Title:
Process-structure linkages with materials knowledge systems

dc.contributor.advisor Kalidindi, Surya R.
dc.contributor.author Brough, David
dc.contributor.committeeMember Aluru, Srinivas
dc.contributor.committeeMember Garmestani, Hamid
dc.contributor.committeeMember Grover, Martha A.
dc.contributor.committeeMember Zha, Hongyuan
dc.contributor.department Computational Science and Engineering
dc.date.accessioned 2017-01-11T14:02:08Z
dc.date.available 2017-01-11T14:02:08Z
dc.date.created 2016-12
dc.date.issued 2016-09-09
dc.date.submitted December 2016
dc.date.updated 2017-01-11T14:02:08Z
dc.description.abstract The search for optimal manufacturing process routes that results in the combination of desired properties for any application is a highly dimensional optimization problem due to the hierarchical nature of material structure. Yet, this problem is a key component to materials design. Customized materials data analytics provides a new avenue of research in the efforts to address the challenge described above, while accounting for the inherently stochastic nature of the available data. The analytics mine and curate transferable, high value, materials knowledge at multiple length and time scales. More specifically, this materials knowledge is cast in the form of Process-Structure-Property (PSP) linkages of interest to the design/manufacturing experts. The extension of the novel Materials Knowledge Systems (MKS) framework to Process-Structure linkages holds the exciting potential to development full PSP linkages that can be can be leveraged by experts in data science, manufacturing and materials science and engineering communities. PSP linkages are an essential component in the to realize a modern accelerated materials innovation ecosystem. This work describes the methodologies used to extend the MKS framework to Process-Structure linkages and demonstrates their utility.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/56261
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Materials knowledge systems
dc.subject Multiscale simulations
dc.subject Machine learning
dc.subject Data sciences
dc.subject Phase field
dc.title Process-structure linkages with materials knowledge systems
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Kalidindi, Surya R.
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Computational Science and Engineering
relation.isAdvisorOfPublication e5ad79b6-4761-4f35-86c3-0890d432fe44
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication 01ab2ef1-c6da-49c9-be98-fbd1d840d2b1
thesis.degree.level Doctoral
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