Journal Published Online: 11 December 2024
Volume 8, Issue 1

Hybrid Analytics in Smart Manufacturing Systems—Addressing the Manufacturing Data Challenge

CODEN: SSMSCY

Abstract

Smart manufacturing has opened tremendous opportunities to access, collect, and analyze a plethora of process and product data. Simultaneously, the manufacturing domain presents unique challenges regarding data for current modeling approaches—be it machine learning or physics-based models. In this paper, we highlight the opportunity presented by combining data-driven and physics-based models in a hybrid approach to address these data challenges. The paper provides a depiction of the unique data challenges in the manufacturing domain, illustrates the different facets of data analytics in manufacturing (including physics-based, data-driven, and hybrid modeling), and provides a qualitative mapping of fit for the different modeling classes on the data challenge dimensions.

Author Information

Wuest, Thorsten
Mechanical Engineering, Molinaroli College of Engineering and Computing, University of South Carolina, Columbia, SC, USA
Pages: 9
Price: Free
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Details
Stock #: SSMS20230015
ISSN: 2520-6478
DOI: 10.1520/SSMS20230015