Abstract

Given recent advancements in technology and recognizing the evolution of smart manufacturing, the implementation of digital twins for factories and processes is becoming more common and more useful. Additionally, expansion in connectivity, growth in data storage, and the implementation of the Industrial Internet of Things (IIoT) allow for greater opportunities not only with digital twins but closed loop analytics. Discrete Event Simulation (DES) has been used to create digital twins and in some instances fitted with live connections to closely monitor factory operations. However, the benefits of a connected digital twin are not easily quantified. Therefore, a test bed demonstration factory was used, which implements smart technologies, to evaluate the effectiveness of a closed-loop digital twin in identifying and reacting to trends in production. This involves a digital twin of a factory process using DES. Although traditional DES is typically modeled using historical data, a DES system was developed which made use of live data with embedded machine learning to improve predictions. This model had live data updated directly to the DES model without user interaction, creating an adaptive and dynamic model. It was found that this DES with machine learning capabilities typically provided more accurate predictions of future performance and unforeseen near future problems when compared to the predictions of a traditional DES using only historic data

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2021-08-06

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd11880

Keywords

digital twin, discrete event simulation, real-time factory analytics, closed-loop processes, smart manufacturing

Language

english

Included in

Engineering Commons

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