Big Data in Materials Research and Development Summary of a Workshop (2014) / Chapter Skim
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Session 2: Big Data Issues in Materials Research and Development
Session 2: Big Data Issues in Materials Research and Development
Pages 23-40
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Davis made presentations in Session 2. PHYSICS IN BIG DATA Thom Mason, Director, Oak Ridge National Laboratory Dr.
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He pointed out that the Spallation Neutron Source is improving, and data volumes are growing at corresponding rates. In the past, the data volumes were handled via an individual researcher's analysis code.
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He then described several examples from the MDF: • Additive manufacturing of turbine blades. ORNL and its industrial partner, Morris Technologies, use neutron scattering to measure atomic plane spacing and better understand the link between residual stress and laser additive manufacturing processing.
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MATERIALS GENOME INITIATIVE AND BIG DATA Charles Ward, Lead for Integrated Computational Materials Science and Engineering, Air Force Research Laboratory Dr. Ward began his presentation by showing that the amount of informa tion related to materials has been increasing dramatically each year; in 2012, over 162,000 journal articles were published in the fields of materials science and engi neering.
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SOURCE: White House Materials Genome Initiative, http://www.whitehouse.gov/mgi/ goals. Accessed February 26, 2014.
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workshop on MGI data, held in May 2012. The workshop identified common themes to be addressed for materials data archiving, including the following: • Materials schema/ontology, • Data and metadata standards, • Data repositories/archives, 4 ALPHAD refers to the CALculation of PHAse Diagrams, a computational method of modeling C the thermodynamic properties of materials.
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. Some research disciplines, such as crystallography and evolutionary biology, have taken the initia tive by adopting their own data archiving policies.
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FIGURE B-2 An ICME system unifies materials information into a holistic system that is linked by means of a software integration tool to a designer knowledge base containing tools and models from other engineering disciplines. SOURCE: NRC, Integrated Computational Materials Engineering: A Transformational Dis cipline for Improved Competitiveness and National Security.
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A data citation index might also be useful. GE EFFORTS IN MATERIALS DATA: DEVELOPMENT OF THE ICME-NET Russell Irving, Services Technology Leader, GE Global Research Mr.
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SOURCE: Russell Irving, GE Global Research, presentation to the Metals Affordable Initiative Workshop on Data Management for Integrated Computational Materials Engineering, Slide 6.
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Users build small pieces of software to open the files and analyze the data one section at a time. The ICME Net application contains a graphical user interface to lay out work flows and show linkages.
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The users are materials scientists with data that need to be analyzed. SMART MANUFACTURING: ENTERPRISE RIGHT TIME, NETWORKED DATA, INFORMATION, AND ACTION Jim Davis, Vice Provost, Information Technology, and Chief Technology Officer, University of California Los Angeles Dr.
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Dr. Davis noted that while the Cheerios application may not qualify as big data, a lot of supporting data are involved in the manufacture of a Cheerio.
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Smart manufacturing intelligence is characterized by • Applications that can share data, data that can share applications, and ap plications that can connect to applications to achieve horizontal enterprise views and actions. • Orchestration of standardized decision work flows based on structured adaptation and autonomy.
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The app model allows for enhanced flexibility. A work flow schematic is shown in Figure 3.
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The apps themselves are not new, but the toolkits (work flows assembled to have a certain function) are the most valuable to the manufacturing community.
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There are no technical constraints, but the business models do not match well. 8 Kepler is a free, open source, scientific work flow tool.
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The materials community may not be as limited by computational technology as other fields are, though there are still unresolved issues in how to manage and analyze data.
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