Digital Twin Technology Research Articles

Research on online deformation monitoring of thin-walled parts driven by digital twin

Thin-walled parts are widely used in aerospace. Because thin-walled parts have thin walls, weak stiffness, and are easy to deform and other characteristics, it is easy to produce deformation in the processing, affecting the quality and accuracy of the workpiece processing. Therefore, accurate and efficient online real-time monitoring of the deformation of thin-walled parts has become a vital issue in process control. The visualization, real-time monitoring, and iterative optimization of digital twin technology can effectively solve the problem of online monitoring of thin-walled parts processing. Therefore, this paper proposes a digital twin driven deformation monitoring method for thin-walled parts, which realizes the effective monitoring of deformation during the processing of thin-walled parts. Firstly, the digital twin architecture for online deformation monitoring of thin-walled parts is established, and the critical technologies of the digital twin for online deformation monitoring of thin-walled parts are proposed. Secondly, the Support Vector Regression (SVR) finite element surrogate model is established to realize the rapid simulation of thin-walled parts cutting. At the same time, a One-Dimensional Convolutional Neural Network (1DCNN) combined with the Self-Attention Mechanism (SAM) deformation prediction model was established based on sensor data. The real-time monitoring and accurate prediction of the deformation of thin-walled components are realized through the parallel guidance of the deep learning and surrogate model. Finally, an experimental analysis was carried out by machining thin-walled plates of aluminum alloy 7075, and the fit of the monitoring models was above 95%, thus verifying the validity of the proposed method.

A Systematic Review of the Digital Twin Technology in Buildings, Landscape and Urban Environment from 2018 to 2024

Digital Twin (DT) technologies have demonstrated a positive impact across various stages of the Architecture, Engineering, and Construction (AEC) industry. Nevertheless, the industry has been slow to undergo digital transformation. The paper utilizes the Systematic Literature Review (SLR) approach to study a total of 842 papers on the application of DT in buildings, landscapes, and urban environments (BLU) from 2018 to 2024. Based on the research results, suggestions have been made for future research and practical directions. Meanwhile, it provides assistance to BLU’s designers, constructors, managers, and policymakers in establishing their understanding of the digital transformation of the AEC industry. The existing relevant research can be mainly divided into three categories: case study, framework study, and technology study. Compared with the buildings and urban environment industries, the number and depth of research in the landscape industry are relatively low. Through in-depth analysis of BLU projects, three research trends in the future are determined: (1) research and application of DT framework in the design and planning stage; (2) development of design tools and basic theory based on DT model; (3) application and exploration of DT technology in the landscape industry.

A Robotic Teleoperation System with Integrated Augmented Reality and Digital Twin Technologies for Disassembling End-of-Life Batteries

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, this paper presents a robotic teleoperation system that leverages augmented reality (AR) and digital twin (DT) technologies to enable a human operator to work away from the danger zone. By integrating AR and DTs, the system not only provides a real-time visual representation of the robot’s status but also enables remote control via gesture recognition. A bidirectional communication framework established within the system synchronises the virtual robot with its physical counterpart in an AR environment, which enhances the operator’s understanding of both the robot and task statuses. In the event of anomalies, the operator can interact with the virtual robot through intuitive gestures based on information displayed on the AR interface, thereby improving decision-making efficiency and operational safety. The application of this system is demonstrated through a case study involving the disassembly of a busbar from an EoL EV battery. Furthermore, the performance of the system in terms of task completion time and operator workload was evaluated and compared with that of AR-based control methods without informational cues and ‘smartpad’ controls. The findings indicate that the proposed system reduces operation time and enhances user experience, delivering its broad application potential in complex industrial settings.

Multi-agent Deep Reinforcement Learning for cloud-based digital twins in power grid management

As the power industry becomes increasingly complex, Digital Twin (DT) technology has emerged as a crucial tool for enhancing grid resilience and operational efficiency by creating dynamic digital replicas of physical systems. These replicas enable accurate simulations and proactive management, but the vast amount of data generated by DT systems poses significant challenges for processing and analysis. Cloud computing offers a flexible solution by offloading these computational tasks to distributed resources, allowing for real-time analysis and scalable operations. However, this approach introduces complexities in task distribution and maintaining quality of service. Recent efforts have applied Deep Reinforcement Learning (DRL) to address these challenges, primarily using single-agent methods. However, these methods struggle with scalability and performance in increasingly complex cloud environments. To overcome these limitations, we propose an efficient task scheduling framework based on Multi-Agent Deep Q-Network (MADQN) principles, specifically designed to optimize both response times and operational costs. We provide a comprehensive design overview of our approach and conduct a thorough evaluation of its performance. The experimental results clearly indicate that our approach can considerably reduce response times amd lower operational costs compared to current methods, including state-of-the-art single-agent approaches.

