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Abstract The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts 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 throughput cost-effectively. This research is strongly promised to help SMMs fully utilize big data and DT technology for gaining competitive advantages in the global marketplace.
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This content will become publicly available on August 17, 2026
Hierarchical Blockchain for Mapping Manufacturing Process Flow
As manufacturing processes become increasingly complex, maintaining quality and improving efficiency requires mapping of process flows. Mapping process flows, in turn, depends on comprehensive end-to-end data traceability. Such traceability relies on lifecycle data that capture every stage, from raw-material handling to final-product assembly, and provide indispensable insights for process refinement. However, conventional centralized database-based systems for managing these data introduce single points of failure and remain vulnerable to tampering and cyberattacks. As a result, data traceability and authenticity are compromised. Therefore, this research develops a novel blockchain architecture coupled with digital twin (DT) model to secure end-to-end documentation of manufacturing process flows. First, a hierarchical blockchain framework is developed to record production events and ensure comprehensive, tamper-proof records of process activities. Second, the DT model, operating in collaboration with the blockchain tiers, enables real-time alignment between the manufacturing floor and its virtual twin. Third, a unified data representation is designed to transform diverse manufacturing datasets into a homogeneously structured format. Experimental results show that the proposed framework significantly enhances data authenticity while reducing the time required to map manufacturing process flows.
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- Award ID(s):
- 2302834
- PAR ID:
- 10639948
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2161-8089
- Page Range / eLocation ID:
- 2657 to 2662
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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