Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Knowledge Management for Data Analytics in Additive Manufacturing

Published

Author(s)

Yeun Park, Paul Witherell, Albert T. Jones, Hyunbo Cho

Abstract

As a multi-staged digital manufacturing process, Additive manufacturing (AM) inherently benefits from data analytics (DA) decision-making opportunities. The abundance of data associated with the various observations and measurements taken throughout the design to product transformation creates ample opportunity for iterative process improvements. To best formulate and address these opportunities, knowledge needs to be strategically and deliberately managed for efficient DA development. However, knowledge in AM is broad and comparatively sparse, making it difficult to create robust DA solutions. Also, existing methods for knowledge management in AM are often case-dependent. To address such challenges, this paper proposes a novel framework to manage case-independent, knowledge for AM data analytics. The proposed framework consists of two phases: a knowledge-identification phase and a knowledge-representation phase. A knowledge architecture is defined to provide a reference for discovering knowledge that facilitates AM data analytics. In the knowledge identification phase, the architecture is used to facilitate the identification of knowledge relevant to specific DA use cases. In the knowledge representation phase, ontologies are used for representing and linking identified knowledge. A case study of application scenarios demonstrates how actionable knowledge is identified, represented, and managed by the framework. The framework enhances efficiency of AM data analytics development and enables knowledge sharing, understanding and reuse in AM data analytics activities.
Proceedings Title
International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
Conference Dates
August 20-23, 2023
Conference Location
Boston, MA, US

Keywords

Data Analytics, Knowledge Management, Knowledge Representation, Knowledge Identification, Additive Manufacturing

Citation

Park, Y. , Witherell, P. , Jones, A. and Cho, H. (2023), Knowledge Management for Data Analytics in Additive Manufacturing, International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Boston, MA, US, [online], https://doi.org/10.1115/DETC2023-116566, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936862 (Accessed June 21, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created November 21, 2023, Updated December 19, 2023