DIGITAL TWIN SIMULATION AND OPTIMIZATION OF MANUFACTURING PROCESS FLOWS

Hankang Lee, Hui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amount of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models - job flow graph and AGV traveling graph - to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughputs in a cost-effective way. This research is strongly promised to help SMMs fully utilize big data and DT technologies for gaining competitive advantages in the global market.

Original languageEnglish (US)
Title of host publicationManufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791887240
DOIs
StatePublished - 2023
EventASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023 - New Brunswick, United States
Duration: Jun 12 2023Jun 16 2023

Publication series

NameProceedings of ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023
Volume2

Conference

ConferenceASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023
Country/TerritoryUnited States
CityNew Brunswick
Period6/12/236/16/23

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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