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Research Papers

Toward Knowledge Management for Smart Manufacturing

[+] Author and Article Information
Shaw C. Feng

Systems Integration Division,
Engineering Laboratory,
National Institute of Standards and Technology,
100 Bureau Drive, MS 8260,
Gaithersburg, MD 20899
e-mail: shaw.feng@nist.gov

William Z. Bernstein

Systems Integration Division,
Engineering Laboratory,
National Institute of Standards and Technology,
100 Bureau Drive, MS 8260,
Gaithersburg, MD 20899
e-mail: william.bernstein@nist.gov

Thomas Hedberg, Jr.

Systems Integration Division,
Engineering Laboratory,
National Institute of Standards and Technology,
100 Bureau Drive, MS 8260,
Gaithersburg, MD 20899
e-mail: thomas.hedberg@nist.gov

Allison Barnard Feeney

Systems Integration Division,
Engineering Laboratory,
National Institute of Standards and Technology,
100 Bureau Drive, MS 8260,
Gaithersburg, MD 20899
e-mail: allison.barnardfeeney@nist.gov

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 August 22, 2016; final manuscript received June 20, 2017; published online July 24, 2017. Assoc. Editor: Yong Chen.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(3), 031016 (Jul 24, 2017) (9 pages) Paper No: JCISE-16-2051; doi: 10.1115/1.4037178 History: Received August 22, 2016; Revised June 20, 2017

The need for capturing knowledge in the digital form in design, process planning, production, and inspection has increasingly become an issue in manufacturing industries as the variety and complexity of product lifecycle applications increase. Both knowledge and data need to be well managed for quality assurance, lifecycle impact assessment, and design improvement. Some technical barriers exist today that inhibit industry from fully utilizing design, planning, processing, and inspection knowledge. The primary barrier is a lack of a well-accepted mechanism that enables users to integrate data and knowledge. This paper prescribes knowledge management to address a lack of mechanisms for integrating, sharing, and updating domain-specific knowledge in smart manufacturing (SM). Aspects of the knowledge constructs include conceptual design, detailed design, process planning, material property, production, and inspection. The main contribution of this paper is to provide a methodology on what knowledge manufacturing organizations access, update, and archive in the context of SM. The case study in this paper provides some example knowledge objects to enable SM.

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References

Figures

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Fig. 1

Levels in smart manufacturing knowledge management

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Fig. 2

Example of knowledge creation in the context of quality assurance

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Fig. 3

Knowledge engineering context

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Fig. 4

UML depiction of a smart manufacturing knowledge construct

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Fig. 5

Heat sink part: solid model of the test part used in the case study

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Fig. 6

Comparison of simulated data for part build generated by Mastercam compared to actual machine data. Note: The X-position of each dataset has been translated for ease of comparison. The vertical scales are consistent with both datasets.

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Fig. 7

Summary of data and information flow to create knowledge constructs relevant for producing the test part

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Fig. 8

Example of a posteriori knowledge

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