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This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations. Design/methodology/approachThis study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques. FindingsA comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions. Originality/valueBased on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.
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A Unified Digital Twin Framework for Real-time Monitoring and Evaluation of Smart Manufacturing Systems
Digital Twin (DT) is one of the key enabling technologies for realizing the promise of Smart Manufacturing (SM) and Industry 4.0 to improve production systems operation. Driven by the generation and analysis of high volume data coming from interconnected cyber and physical spaces, DTs are real-time digital images of physical systems, processes or products that help evaluate and improve business performance. This paper proposes a novel DT architecture for the real- time monitoring and evaluation of large-scale SM systems. An application to a manufacturing flow-shop is presented to illustrate the usefulness of the proposed methodology.
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- Award ID(s):
- 1544901
- PAR ID:
- 10104293
- Date Published:
- Journal Name:
- IEEE International Conference on Automation Science and Engineering (CASE)
- ISSN:
- 2161-8070
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper proposes a conceptual architecture of digital twin with human-in-the-loop-based smart manufacturing (DH-SM). Our proposed architecture integrates cyber-physical systems with human spaces, where artificial intelligence and human cognition are employed jointly to make informed decisions. This will enable real-time, collaborative decision-making between humans, software, and machines. For example, when evaluating a new product design, information about the product’s physical features, manufacturing requirements, and customer demands must be processed concurrently. Moreover, the DH-SM architecture enables the creation of an immersive environment that allows customers to be effectively involved in the manufacturing process. The DH-SM architecture is well fitted to those relatively new manufacturing processes, such as metal additive manufacturing, since they can benefit from using digital twins, data analytics, and artificial intelligence for monitoring and controlling those processes to support non-contact manufacturing. The proposed DH-SM will enable manufacturers to leverage the existing cyber-physical system and extended reality technologies to generate immersive experiences for end users, operators, managers, and stakeholders. A use case of wire + arc additive manufacturing is discussed to demonstrate the applicability of the proposed architecture. Relevant development and implementation challenges are also discussed.more » « less
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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.more » « less
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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.more » « less
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Accepted Manuscript1.0
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- The DOI is not currently available.