Design and Implementation of Digital Twin Factory Synchronized in Real-Time Using MQTT

As information technology progresses, the need for digital transformation within the industrial sector has become increasingly apparent, and digital twin technology has emerged as a significant trend in manufacturing. Digital twins synchronize physical and digital environments, overcoming spatial and temporal limitations to create various added values that are unattainable in reality. This paper presents a model that integrates digital twin technology with production and operational technologies at manufacturing sites, enabling remote, centrally controlled manufacturing services that transcend physical constraints. Specifically, by utilizing Message Queuing Telemetry Transport (MQTT) for real-time synchronization, this approach ensures efficient and timely data transfer between physical and digital environments. While traditional approaches often encounter challenges due to high investment costs and design complexities, this paper proposes a cost-effective and practical solution that reflects actual factory conditions.

Digital twin-enhanced robotic system for remote diesel engine assembly defect inspection

Purpose This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork. Design/methodology/approach This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period. Findings Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers. Originality/value The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Research on the Possibilities of Expanding the Photovoltaic Installation in the Microgrid Structure of Kielce University of Technology Using Digital Twin Technology

Global challenges related to sustainable development are increasingly focusing on the use of digital twin technology as a universal tool for optimizing and monitoring renewable energy installations. This article discusses digital twin technology as a support for sustainable development based on the analysis of microgrid structures. Digital twins allow the creation of virtual models of physical systems. This capability facilitated the accurate replication of the microgrid model at Kielce University of Technology using ETAP (Electrical Transient Analyzer Program) software (version 22.5). The operational parameters of the microgrid structure were analyzed for the examined power range of the photovoltaic installation to determine the possibilities of expanding the existing installation. The impact of the photovoltaic installation’s power on the operational parameters of the microgrid structure was visualized, and final conclusions were formulated. Moreover, the integration of digital twin technology into renewable energy systems not only enhances operational efficiency but also plays a pivotal role in advancing sustainability objectives. Through real-time monitoring and predictive maintenance, digital twin technology facilitates the optimization of energy production and distribution, thereby reducing waste and contributing to the overall sustainability of energy systems. This technology enables the simulation of various scenarios, such as fluctuations in energy demand or the integration of new renewable sources, which can inform more sustainable decision-making processes. In the context of microgrids, digital twin technology ensures that energy production is closely aligned with consumption patterns, minimizing energy losses and enhancing grid resilience. Furthermore, digital twin technology supports the sustainable expansion of renewable installations by providing detailed insights into potential environmental impacts and the long-term sustainability of various energy configurations. As the demand for clean energy continues to grow, digital twin technology will be indispensable in achieving a balance between energy needs and environmental stewardship, ensuring that the expansion of renewable energy sources contributes positively to global sustainability objectives.

Double edged tech: navigating the public health and legal challenges of digital twin technology

Abstract This research addresses the legal challenges posed by the implementation of digital twin technology in public health. Digital twins are advanced computational models which aim to replicate human organ functions and biological processes. Their goal is to enable the simulation of the entire human body and to personalize the outputs with individual patient’s health data. Digital twin technology promises to bridge gap between personalized medicine and precision public health. This research is based on a comprehensive review of the current European and Swiss legal frameworks for medical devices and data protection that apply to digital twin technology. Within these frameworks, digital twins are in a grey area, as they are only considered as medical device software when applied to individuals. They also incorporate artificial intelligence, machine learning, and data analytics, which are already a source of legal concern in a healthcare setting, but their effective use in public health raises additional issues. Preliminary findings suggest significant shortcomings in the legal frameworks regarding data privacy, and the potential for increased surveillance by health insurance systems, which could lead to human rights violations. The public health application of digital twins would require global data analysis of sensitive personal information, raising further questions about the secure storage, transmission, and processing of this data. The study concludes that current legal frameworks must be future proofed to manage the complexities introduced by digital twins in public health. In particular, it suggests that regulatory oversight of medical devices needs to be significantly improved in order to safely harness the potential of digital twin technologies for public health. Finally, the new European Union health data space and the forthcoming EU AI act will also play important roles in the implementation of digital twin technologies. Key messages • Applying digital twin technology in public health faces considerable challenges, particularly the issues around data privacy, potential discrimination, and increased surveillance. • We need to adapt and strengthen existing legal frameworks to effectively manage the complexities introduced by digital twins, ensuring that these technologies can be safely used for public health.

