A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

Today's manufacturing sector undergoes essential changes due to emerging technologies and sustainability-focused practices alongside innovative operational methods. Industries worldwide are undergoing a substantial revolution as they adopt Industry 4.0 principles which involve extensive digitization and the implementation of artificial intelligence (AI). Traditional production methods are experiencing substantial transformations as Industry 4.0 principles and advanced technologies become integrated to increase industrial efficiency. According to Nathany (2024), global manufacturing output could achieve up to 25% growth through the adoption of Industry 5.0 technologies which also promises major operational cost savings. This research investigates the central elements of Industry 4.0 which consist of additive manufacturing, big data analytics, digital twins, artificial intelligence and machine learning, the Internet of Things (IoT), cloud computing, augmented and virtual reality along with blockchain technology. This study analyzes the applications of these technologies as well as their benefits and how they affect manufacturing processes. The research focuses on the ways that these technologies boost efficiency while improving sustainability and competitiveness in manufacturing. Manufacturing has experienced a revolutionary transformation through the integration of artificial intelligence and IoT which enables advanced defect detection capabilities as well as predictive maintenance and real-time monitoring. The method boosts human-machine teamwork alongside operational effectiveness and quality assurance while supporting personalized manufacturing and enhanced environmental sustainability (Nathany, 2024). This paper examines how sustainability and decarbonization will become essential factors in manufacturing's future path. Reducing environmental impact alongside optimizing resource use represents critical elements of the manufacturing industry’s transition. Additive manufacturing stands at the forefront of manufacturing evolution due to its infinite design capabilities and its efficient use of materials. Additive manufacturing generates substantial economic and environmental benefits through waste reduction and production customization. The research investigates the application of big data analytics for predictive maintenance and real-time monitoring and explains how these technologies increase production efficiency while minimizing breakdowns and unscheduled downtime. Big data analytics applications result in a 25% decrease in breakdowns and unscheduled downtime within manufacturing sectors as shown by Wang & Wang (2017). The study investigates how digital twins function as virtual models of physical assets to reveal their transformative capabilities. Real-time data analytics enable digital twins to optimize performance while predicting issues and detecting problems which then lead to substantial cost savings and enhanced planning efficiency and productivity. The advancement of digital twin technology reflects ongoing enhancements in product design and industrial manufacturing operations. Research shows digital twins can achieve 30% savings in costs while also reducing planning time by 20% and achieving 8% productivity enhancements (Kim, 2017). The discussion covers how cloud computing delivers essential computational power to support AI applications. Cloud technologies enable seamless data sharing between systems while providing adaptable solutions and support for manufacturing services that operate on demand. This research looks at how augmented and virtual reality systems can help manufacturing become more efficient by improving operational procedures and training programs through simulation-based methods. The research considers how blockchain technology supports sustainable operations while ensuring secure and transparent manufacturing networks.The study concludes by proposing future research pathways in Industry 4.0 domains which cover organizational management comprehensively and incorporate the latest organizational models. Innovative technologies will continue to generate vast opportunities for manufacturing growth and transformation. When manufacturers adopt these technological advancements they will achieve growth and competitive advantage while fostering sustainability.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

