A Framework for Identifying and Prioritizing Data Analytics Opportunities in Additive Manufacturing
Hyunseop Park, Hyunwoong Ko, Yung-Tsun T. Lee, Hyunbo Cho, Paul W. Witherell
Many industries, including manufacturing, are adopting data analytics (DA) in making decisions to improve quality, cost, and on-time delivery. In recent years, more research and development efforts have applied DA to additive manufacturing (AM) decision-making problems such as part design and process planning. Though there are many AM decision-making problems, not all benefit greatly from DA. This may be due to insufficient AM data, unreliable data quality, or the fact that DA is not cost effective when it is applied to some AM problems. This paper proposes a framework to investigate DA opportunities in a manufacturing operation, specifically AM. The proposed framework identifies and prioritized AM potential opportunities where DA can make impact. The proposed framework is presented in a five-tier architecture, including value, decision-making, data analytics, data, and data source tiers. A case study is developed to illustrate how the proposed framework identifies DA opportunities in AM.
IEEE Big Data 2019
December 9-12, 2019
Los Angeles, CA
5th International Workshop on Methodologies to Improve Managing Big Data projects in IEEE
, Ko, H.
, Lee, Y.
, Cho, H.
and Witherell, P.
A Framework for Identifying and Prioritizing Data Analytics Opportunities in Additive Manufacturing, IEEE Big Data 2019, Los Angeles, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=929049
(Accessed June 3, 2023)