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Collaborative knowledge management to identify data analytics opportunities in additive manufacturing

Author

Listed:
  • Hyunseop Park

    (National Institute of Standards and Technology (NIST)
    Pohang University of Science and Technology (POSTECH))

  • Hyunwoong Ko

    (National Institute of Standards and Technology (NIST)
    Arizona State University)

  • 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))

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.

Suggested Citation

  • Hyunseop Park & Hyunwoong Ko & Yung-tsun Tina Lee & Shaw Feng & Paul Witherell & Hyunbo Cho, 2023. "Collaborative knowledge management to identify data analytics opportunities in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 541-564, February.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01811-1
    DOI: 10.1007/s10845-021-01811-1
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    References listed on IDEAS

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    1. Salomé Sanchez & Divish Rengasamy & Christopher J. Hyde & Grazziela P. Figueredo & Benjamin Rothwell, 2021. "Machine learning to determine the main factors affecting creep rates in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2353-2373, December.
    2. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    3. v. Alberti-Alhtaybat, Larissa & Al-Htaybat, Khaldoon & Hutaibat, Khalid, 2019. "A knowledge management and sharing business model for dealing with disruption: The case of Aramex," Journal of Business Research, Elsevier, vol. 94(C), pages 400-407.
    4. Peng Wang & Zhouquan Zhu & Shuai Huang, 2017. "The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 229-243, January.
    5. William Mycroft & Mordechai Katzman & Samuel Tammas-Williams & Everth Hernandez-Nava & George Panoutsos & Iain Todd & Visakan Kadirkamanathan, 2020. "A data-driven approach for predicting printability in metal additive manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1769-1781, October.
    6. Leong, G. K. & Snyder, D. L. & Ward, P. T., 1990. "Research in the process and content of manufacturing strategy," Omega, Elsevier, vol. 18(2), pages 109-122.
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