Date of Award
Summer 2025
Document Type
Open Access Dissertation
Department
Mechanical Engineering
First Advisor
Thorsten Wuest
Abstract
More than 50 years of sustained manufacturing decline in the United States has plagued the availability of quality goods and employment, thereby eroding the social health and quality of life of American families. Smart manufacturing and industry 4.0 aim to reverse this trend and revolutionize manufacturing by leveraging emerging technologies such as artificial intelligence and digital twins to improve output, productivity, and opportunity.
Digital twins have emerged as a key enabling technology for smart manufacturing and industry 4.0, garnering significant attention, prioritization, and investment. Despite their popularity, concrete implementations of digital twins are scarce and lack the key capabilities of prediction and control in combination. Leveraging artificial intelligence's predictive capability, this dissertation examines the barriers which inhibit the implementation of digital twins in smart manufacturing systems and contributes to laying a foundation for establishing predictive process control digital twins including their fundamental building blocks of data acquisition, testbed access, and time-series analytics.
In addition to providing technical analyses which enable researchers and practitioners to overcome system integration and testbed capability challenges, this dissertation posits that interoperability is an underestimated and pervasive challenge on the factory floor. Furthermore, this dissertation posits that interoperability inhibitors are as much social as they are technical, and the failure to achieve robust and widespread interoperability is due to an overemphasis on technical in lieu of social influencing factors. In addition, this dissertation identifies limited interdisciplinary collaboration and limited access to capable testbeds which provide streaming authentic manufacturing data as major inhibitors to emerging technology research and implementation. To overcome these inhibitors, this dissertation proposes a novel method for providing worldwide access to commissioned testbeds and achieving geographically-distributed collaboration. Furthermore, this dissertation demystifies time-series analytics in manufacturing and empirically evaluates classification and forecasting algorithms, providing practical guidance for algorithm selection and implementation. Finally, this dissertation evaluates time-series classification performance, industrial communication performance, and the tradeoffs between edge computing and computational offloading in the context of predictive process control, laying a foundation for the realization of predictive process control digital twins.
Through providing deep technical analyses of technologies, methods, solutions, and tradeoffs for practitioners and researchers coupled with educational resources and tools for educators and autodidacts, this dissertation aims to facilitate the implementation of emerging technologies on the factory floor and in research labs while expediting the upskilling and bootstrapping of the manufacturing workforce. Embracing the American spirit of innovation and invigorating the U.S. manufacturing workforce's potential to fashion dignity and prosperity, this dissertation aims to lay a foundation for addressing critical social and technical manufacturing challenges in order to furnish the quality goods and employment needed to improve the social health and quality of life for American families.
Rights
© 2025, Matthew Richard McCormick
Recommended Citation
McCormick, M. R.(2025). A Foundation for Predictive Process Control Digital Twins in Smart Manufacturing and Industry 4.0. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/8508