Collaborative knowledge management to identify data analytics opportunities in additive manufacturing

Hyunseop Park (), Hyunwoong Ko (), Yung-tsun Tina Lee (), Shaw Feng (), Paul Witherell () and Hyunbo Cho ()
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Hyunseop Park: National Institute of Standards and Technology (NIST)
Hyunwoong Ko: National Institute of Standards and Technology (NIST)
Yung-tsun Tina Lee: National Institute of Standards and Technology (NIST)
Shaw Feng: National Institute of Standards and Technology (NIST)
Paul Witherell: National Institute of Standards and Technology (NIST)
Hyunbo Cho: Pohang University of Science and Technology (POSTECH)

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 2, No 9, 564 pages

Abstract: Abstract Additive Manufacturing (AM) is becoming data-intensive. The ability to identify Data Analytics (DA) opportunities for effective use of AM data becomes a critical factor in the success of AM. To successfully identify high-potential DA opportunities in AM requires a set of distinctive interdisciplinary knowledge. This paper proposes a methodology that enables collaborative knowledge management for identifying and prioritizing DA opportunities in AM. The framework of the proposed methodology has three components: a team of experts, a DA Opportunity Knowledge Base (DOKB), and a prioritization tool. The team of experts provides diverse knowledge that can be used to identify and prioritize DA opportunities. The DOKB, developed by using the Web Ontology Language (OWL), captures diverse knowledge from the experts to identify DA opportunities. The prioritization tool ranks the identified DA opportunities by using the Fuzzy integrated Technique of Order Preference Similarity to the Ideal Solution (Fuzzy-TOPSIS). A case study, in which National Institute of Standards and Technology (NIST) researchers participated, demonstrates our methodology. As a result, 264 DA opportunities for AM’s Laser-Powder Bed Fusion (L-PBF) process are identified and prioritized. The prioritized DA opportunities help set a DA direction for L-PBF AM. Our methodology keeps knowledge sharable, reusable, revisable, and extendable. Thus, this methodology can continue to facilitate collaboration within the AM community to identify high potential and high impact DA opportunities in AM.

Keywords: Additive manufacturing; Big data; Data analytics; Data-driven decision support; Knowledge-based system; Multiple criteria decision-making (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-021-01811-1

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