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Displaying 51 - 75 of 88

Incorporating unit manufacturing process models into life cycle assessment workflows

May 5, 2019
Author(s)
William Z. Bernstein, Cesar D. Tamayo, David Lechevalier, Michael Brundage
Life cycle assessment (LCA) carries significant uncertainties and imprecision due to a number of factors, including the framework's linearity assumptions and the wide use of aggregate unit processes in practice. The goal of this work is to exploit the unit

2018 NIST/OAGi Workshop: Enabling Composable Service-Oriented Manufacturing Systems

April 22, 2019
Author(s)
Nenad Ivezic, Boonserm Kulvatunyou, Michael P. Brundage, Yan Lu, Evan K. Wallace, Albert W. Jones
This report summarizes the results from the 2018 NIST/OAGi Workshop: Enabling Composable Service-Oriented Manufacturing Systems, which was held at the National Institute of Standards and Technology campus in Gaithersburg, MD, on April 23-24, 2018. This was

Standards Needs for Maintenance Work Order Analysis in Manufacturing

April 3, 2019
Author(s)
Thurston Sexton, Michael Brundage
To bolster the efficiency and performance of maintenance work in manufacturing-maintenance being one of the key components to ensuring successful long-term operations-it is becoming increasingly necessary to ensure that maintenance operations are capable

Benchmarking for keyword extraction methodologies in maintenance work orders

September 24, 2018
Author(s)
Thurston B. Sexton, Michael P. Brundage, Melinda Hodkiewicz, Thomas Smoker
Maintenance has largely remained a human-knowledge centered activity, with the primary records of maintenance activity being text-based maintenance work orders (MWOs) that attempt to encode the diagnostic processes of technicians. However, the bulk of

Hybrid Datafication of Maintenance Logs from AI-Assisted Human Tags

January 15, 2018
Author(s)
Thurston B. Sexton, Michael P. Brundage, Michael L. Hoffman, Katherine C. Morris
One of the main challenges of applying AI to some datasets derives from the datasets themselves being unstructured, unclear, and ambiguous. Furthermore, the insights that are to be gained reflect the quality of the data itself; if the data is skewed, so