Conditional Generative Adversarial Networks with Optimized Machine Learning for Fault Detection of Triplex Pump in Industrial Digital Twin

In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use of Generative Adversarial Networks (GANs) to generate synthetic data that replicate real-world data, capturing essential features indicative of health-related information without directly referencing actual industrial DT systems. This paper introduces an intelligent fault detection and diagnosis framework for industrial triplex pumps, enhancing fault recognition capabilities and offering a robust solution for real-time industrial applications within the DT paradigm. The proposed framework leverages Conditional GANs (CGANs) alongside the Harris Hawk Optimization (HHO) as a metaheuristic method to optimize feature selection from input data to enhance the performance of machine learning (ML) models such as Bagged Ensemble (BE), AdaBoost (AD), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB). The efficacy of the approach is evaluated using key performance metrics such as accuracy, precision, recall, and F-measure on a triplex pump dataset. Experimental results indicate that hybrid-optimized ML algorithms (denoted by “ML-HHO”) generally outperform or match their classical counterparts across these metrics. BE-HHO achieves the highest accuracy at 95.24%, while other optimized models also demonstrate marginal improvements, highlighting the framework’s effectiveness for real-time fault detection in DT systems, where SVM-HHO attains 94.86% accuracy, marginally higher than SVM’s 94.48%. KNN-HHO outperforms KNNs with 94.73% accuracy compared to 93.14%. Both DT-HHO and DT achieve 94.73% accuracy, with DT-HHO exhibiting slightly better precision and recall. NB-HHO and NB show near-equivalent performance, with NB-HHO at 94.73% accuracy versus NB’s 94.6%. Overall, the optimized algorithms demonstrate consistent, albeit marginal, improvements over their classical versions.

Agent-based modeling system for real-time characterization of film thickness in digital twin coating

In the automated spraying workshop of ship segments, poor visibility caused by dust and fog hampers the effectiveness of the video monitoring system. The traditional method used during the automated spraying process fails to provide real-time measurement of film thickness and prediction of spraying quality, resulting in uneven film thickness and high rework rates. Robotic spraying technology also faces challenges, such as the time-consuming and labor-intensive process of building and computing a highly reliable Computational Fluid Dynamics (CFD) based spraying film-forming simulation model, which cannot be reflected in real-time during the spraying process. The digital twin characterization method, based on the agent model, offers a solution to these limitations and challenges, providing new ideas and approaches for improving and optimizing the spray film formation model. This paper presents the development of a real-time characterization system for sprayed film thickness based on digital twin technology. The spraying film thickness under different working conditions is obtained using Ansys-Fluent simulation software by varying the gun height and gun speed. The predictive model for spraying film thickness is then established using the obtained simulation data and the corresponding gun height and gun speed, employing the surrogate modeling method (BP neural network). The real-time characterization system for spraying film thickness is developed by combining C# and Unity3D. Finally, in order to ensure the accuracy of the predictive model, a spraying experimental platform is built to verify the accuracy of the predictive model.

Digital Twins for Reducing Energy Consumption in Buildings: A Review

This research investigates the use of digital twin (DT) technology to improve building energy management and analyse occupant behaviour. DTs perform the function of acting as virtual replicas of physical assets, which facilitates real-time monitoring, predictive maintenance, and data-driven decision-making. Consequently, energy performance and occupant comfort can be enhanced. This study evaluates the efficiency of DTs in optimising energy usage by a mix of a systematic literature review and scientometric analysis of 466 articles from the Scopus database. Among the main obstacles noted are interoperability issues, privacy and data quality difficulties, and the requirement for a more thorough integration of digital and physical interactions. The results highlight the necessity of standardised frameworks to direct DT implementations and suggest areas for further study, especially in the areas of improving cybersecurity and incorporating occupant behaviour into DT models. This research makes practical recommendations for using DT technology to increase energy efficiency and sustainability in the built environment.

