dc.contributor | National Institute of Standards and Technology | en_US |
dc.contributor.author | Li, Shengyen | |
dc.contributor.author | Kattner, Ursula | |
dc.contributor.author | Campbell, Carelyn | |
dc.contributor.other | shengyen.li@nist.gov | en_US |
dc.date.accessioned | 2017-08-30T20:57:38Z | |
dc.date.available | 2017-08-30T20:57:38Z | |
dc.date.issued | 2017-08-18 | |
dc.identifier.citation | Shengyen Li, Ursula R. Kattner, Carelyn E. Campbell. A computational framework for material design. Integrating Materials and Manufacturing Innovation. 2017, Volume 6, Issue 3, pp 229–248. DOI: 10.1007/s40192-017-0101-8 | en_US |
dc.identifier.uri | http://hdl.handle.net/11256/946 | |
dc.description.abstract | A computational framework is proposed that enables the integration
of experimental and computational data, a variety of user-selected models, and a
computer algorithm to direct a design optimization. To demonstrate this framework a sample design of a ternary Ni-Al-Cr alloy with a high work-to-necking ratio
is presented. This design example illustrates how CALPHAD phase-based, composition and temperature dependent phase equilibria calculations and precipitation
models are coupled with models for elastic and plastic deformation to calculate the
stress-strain curves. A genetic algorithm then directs the search within a specific
set of composition and processing constrains for the ideal composition and processing profile to optimize the mechanical properties. The initial demonstration of
the framework provides a potential solution to initiate the material design process
in a large space of composition and processing conditions. This framework can also
be used in similar material systems or adapted for other material classes. | en_US |
dc.subject | Ni-based superalloy | en_US |
dc.subject | genetic algorithm | en_US |
dc.subject | CALPHAD | en_US |
dc.title | A computational framework for material design | en_US |
dc.type | Dataset | en_US |
dc.type | Evaluated Data | en_US |
dc.type | Macro/Script | en_US |
dc.type | Preprint | en_US |
cc.accessURL | | |