Session: CIE-15/16: SEIKM Joint Topics

Paper Number: 116566

116566 - Knowledge Management for Data Analytics in Additive Manufacturing 

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.

Presenting Author: Yeun Park National Institute of Standards and Technology

Presenting Author Biography: Yeun Park works at NIST as an International Associate.
She is studying 'Industrial and Management Engineering' as a Ph.D. candidate at POSTECH, Republic of Korea.
Her research areas are 1) AI systems for productivity prediction in manufacturing, 2) recommendation systems with semi-structured data, 3) data quality assessment, and 4) knowledge management.
Recently, she is working on data analytics in additive manufacturing and knowledge management for data analytics.

Authors:

Yeun Park National Institute of Standards and Technology
Paul Witherell National Institute of Standards and Technology
Albert Jones National Institute of Standards and Technology
Hyunbo Cho Pohang University of Science and Technology