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Research Papers

Toward a Lifecycle Information Framework and Technology in Manufacturing

[+] Author and Article Information
Thomas Hedberg, Jr.

Systems Integration Division,
Engineering Laboratory,
National Institute of Standards and Technology,
Gaithersburg, MD 20899
e-mail: tdh1@nist.gov

Allison Barnard Feeney, Moneer Helu

Systems Integration Division,
Engineering Laboratory,
National Institute of Standards and Technology,
Gaithersburg, MD 20899

Jaime A. Camelio

Grado Department of Industrial
and Systems Engineering,
Virginia Tech,
Blacksburg, VA 24061

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received June 9, 2016; final manuscript received June 16, 2016; published online February 16, 2017. Editor: Bahram Ravani.This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Comput. Inf. Sci. Eng 17(2), 021010 (Feb 16, 2017) (13 pages) Paper No: JCISE-16-1981; doi: 10.1115/1.4034132 History: Received June 09, 2016; Revised June 16, 2016

Industry has been chasing the dream of integrating and linking data across the product lifecycle and enterprises for decades. However, industry has been challenged by the fact that the context in which data are used varies based on the function/role in the product lifecycle that is interacting with the data. Holistically, the data across the product lifecycle must be considered an unstructured data set because multiple data repositories and domain-specific schema exist in each phase of the lifecycle. This paper explores a concept called the lifecycle information framework and technology (LIFT). LIFT is a conceptual framework for lifecycle information management and the integration of emerging and existing technologies, which together form the basis of a research agenda for dynamic information modeling in support of digital-data curation and reuse in manufacturing. This paper provides a discussion of the existing technologies and activities that the LIFT concept leverages. Also, the paper describes the motivation for applying such work to the domain of manufacturing. Then, the LIFT concept is discussed in detail, while underlying technologies are further examined and a use case is detailed. Lastly, potential impacts are explored.

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Copyright © 2017 by ASME
Topics: Manufacturing , Design
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References

Figures

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Fig. 1

Essential supporting structure of the LIFT concept and the relationship of the three layers that make up the framework

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Fig. 2

Schematic overview of the LIFT concept

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Fig. 3

Example common elements model formed by linking domain-specific information models

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Fig. 4

Example data flow for engineering change requests to design and automated/dynamic scheduling to manufacturing. Each dotted line represents a different abstraction layer. Here, we illustrate how data exchange manifests itself. First, as data are collected out of the process layer, context is added to the data layer to generate the information layer. Then, knowledge is built upon the information layer to support decision-making. In this case, deciding when an engineering-change request is required or to determine dynamic scheduling within manufacturing.

Grahic Jump Location
Fig. 5

Example process for automated engineering change requests to show how data are first aggregated together, statistical anomalies detected, engineering-design problems discovered, and engineering changes requests generated

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Fig. 6

Single-loop learning versus double-loop learning—derived from Ref. [68]

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