In an era where the resilient planning of cities is mainly done by a multitude of digital tools for thinking, predicting, building, and observing, the data used remains globally in a static condition[1], however the city and its environment are in a perpetual state of change and must include dynamic data for being as close to reality as possible[2].Coming from the industrial world, the emerging concept of the "Digital Twin" (DT) is positioned as a tool linking the 3D digital model and the implementation of continuous and dynamic information (from sensors among others), allowing to introduce the notion of piloting and maintenance of doubled physical objects[3]. Applied at different scales and functions the "DT" becomes a unique database allowing both the simulation of different climate risk scenarios and the real-time assessment of the existing environment[4].Based on the existing literature[5] and on experimental projects such as European or National "DT" projects designed for resilient planning such as LEAD, DUET or DIAMS[6], this communication will first focus on the analysis of different dynamic and static environmental indicators used (definitions and management of the data). In a second step, we will concentrate on the different actors of these "DT" by questioning their expectations and needs (visualizations and simulations of the different impacts due to climate change as well as the maintenance of real subjects). But also by analyzing their processes of use to understand the advantages and the limits noticed during their experimentations. Finally, we will ask ourselves how this tool could evolve thanks to the feedback from these tests.The aim is to evaluate the role that the "DT" can have on the dynamic and resilient planning of territories and conversely the impacts of programming on the use of this tool. Keywords: Digital Twin, dynamic planning, urban resiliency Références : [1] Ramalho, Cristina E., et Richard J. Hobbs. “Time for a Change: Dynamic Urban Ecology” Trends in Ecology & Evolution 27, no 3 (2012): 179‑88. https://doi.org/10.1016/j.tree.2011.10.008. [2] Chunyang He et al., “Alternative Future Analysis for Assessing the Potential Impact of Climate Change on Urban Landscape Dynamics ”, Science of The Total Environment 532 (1 novembre 2015): 48‑60, https://doi.org/10.1016/j.scitotenv.2015.05.103. [3] A. Fuller, Z. Fan, C. Day and C. Barlow, "Digital Twin: Enabling Technologies, Challenges and Open Research," in IEEE Access, vol. 8, pp. 108952-108971, (2020), doi: 10.1109/ACCESS.2020.2998358. [4] Guillaud, M., & Chéreau, M. (2022). "3. Des outils techniques accessibles et des données ouvertes ". In Inventer les villes durables (p. 119‑133). Dunod. https://www.cairn.info/inventer-les-villes-durables--9782100834204-p-119.htm [5] Shahat, Ehab, Chang T. Hyun, et Chunho Yeom. « City Digital Twin Potentials: A Review and Research Agenda ». Sustainability 13, no 6 (janvier 2021): 3386. https://doi.org/10.3390/su13063386. [6]    Objectives Lead Project. (2023). https://www.leadproject.eu/objectives/  About digital urban european twins. (s. d.). DUET. https://www.digitalurbantwins.com/digitaltwindemo  Planification urbaine pour les collectivités pilotes DIAMS. (s. d.). https://www.airdiams.eu/planification-urbaine-pour-les-collectivites-pilotes

Cities worldwide are rapidly integrating urban management information technology (IT) systems to address increasing urban challenges effectively. This paper examines the evolution of these systems, from the Ubiquitous City (U-City) to contemporary Smart Cities, and explores emerging technologies like the Metaverse and Digital Twin. Despite their growing relevance, there is ambiguity in their conceptual definitions and how they are applied interchangeably in urban management contexts. This study aims to clarify these technologies by analyzing U-City, Smart City, Metaverse, and Digital Twin within an Analysis Framework of Urban Management IT Systems. Our analysis reveals distinct roles and interconnections among these technologies. U-City initiated the development of essential IT infrastructure, laying the groundwork for subsequent systems. Smart City technology, leveraging the infrastructure provided by U-City, addresses real urban issues but lacks predictive capabilities for future challenges. In contrast, the Metaverse facilitates the simulation of urban changes and planning in a virtual environment, influencing real-world city development. Digital Twin technology enhances the operational efficiency of U-City and Smart City by simulating real-world data and optimizing urban management processes. The study highlights that while U-City and Smart City function in the tangible realm, Metaverse and Digital Twin operate in the virtual domain, yet they are closely integrated. Metaverse assists in urban planning through simulations that inform real-world Smart City designs, while Digital Twin optimizes real-time city operations and informs Metaverse simulations for future planning. The findings underscore the importance of understanding these technologies’ unique contributions and their interplay. Although current urban management IT systems have limitations, especially in technology classification and data accessibility, this research paves the way for future investigations. Further research should focus on refining the definitions and applications of Metaverse and Digital Twin, clarifying their relationships with Smart City and U-City, and addressing security and privacy concerns associated with these technologies.

