Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Smart and Sustainable Manufacturing Systems

DOI: 10.1520/ssms20230015

Author: Thorsten Wuest

Institution: University of South Carolina

Language: English

Topic: Digital Transformation in Industry

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funder: National Science Foundation