TRIPLE: A blockchain-based digital twin framework for cyber–physical systems security

Cyber–physical systems (CPSs) are being increasingly adopted for industrial applications, yet they involve a dynamic threat landscape that requires CPSs to adapt to emerging threats during their operation. Recently, digital twin (DT) technology (which refers to a virtual representation of a product, process, or environment) has emerged as a suitable candidate to address the security challenges faced by dynamic CPSs. DT has the capability of strengthening the security of CPSs by continuously mapping the physical to twin counterparts to detect inconsistencies. The existing DT-based security solutions are constrained by untrustworthy data dissemination as well as limited data sharing among the involved stakeholders, which, in turn, limit the ability of DTs to run accurate simulations or make valid decisions. To address these challenges, this paper proposes a modular framework called TRusted and Intelligent cyber-PhysicaL systEm (TRIPLE), that leverages blockchain, DTs, and threat intelligence (TI) to secure CPSs. The blockchain-based DT components in the framework provide data integrity, traceability, and availability for trusted DTs. Furthermore, to accurately and comprehensively model system states, the framework envisions fusing process knowledge for modeling DTs from system specification-based and learning-based information and other sources, including infrastructure-as-code (IaC) and knowledge base (KB). The framework also integrates TI for future-proofing against emerging threats, such that threats can be detected either reactively by mapping the behavior of physical and virtual spaces or proactively by TI and threat hunting. We demonstrate the viability of the framework through a proof of concept. Finally, we formally verify the TRIPLE framework to demonstrate its correctness and effectiveness in enhancing CPS security.

Mitigating Thermal Side-Channel Vulnerabilities in FPGA-Based SiP Systems Through Advanced Thermal Management and Security Integration Using Thermal Digital Twin (TDT) Technology

Mitigating Thermal Side-Channel Vulnerabilities in FPGA-Based SiP Systems Through Advanced Thermal Management and Security Integration Using Thermal Digital Twin (TDT) Technology

Squirrel Cage Induction Motors Accurate Modelling for Digital Twin Applications

The ongoing industrial revolution emphasizes the importance of precise machinery monitoring. Among these machines, induction motors (IMs) stand out due to their large numbers, which imply a significant part of industrial energy consumption. To achieve accurate in-service IM monitoring, robust modelling is required, with a particular emphasis on in situ constraints. In this study, we create a precise digital model for squirrel cage induction motors (SCIMs) that can be used in Industry 4.0 digital twin applications. To achieve this, we survey the existing literature, describe the main modelling techniques, identify the best models in terms of ease of implementation, and ensure the accuracy of our digital representation. We develop four methods, namely finite element analysis (FEA), thermal modelling, circuit-based models, and quantum-based fuzzy logic control, as a crucial first step in implementing digital twin (DT) technology for IMs. The quantum fuzzy logic is based on the transition from classical equations to the quantum equation determining the speed of the motor in the quantum world by passing through the Schrödinger equation. We propose the DT level of integration architecture for IMs based on the industry 4.0 reference architecture model. Finally, the main tools used to successfully implement DT for IMs are revealed.

Enhancing internal supply chain management in manufacturing through a simulation-based digital twin platform

Digital Twin (DT) technology is profoundly changing the manufacturing landscape and supply chain management with its ability to create real-time digital replicas of physical processes, allowing for enhanced monitoring and optimized decision-making. However, the analysis of scientific literature reveals that further efforts are needed to spread the use of Industry 4.0 technologies, in the specific context of Internal Supply Chains (ISCs). The main aim of this study is to design, develop test and validate a multi-plant Simulation-Based DT production planning platform for ISCs management. A modular architecture is adopted, and the focus is on a Simulation-Based Digital Twin module, which uses an object-oriented structure and enables what-if analyses, involving several scheduling rules and ISC configurations. The proposed solution ensures flexibility and scalability, two crucial features in a constantly evolving market environment. The platform is tested and validated through a case study, involving a corporate group in the Oil & Gas manufacturing sector, which needs to improve the ISC performance, under a Make-To-Order production strategy. The comparison with a baseline scenario, where the platform is not adopted, shows that the proposed approach can significantly reduce the average flow time, the average tardiness, the number of late orders. This study has important practical implications because enables proactive and smart decision-making, aimed at resource optimization and continuous improvement, through predictive analytics and scenario analysis.