Digital Twin (DT) technology, when combined with Artificial Intelligence (AI), has emerged as a transformative approach for predictive maintenance in modern industries. This paper synthesizes secondary evidence from academic literature, industry reports, and international standards (2018–2025) to examine how AI-enhanced digital twins are enabling predictive maintenance across manufacturing, energy, and transportation. Digital twins replicate physical assets virtually, while AI models analyze real-time sensor data to detect anomalies, forecast failures, and optimize maintenance schedules. Using structured analysis of ISO standards, Gartner, McKinsey, and IEEE literature, we map how digital twins integrate with IoT data streams, machine learning models, and maintenance workflows. The study identifies key enabling technologies (IoT, cloud computing, ML/DL), common architectural layers, and documented benefits, such as reduction in unplanned downtime (30–50%) and improved asset life cycles. A conceptual flowchart illustrates the AI–DT predictive maintenance loop, and a table compares sectoral adoption patterns. Findings highlight the convergence between AI analytics and DT simulations, enabling data-driven, condition-based maintenance strategies. Challenges remain in data interoperability, model updating, and cybersecurity. Future work should focus on standardized frameworks and cost-benefit models for broader adoption.

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.

Digital Twins are emerging as a transformative technology within Cyber-Physical Systems (CPS), offering enhanced optimization, predictive maintenance, and real-time monitoring. However, their integration also introduces significant security challenges. These include vulnerabilities such as data breaches, unauthorized access, and cyber-attacks that disrupt real-time data flow between their physical and digital components. The involvement of IoT devices, sensors, and complex networked environments expands the attack surface, making Digital Twins susceptible to threats like Distributed Denial-of-Service (DDoS) attacks, malware infiltration, and insider sabotage. Effective risk management and assessment are crucial in identifying vulnerabilities, evaluating risks, and implementing mitigation strategies. Securing Digital Twins ensures data integrity, system reliability, and the continued functionality of the physical assets they represent. This paper aims to classify the various security threats associated with Digital Twins and propose structured risk management approaches to enhance their security within CPS. By addressing these challenges, organizations can ensure the dependability and trustworthiness of Digital Twin implementations across industries such as manufacturing, healthcare, smart cities, and IoT ecosystems.

The rapid advancement of Industry 4.0 and the Internet of Things (IoT) has underscored the importance of effective diagnostics for electric motors, which are critical components in industrial, energy, and transportation systems. Traditional diagnostic methods, such as vibration, thermographic, and electromagnetic analyses, often lack adaptability and pre-dictive capabilities, limiting their ability to proactively identify faults under variable operating conditions. The digital twin concept, a virtual representation of physical assets synchronized with real-time sensor data, offers a transformative approach to technical condition monitoring. This paper reviews the state of diagnostic methods for electric mo-tors and evaluates the integration of digital twins to enhance diagnostic accuracy and pre-dictive maintenance. By leveraging platforms like Ansys Twin Builder and MATLAB/Simulink, digital twins enable real-time simulation, fault detection, and opera-tional optimization. The study proposes a novel methodology for diagnosing electric motor conditions using digital twins, incorporating mathematical modeling and real-time signal processing. Key findings include improved fault detection accuracy (MAE < 2.5%, RMSE < 3.1%) and the ability to simulate typical defects like rotor imbalance and insula-tion breakdown. The results demonstrate the potential of digital twins to revolutionize condition-based and predictive maintenance, offering scalable solutions for industrial applications. Future prospects include integrating machine learning for adaptive diagnos-tics and expanding digital twin applications to complex electromechanical systems.

