https://orcid.org/0000-0001-7457-7927
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.
Issue Section:
Technical Note
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