Smart Building Digital Twin for Interior Water Distribution System Management

Abstract. A smart campus integrates the Internet of Things, artificial intelligence, and real-time sensor data to optimize campus functions and create a context-aware decision-support platform for effective campus management. A crucial aspect of a smart campus is the water distribution system, which faces several challenges due to bursts, leaks, and water quality issues. This study uses Digital Twin technology to address these challenges through real-time monitoring, detection, and management of campus water distribution networks. A building-level digital twin is developed for the interior water networks of the Daphne Cockwell Complex at Toronto Metropolitan University. The major components include a 3D network model capable of analysis and simulations, a smart water management system, and real-time visualization. A 3D static model of the interior water utility network is created using the Industrial Foundation Class data. The semantic and topological models based on graph models are developed for network analysis. The models are integrated into the ESRI ArcGIS Pro to facilitate BIM-GIS integration. A dynamic model is created by incorporating automatic meter readings based on IoT. Combined with the 3D model of the utility network, the digital twin monitors and visualizes building water consumption, facilitating anomaly detection and real-time maintenance by the facilities management department. This study provides practical guidance for managing water distribution systems in university buildings, with potential applications in other complex environments. Future work aims to develop a system for detecting complex event processes by integrating different utilities.

Leveraging cloud compute and open source software to generate 3D models from drone photography

Abstract. Modern drone platforms provide the opportunity to capture large quantities of high quality, geotagged aerial imagery quickly and over large distances, at a relatively low cost. As organisations increasingly turn to digital twin technology to manage their operations, rich, photorealistic three dimensional (3D) models of their assets become increasingly important. This paper describes how cloud computing, and container based serverless compute in particular, can be used together with open source software to generate a 3D model, converted to 3D tiles for optimised transmission, from images of large outdoor assets captured by drone. This approach offers a flexible and cost effective way to processing drone imagery into 3D models whilst allowing you to maintain ownership and control of your data.

The digital twin framework: A roadmap to the development of user- centred digital twin in the built environment

With the prompt digital revolution in architecture, engineering, and construction (AEC), it is crucial to set the early practical steps for advanced digital technology namely digital twin technology. Although theoretical evidence on the potential of digital twins exists in AEC scope, the practical framework to the construction and operation of user-centred digital twin is yet to be investigated. Hence, this applied research investigates systematic methods and techniques for the construction and enablement of a user-centred digital twin paving the way to advanced digital applications in AEC centred on the user of the built environment. As a result, the framework unleashes a systematic roadmap to the creation and development of a city-scale digital twin centred on the human and capable of describing, analyzing, and assessing a built context per its users and usability manner. Further, the framework pours the foundations for a spectrum of digital twin applications in the scope of architectural design, building construction, infrastructure engineering, and facility management. The production of this research is foreseen to outline a systematic and transferable framework for the construction and enablement of a user-centred digital twin.

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

Research on Electric Vehicle Powertrain Systems Based on Digital Twin Technology

As a critical component of electric vehicles, the powertrain has a significant impact on the overall performance of vehicles. In addressing the challenge of lengthy testing cycles, this study develops a para model of the powertrain, utilizing digital twin (DT) technology, thereby establishing a framework for simulation testing of multi-controller intermodulation. We establish functional definition coverage testing by designing specific functional requirement use cases, and we validate the failure mechanism via fault injection use cases. The results indicate that the DT testing platform can effectively simulate the operational interactions among various controllers within the powertrain system. In comparison to traditional field testing, the digital twin-based testing methodology offers enhanced operational efficiency and allows for the examination of testing conditions that are impractical to implement in real vehicles, particularly in the context of fault injection testing, thus facilitating the early detection of potential safety risks within the system. The advancement of this technical solution holds significant practical implications for the future mass production and development of electric vehicles.