This study proposes a microservices-based software architecture for implementing digital twins (DT) in real-time industrial process control. Unlike monolithic approaches, the proposed architecture decouples acquisition, modeling, prediction, and actuation into independent containerized services orchestrated by Kubernetes. Communication is handled via MQTT for lightweight telemetry and gRPC for low-latency control calls, achieving highly predictable, bounded synchronization latencies below 10 ms under nominal edge conditions. A discrete-time Luenberger observer ensures bidirectional state fidelity between the physical asset and its virtual replica. Experimental validation on an edge cluster with TSN compatibility demonstrates elastic scalability, fault isolation, and 100% operational uptime during a continuous 24 h stress test, validating the architectural mechanisms designed to achieve high availability in production environments. The architecture bridges the gap between passive DT monitoring and active closed-loop control, enabling predictive interventions and real-time adaptability.
Digital Twin (DT) technology has become a key enabler of smart manufacturing; however, its application in Computer Numerical Control (CNC) systems remains largely limited to monitoring and predictive tasks rather than real-time control. This limitation is particularly critical for remote operation of multi-axis CNC machines, where latency and synchronization directly affect control performance. This paper presents a systematic review of Digital Twin architectures for CNC machine tools, focusing on their suitability for remote monitoring and control. A PRISMA-based methodology is adopted to ensure a transparent and reproducible study selection process. The selected works are analyzed from an architectural perspective, considering system structure, communication protocols, synchronization strategies, and control integration. The results indicate that most existing implementations are not designed for time-critical closed-loop control and exhibit significant latency constraints. Cloud-based architectures typically introduce delays ranging from tens to hundreds of milliseconds, whereas edge-based approaches reduce latency to a few milliseconds, highlighting a trade-off between scalability and real-time performance. The study identifies key research gaps and emphasizes the need for hybrid edge–cloud Digital Twin architectures capable of supporting reliable and low-latency remote CNC control.
Omar A. Hashem, A. M. Bassiuny, Hussein M. A. Hussein et al.· Evolutionary Intelligence· 0 citations
Digital twins have become foundational in cyber-physical systems by mirroring asset state and enabling monitoring, simulation, and optimization. However, the data infrastructure that powers modern analytics and AI—including transformation logic, orchestration state, lineage, and data quality rules—is typically encoded across fragmented scripts, configuration artifacts, and logs, making it difficult to audit, evolve, or safely automate. This paper proposes Meta-Twins, a system-of-systems digital twin for data engineering infrastructure, where engineering intent is represented as normalized relational metadata (mirrored state) and continuously enforced by persistent daemon processes (autonomous behavior). Meta-Twins exposes a SQL-native control plane for defining transformations, dependencies, schedules, execution history, and quality gates, thereby decoupling semantics from tool-specific execution mechanics and providing a bounded interface for safe AI-assisted updates. We implement a prototype on Databricks to demonstrate feasibility and portability of the design, and we evaluate Meta-Twins through a structured comparison with procedural pipelines and declarative modeling tools across coupling, transparency, self-service readiness, and resilience. The results indicate that representing infrastructure intent as relational state enables consistent auditability, deterministic execution, and reduced operational coupling, supporting human-centric and resilient data engineering aligned with the conceptual goals of Industry 5.0, particularly in terms of transparency and human-centric interaction.
Z. T. Lee, Arvin Shadravan, Hamid R. Parsaei· Human-Intelligent Systems In...· 0 citations
Integrating Model-Based Systems Engineering (MBSE) with Digital Twins (DTs) offers a promising way to transform static system models into dynamic, runtime-connected representations that enable execution, monitoring, and validation. Much current research in this area remains focused on descriptive modeling, simulation-based workflows, or unidirectional data flow, leaving a gap in methods that link architectural models directly to physical system behavior in a closed-loop system. This study fills that gap by proposing and demonstrating a four-layer framework that supports traceable, bidirectional interaction between a system’s architecture and its physical implementation. The framework consists of four interconnected layers: System Definition, Model Translation and Integration, Execution and Monitoring, and Feedback and Synchronization. The System Definition Layer captures mission goals, functional responsibilities, subsystem decomposition, and mission parameters in Capella using the Arcadia methodology. The Model Translation and Integration Layer translates these properties into executable commands via Python4Capella, converting architecture-level parameters into actionable instructions. The Execution and Monitoring Layer executes these commands in MATLAB, while the Feedback and Synchronization Layer returns runtime data to the model, supporting validation, model awareness, and refinement. A UAV case study is used to validate the framework: the UAV architecture is decomposed into key logical subsystems, and mission behaviors such as takeoff, movement, turning, and landing were modeled parametrically using the Property Values Management Tool (PVMT). These properties were translated into MATLAB commands, executed on a physical UAV, and evaluated based on telemetry-based mission distance accuracy. This work demonstrates that an MBSE model can serve not only as a design artifact but also as an authoritative, execution-connected component of a digital twin workflow. The framework contributes to MBSE-driven digital twin research by providing a structured process for integrating architecture, translation, execution, monitoring, and synchronization in a traceable manner. Overall, the study provides a scalable foundation for future developments in hardware-in-the-loop testing and digital twin applications for cyber–physical systems, and further empirical evaluation is needed to assess the framework’s transferability across domains.
