Aug 2026· Journal of Computer Science and Information Technology· 0 citations· 52 references
TL;DR
The integrated architectural approach is able to link manufacturing at the physical level with the sensing, data infrastructure, physics-based modelling, surrogate modelling, artificial intelligence, and closed-loop control levels, and puts the emphasis on the remaining need for physics-based knowledge, transparent decision making, human supervision, and validated control architectures.
Abstract
Machine learning, digital twins, cyber-physical systems, and smart infrastructure are changing the way additive and hybrid manufacturing goes from static, process-defined to adaptive, data-driven manufacturing. The reviewed integrated architectural approach is able to link manufacturing at the physical level with the sensing, data infrastructure, physics-based modelling, surrogate modelling, artificial intelligence, and closed-loop control levels. Digital threads enable ongoing data connectivity and traceability from design to production, inspection, and maintenance phases, and digital twins maintain a dynamic representation of changing conditions in the processes. In the manufacturing sector, Edge and cloud infrastructure make it possible to capture and process data in real time and manage and analyze it at scale in a variety of factory conditions. Surrogate and physics-informed models complement high-fidelity physics-based simulations for reducing computational demands and enabling rapid prediction and optimization. Layer-to-layer and within-layer control strategies further allow the adjustment of manufacturing parameters in an adaptive way using real-time process information. The framework also introduces the possibility of hybrid manufacturing processes: Additive deposition and subtractive machining, finishing, and inspection processes are linked through continuous digital data exchange. Key needs for safe industrial deployment are identified to include safety, cyber security, regulatory compliance, data governance, and model traceability. Overall, the integrated approach offers a way to more autonomous, responsive, traceable, and efficient manufacturing systems, and puts the emphasis on the remaining need for physics-based knowledge, transparent decision making, human supervision, and validated control architectures.
In general, physics-based informed intelligence and adaptive AM create a promising basis for reliable, efficient, traceable, and environmentally friendly high-performance engineering.
Fahmina Afrin· Journal of Artificial Intell...· 0 citations
Ten contributions are brought together to demonstrate how the systematic integration of physical knowledge can enhance model robustness, reduce data requirements, and improve generalization across manufacturing applications.
Jiewu Leng, Hui Yang, Min Xia et al.· Journal of Computing and Inf...· 0 citations
Digital Twin (DT) technology has emerged as a transformative paradigm in the electronics industry by enabling the creation of real-time virtual replicas of physical electronic systems, devices, and manufacturing processes. The integration of Internet of Things (IoT) sensors, artificial intelligence (AI), machine learning (ML), cloud computing, and edge computing facilitates continuous synchronization between physical assets and their digital counterparts, allowing real-time monitoring, predictive analysis, fault diagnosis, and performance optimization. In electronics design and manufacturing, Digital Twins improve production efficiency by detecting defects at early stages, optimizing process parameters, reducing equipment downtime, and enhancing product quality through predictive maintenance and intelligent decision-making. Furthermore, DT technology supports lifecycle management by enabling virtual testing, design validation, thermal analysis, reliability assessment, and energy optimization before physical deployment, thereby minimizing development costs and shortening time-to-market. The incorporation of advanced data analytics and simulation models also enables adaptive manufacturing, supply chain optimization, and sustainable electronics production. Despite these advantages, several challenges remain, including high computational requirements, interoperability among heterogeneous systems, cybersecurity risks, data privacy concerns, and the need for standardized communication frameworks. This paper presents a comprehensive overview of Digital Twin technology in electronics, discussing its architecture, enabling technologies, applications, benefits, and current research challenges. The study also highlights future research directions involving AI-driven autonomous Digital Twins, federated learning, blockchain-enabled secure data sharing, explainable artificial intelligence, and next-generation intelligent electronic systems for Industry 5.0. The findings demonstrate that Digital Twin technology has significant potential to revolutionize electronic system design, manufacturing, maintenance, and lifecycle management through intelligent, data-driven, and autonomous operations.
Malloju Dushyanthachary, Edla Chandu, N. Swaroop· International Journal of Sci...· 0 citations
This paper proposes an advanced 5D digital twin framework specifically designed for intelligent manufacturing scenarios with nonlinear, high-dimensional process dynamic characteristics. By systematically integrating geometric, temporal, physical, behavioral, and probabilistic dimensions, the proposed system extends the traditional digital twin paradigm, enabling it to comprehensively and accurately model complex manufacturing environments. Embedding physical priors and real-time probabilistic reasoning, as well as cross-domain data fusion, adaptive data preprocessing, and high-frequency industrial sensor networks—particularly LSTM-based simulation modules—constitute this architecture. To achieve interaction and monitoring, a modular visualization interface was developed. Supports dynamic process analysis, anomaly localization, and intuitive operator involvement. In the actual thermal management tasks of battery production lines, experimental validation shows that compared to traditional digital twin configurations, the 5D method improves prediction accuracy, shortens anomaly detection intervals, and enhances operational usability. This framework provides quantifiable performance metrics for benchmarking complex system behavior and supports multimodal input streams. The results highlight the 5D framework's capabilities in achieving smart manufacturing, including unifying heterogeneous industrial data sources, driving high-fidelity simulations, and providing actionable visual analytics. This comprehensive strategy makes the system more transparent, scalable, and robust.
Weijuan Leng· International Conference on...· 0 citations
Abstract. With the advent of the digital twin technology, it is now possible to monitor and control manufacturing systems in an advanced way, as the technology has developed a virtual representation of physical processes in real-time. Nevertheless, the practical use of real-time sensor information in dynamic optimization has been an important challenge in traditional manufactures. This paper introduces a digital twin-inspired optimization model, which combines real-time sensor measurements with smart decisions to improve operational efficiency. According to the offered solution, there is constant alignment between the real and virtual systems through the IoT-enabled data acquisition. The digital twin has a predictive and optimization module that is used to analyze the behavior of the system, predict the performance, and adjust operational parameters on the fly. An optimization formulation to reduce production inefficiencies, energy use, and machine downtime and uphold system constraints is created. The model is tested with a representative manufacturing scenario that has sensor-driven inputs. Its results reveal that it performs better in terms of production performance, decreased downtime, and responsiveness compared with the traditional, non-dynamic optimization strategies. The proposed methodology provides a high-scaled and realistic approach to intelligent, data-driven manufacturing systems that align with the Industry 4.0 paradigms.
A. Jain· Materials Research Proceedin...· 0 citations
Key performance indicators, including production efficiency, resource utilization, product quality, energy efficiency, downtime reduction, and system reliability, demonstrate the effectiveness of the proposed Digital Twins framework.
Suresh Babu Reddy· International Journal of App...· 0 citations
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