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1. A state-of-the-art digital factory integrating digital twin for laser additive and subtractive manufacturing processes

   https://doi.org/10.1108/RPJ-03-2023-0113  

   Tariq, Usman; Joy, Ranjit; Wu, Sung-Heng; Mahmood, Muhammad Arif; Malik, Asad Waqar; Liou, Frank (August 2023, Rapid Prototyping Journal)

   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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2. AI-Powered urban transportation digital twin: Methods and applications,

   Fu, Y; Turkcan, M; Ghasemi, M; Mo, Z; Zang, C; Adhikari, A; Kostic, Z; Zussman, G; Di, X (December 2024, arXiv:2501.10396 [eess.SY])

   We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its “eyes,” which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its “brain,” the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add value to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency highbandwidth sensing and networking technologies, in other words, cyberphysical systems (CPS). We will first review the DT pipeline enabled by CPS and propose our DT architecture deployed on a real-world testbed in New York City. This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications. 

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3. Developing campus digital twin using interactive visual analytics approach

   https://doi.org/10.1007/s44243-024-00033-2  

   Ye, Xinyue; Jamonnak, Suphanut; Van Zandt, Shannon; Newman, Galen; Suermann, Patrick (April 2024, Frontiers of Urban and Rural Planning)

   Abstract Digital Twins (DTs) are increasingly recognized for their potential to improve efficiency and decision-making in various domains of the built environment. Despite their promise, challenges like cost, complexity, interoperability, and data integration remain. This paper introduces a novel interactive visual analytics system that tackles these issues, using a case study of simulating class distribution and campus building capacity at a large public university. The system leverages enrollment data, converting it into a spatial-temporal format for interactive exploration and analysis of class distribution and resource utilization. Through case studies, we demonstrate the system's effectiveness, adaptability, and real-world applicability, highlighting its role in practical DT implementation for built environments. 

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4. Digital Twinning and Optimization of Manufacturing Process Flows

   https://doi.org/10.1115/1.4063234  

   Lee, Hankang; Yang, Hui (November 2023, Journal of Manufacturing Science and Engineering)

   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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5. Trustworthy and Efficient Digital Twins in Post-Quantum Era with Hybrid Hardware-Assisted Signatures

   https://doi.org/10.1145/3638250  

   Nouma, Saif E.; Yavuz, Attila A. (December 2023, ACM Transactions on Multimedia Computing, Communications, and Applications)

   Digital Twins (DT) virtually model cyber-physical objects via sensory inputs by simulating or monitoring their behavior. Therefore, DTs usually harbor vast quantities of Internet of Things (IoT) components (e.g., sensors) that gather, process, and offload sensitive information (e.g., healthcare) to the cloud. It is imperative to ensure the trustworthiness of such sensitive information with long-term and compromise-resilient security guarantees. Digital signatures provide scalable authentication and integrity with non-repudiation and are vital tools for DTs. Post-quantum cryptography (PQC) and forward-secure signatures are two fundamental tools to offer long-term security and breach resiliency. However, NIST-PQC signature standards are exorbitantly costly for embedded DT components and are infeasible when forward-security is also considered. Moreover, NIST-PQC signatures do not admit aggregation, which is a highly desirable feature to mitigate the heavy storage and transmission burden in DTs. Finally, NIST recommends hybrid PQ solutions to enable cryptographic agility and transitional security. Yet, there is a significant gap in the state of the art in the achievement of all these advanced features simultaneously. Therefore, there is a significant need for lightweight digital signatures that offer compromise resiliency and compactness while permitting transitional security into the PQ era for DTs. We create a series of highly lightweight digital signatures called Hardware-ASisted Efficient Signature (HASES) that meets the above requirements. The core ofHASES is a hardware-assisted cryptographic commitment construct oracle (CCO) that permits verifiers to obtain expensive commitments without signer interaction. We created threeHASES schemes:PQ-HASES is a forward-secure PQ signature,LA-HASES is an efficient aggregate Elliptic-Curve signature, andHY-HASES is a novel hybrid scheme that combinesPQ-HASES andLA-HASES with novel strong nesting and sequential aggregation.HASES does not require a secure-hardware on the signer. We prove thatHASES schemes are secure and implemented them on commodity hardware and and 8-bit AVR ATmega2560. Our experiments confirm thatPQ-HASES andLA-HASES are two magnitudes of times more signer efficient than their PQ and conventional-secure counterparts, respectively.HY-HASES outperforms NIST PQC and conventional signature combinations, offering a standard-compliant transitional solution for emerging DTs. We open-sourceHASES schemes for public-testing and adaptation. 

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