• Examined various battery types, BMS architectures, and modelling techniques. • Explored deterministic, non-deterministic, and hybrid SOC estimation methods. • Discussed SOH estimation and RUL prediction using various methods. • Exploration of digital twin technologies and battery digital twins (BDTs) for enhancing BMS functionality. • Discussion of future research directions in battery state estimation. Accurate state estimation is fundamental for the safety, reliability, and performance of electric vehicle (EV) batteries. This review highlights the critical need for precise evaluation of key battery states such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL). It surveys the existing modelling and estimation techniques used in lithium-ion battery (LIB) systems. It explores parameter identification methods and the challenges associated with real-time monitoring and predictive diagnostics in dynamic EV environments. The paper further investigates the transformative role of digital twin (DT) technologies in addressing these challenges. By integrating battery digital twins technologies(BDTs): Internet of Things (IoT), cloud computing, artificial intelligence (AI), and extended reality (XR), it offers a virtual replica of the battery system. That enables continuous monitoring, predictive maintenance, and performance optimization. The review delves into the architecture, functions, challenges and future perspectives of battery digital twins (BDTs). And it emphasizes their potential to enhance traditional battery management systems (BMS) through intelligent, adaptive control. Through comparative analysis and identification of research gaps, this review provides a roadmap for future advancements in state estimation and digital twin integration.

Monitoring and control of air filtration systems: Digital twin based on 1D computational fluid dynamics simulation and experimental data

Digital Twin (DT) technology is revolutionizing industry in the Industry 4.0 era by enabling simulating, analyzing, and monitoring physical assets real-time. To reduce downtime for machines, improve maintenance schedules, and maximize equipment lifespan, this research explores the use of digital twins in implementing predictive maintenance (PdM) concepts. An imitation of an actual machine or process that is continuously supplied with information from embedded Internet of Things sensors is referred to as a "digital twin." Condition-driven maintenance, in contrast to reactive or time-based maintenance, is enabled by the system to predict failures before they occur by the integration of AI/ML algorithms. With reference to four key components—data collection, model building, simulation environment, and feedback mechanisms—the research provides a conceptual framework for the application of digital twins in a typical manufacturing environment. It also illustrates a case study of a CNC machine that employs real-time temperature and vibration data to anticipate spindle bearing issues. Compared to standard methods, DT-based predictive maintenance enhances asset availability, cost savings, and accuracy in maintenance significantly. To deliver insights into future directions and scalability of DT in smart factories, the article concludes by considering challenges such as data security, interoperability, and computing complexity. Keywords: Industry 4.0, IoT, Smart Manufacturing, Machine Learning, CNC, Condition Monitoring, Digital Twin, Predictive Maintenance.

The convergence of digital twins and artificial intelligence (AI) is ushering in a transformative era for businesses across various industries. Digital twins, virtual replicas of physical entities or processes, have evolved beyond their traditional use in manufacturing and are now playing a pivotal role in optimizing operations, enhancing decision-making, and fostering innovation. This chapter explores the profound impact of digital twins in the age of AI, elucidating how these interconnected technologies are reshaping business models, improving efficiency, and driving unprecedented insights. The synergy between digital twins and AI enables organizations to create dynamic and responsive simulations, providing real-time visibility into the performance and behavior of physical assets. From predictive maintenance and supply chain optimization to personalized healthcare solutions and smart cities, the chapter delves into diverse applications where digital twins powered by AI are delivering tangible benefits.

Development and application of digital twin technology for integrated regional energy systems in smart cities

A virtual mirrored replica of the real-world, has become a new trend in recent years with the advent of Industry 4.0, shows how intelligent digital twins (DT) can unlock busi-ness value. The implementation of DT requires various types of technologies, including the Internet of Things (IoT), cloud computing, artificial intelligence, and others. Today, DTs can be used in various fields, including manufacturing, smart cities and healthcare. It can be used to monitor the real environment, predict its future, control its behavior, and improve its overall performance. Despite the aforementioned advantages of DTs, if not protected, they can also be considered as an open environment for attackers. If attackers take over a DT, they may end up owning the real environment controlled by the DT. This can result in damaging consequences. This paper explores the potential security and privacy threats that may be brought in through DTs. It proposes and presents a threat meta-model of DTs that identifies key security aspects of concern.