Knowledge Management for Data Analytics in Additive Manufacturing

Published

November 21, 2023

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

Pub Type

Conferences

Download Paper

https://doi.org/10.1115/DETC2023-116566

Local Download

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 April 28, 2024)