Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

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.
Proceedings Title
IEEE Big Data 2019
Conference Dates
December 9-12, 2019
Conference Location
Los Angeles, CA
Conference Title
5th International Workshop on Methodologies to Improve Managing Big Data projects in IEEE
big data


Data analytics, opportunity identification and prioritization, architecture, additive manufacturing


Park, H. , Ko, H. , Lee, Y. , Cho, H. and Witherell, P. (2019), A Framework for Identifying and Prioritizing Data Analytics Opportunities in Additive Manufacturing, IEEE Big Data 2019, Los Angeles, CA, [online], (Accessed April 14, 2024)
Created December 9, 2019, Updated March 10, 2020