Md Robiul Islam, Aditya Akundi, Sergio A. Luna Fong· Systems· 0 citations
The increasing complexity of automotive software driven by ADAS and autonomous driving has intensified the need for time-deterministic network validation beyond CAN/LIN, while HIL integration remains constrained by limited ECU prototypes and labor-intensive manual configuration. This paper presents a network-oriented virtual verification environment that couples Renode-based virtual ECUs (vECUs) with FMU-based Interaction Layer (FIL) Nodes automatically generated from DBC specifications. The vECUs provide instruction-accurate execution of unmodified target binaries without physical hardware, while the generated FIL Nodes encapsulate communication behavior as model-based FMUs to maintain continuity from MIL to SIL without manual signal mapping. The proposed framework constructs virtual CAN networks from communication-definition files, reproduces periodic and event-triggered traffic patterns, and supports synchronized multi-ECU co-simulation under master-controlled time stepping. Experimental results show that the vHIL environment reproduces physical ECU timing behavior with a maximum relative error of 0.086–0.171% across all evaluated step sizes (10 μs to 1000 μs), confirming binary-level timing fidelity. Replacing vECUs with FIL Nodes for network communication processing significantly reduces wall-clock execution time in multi-node configurations, demonstrating improved scalability without sacrificing determinism. These results demonstrate that the proposed methodology effectively reduces early-stage integration bottlenecks while preserving timing fidelity for automotive networked ECU validation.
Harim Lee, Hyeongrae Kim, Jeonghun Cho· Electronics· 0 citations
Industrial digital twins integrate physical objects, dynamic models, control systems, data analysis tools, 2D and 3D visualization, and existing software systems. Much research has focused on the functional composition of the digital twin, modeling, and application scenarios, while the organization of the digital twin as an evolving software system composed of technologically heterogeneous and autonomous subsystems remains insufficiently formalized. The goal of this study is to develop a conceptual composite architecture for an industrial digital twin, in which complex subsystems are viewed as highly interconnected and loosely coupled service components of a higher-order system. The research method is based on analogy, transfer, and adaptation of proven principles of distributed and microservice systems to the constraints of industrial digital twins. An architectural model is proposed that includes a physical object, a process model, SCADA subsystems, a unified data exchange subsystem, 2D and 3D representations, VR/AR components, and automated model building modules. The practical feasibility of the approach is demonstrated using a proof-of-concept digital twin of a methanol–ammonia co-production plant, integrating Honeywell UniSim Design R460.1, web-based SCADA, Unity, and specialized 2D and 3D representation generation modules. This demonstration confirms the feasibility of integrating independently developed subsystems and the technological heterogeneity of the solution, but does not constitute a production test of performance, scalability, or cost effectiveness. Requirements for contract stability, a consistent interaction environment, assigned data responsibility, version compatibility, and complete documentation are defined. Research limitations and areas for subsequent quantitative architecture validation are identified.
E. Koltsova, Maksim Pysin, A. Lobanov et al.· Information· 0 citations
This paper explores the integration of Spring Boot with containerization and observability, focusing on best practices, architectural patterns, and challenges, and delves into containerization strategies using Docker and orchestration with Kubernetes, emphasizing their roles in deployment and scalability.
Youssef- Al-Habib, Omar Sullaiman· International Journal of Art...· 1 citation