60 years later, metallurgical use of robotics is still largely confined to automation of dangerous, complex, and repetitive tasks. Beyond metallurgy, the field of autonomy is undergoing a renaissance, impacting applications from pharmaceuticals to transportation. In this article, we review the emerging elements of high-throughput experimental automation, which, when combined with artificial intelligence or machine-learning systems, will enable autonomous discovery of novel alloys and process routes.

Keywords

alloymicrostructure
Type
Computational Design And Development Of Alloys
Information
MRS Bulletin , Volume 44 , Issue 4: Computational design and development of alloys , April 2019 , pp. 273 - 280
Copyright
Copyright © Materials Research Society 2019 

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