Concept Of Digital Twin Research Articles

An enhanced state-aware model learning approach for security analysis in lightweight protocol implementations

Owing to the emergence and rapid advances of new-generation information and digitalization technologies, the concept of model-driven digital twin has received widespread attentions and is developing vigorously. Driven by data and simulators, the digital twin can create the virtual twins of physical objects to perform monitoring, simulation, prediction, optimization, and so on. Hence, the application of digital twin can increase efficiency and security of systems by providing reliable model and decision supports. In this paper, we propose a state-aware model learning method to simulate and analyze the lightweight protocol implementations in edge/cloud environments. We introduce the data flow of program execution and network interaction inputs/outputs (I/O) into the extended finite state machine (EFSM) to expand the modeling scope and insight. We aim to calibrate the states and construct an accurate state-machine model using a digital twin based layered approach to reasonably reflect the correlation of a device’s external behavior and internal data. This, in turn, improves our ability to verify the logic and evaluate the security for protocol implementations. This method firstly involves instrumenting the target device to monitor variable activity during its execution. We then employ learning algorithms to produce multiple rounds of message queries. Both the I/O data corresponding to these query sequences and the state calibration information derived from filtered memory variables are obtained through the mapper and execution monitor, respectively. These two aspects of information are combined to dynamically and incrementally construct the protocol’s state machine. We apply this method to develop SALearn and evaluate the effectiveness of SALearn on two lightweight protocol implementations. Our experimental results indicate that SALearn outperforms existing protocol model learning tools, achieving higher learning efficiency and uncovering more interesting states and security issues. In total, we identified two violation scenarios of rekey logic. These situations also reflect the differences in details between different implementations.

IoT-Based Framework for Digital Twins in the Industry 5.0 Era.

Digital twins are considered the next step in IoT-based cyber-physical systems; they allow for the real-time monitoring of assets, and they provide a comprehensive understanding of a system behavior, allowing for data-driven insights and informed choices. However, no comprehensive framework exists for the development of IoT-based digital twins. Moreover, the existing frameworks do not consider the aspects introduced by the Industry 5.0 paradigm, such as sustainability, human-centricity, and resilience. This paper proposes a framework based on the one defined as the outcome of a project funded by the European Union between 2010 and 2013 called the IoT Architectural Reference Model (IoT-A or IoT-ARM), with the aim of the development and implementation of a standard IoT framework that includes digital twins. This framework establishes and implements a standardized collection of architectural instruments for modeling IoT systems in the 5.0 era, serving as a benchmark for the design and implementation of an IoT architecture focused on digital twins and enabling the sustainability, resilience, and human-centricity of the information system. Furthermore, a proof of concept of a monitoring digital twin for a vertical farming system has been developed to test the validity of the framework, and a discussion of applications in the manufacturing and service sectors is presented.

Analysis of the Possibility of Making a Digital Twin for Devices Operating in Foundries

This work aims to conduct an analysis to find opportunities for the implementation of software incorporating the concept of digital twins for foundry work. Examples of implementations and their impact on the work of enterprises are presented, as is a definition and history of the concept of a digital twin. The outcome of this work is the implementation of software that involves a digital copy of the author’s device, created by the “Łukasiewicz” Research Network at the Krakow Institute of Technology. The research problem of this scientific work is to reduce the number of necessary physical tests on real objects in order to find a solution that saves time and energy when testing the thermal expansion of known and new metal alloys. This will be achieved by predicting the behavior of the sample in a digital environment and avoiding causing it to break in reality. Until now, after an interruption, the device often continued to operate and collect data even though no current was flowing through the material, which could be described as inefficient testing. The expected result will be based on the information and decisions obtained by predicting values with the help of a recurrent neural network. Ultimately, it is intended to predict the condition of the sample after a set period of time. Thanks to this, a decision will be made, based on which the twin will know whether it should automatically end its work, disconnect the power or call the operator for the necessary interaction with the device. The described software will help the operator of a real machine, for example, to operate a larger number of workstations at the same time, without devoting all their attention to a process that may last even for hours. Additionally, it will be possible to start work on selecting the chemical composition of the next material sample and plan its testing in advance. The machine learning handles model learning and value prediction with the help of artificial neural networks that were created in Python. The application uses historical test data, additionally retrieves current information, presents it to the user in a clear modern form and runs the provided scripts. Based on these, it decides on the further operation of the actual device.

On the Use of Biofuels for Cleaner Cities: Assessing Vehicular Pollution through Digital Twins and Machine Learning Algorithms

The air pollution caused by greenhouse gas emissions, particularly carbon dioxide (CO2), is a significant environmental concern that impacts air quality and contributes to global warming. The transportation sector plays a pivotal role in this issue, being a major contributor to CO2 emissions. In light of this situation, this article proposes a methodology that utilizes a supervised learning algorithm to estimate CO2 emissions and compare vehicles fueled with ethanol and gasoline. Additionally, the solution adopts an online, unsupervised machine learning algorithm to identify data outliers and improve the confidence in the results. Furthermore, this work incorporates the concept of digital twins, using virtual models of vehicles to carry out more extensive pollution simulations and allowing the simulation of various types of vehicles and the modeling of realistic traffic scenarios. A supervised machine learning approach was adopted to infer emission data in the model, allowing more comprehensive and meaningful comparisons between real-world and simulated measurements. The performed analyses of pollution emissions for different speeds and sections of routes demonstrate that CO2 emissions from ethanol were significantly lower than those from gasoline, favoring more sustainable fuels even in combustion engine vehicles. Adopting cleaner fuels is perceived as crucial to mitigate the negative effects of climate change, with plant-based fuels like ethanol being crucial during the transition from fossil fuels to a more sustainable vehicular landscape.

Recent Advances in Digital Twins for Agriculture 5.0: Applications and Open Issues in Livestock Production Systems

The potential application of the digital twins concept and its principles to all industries, including agriculture, has recently been the subject of considerable research interest. Since livestock farming is one of the mainstays of the agricultural industry, contributing to food security as well as employment and income generation, digital twins appear to be a promising technology to address issues related to the intensive nature of livestock production systems, which are typically characterized by high levels of input and output. The objective of this study is to identify and analyze the latest scientific and technological contributions in the field of digital twins technology, with a view to its adoption and assimilation in livestock production systems in the context of Agriculture 5.0. In particular, the aim of this study is to synthesize the existing research and identify the opportunities and open issues related to the implementation of digital twins in livestock production systems. The results derived in the literature review indicate that digital twins is still in the early stages of development in livestock farming, but it has the potential to transform the industry. As Agriculture 5.0 evolves and the digital twins technology matures, it is expected to play an increasingly important role in the livestock farming industry.

Adaptive digital twins for energy-intensive industries and their local communities

Digital Twins (DTs) are high-fidelity virtual models that behave-like, look-like and connect-to a physical system. In this work, the physical systems are operations and processes from energy-intensive industrial plants and their local communities. The creation of DTs demands expertise not just in engineering, but also in computer science, data science, and artificial intelligence. Here, we introduce the Adaptive Digital Twins (ADT) concept, anchored in five attributes inspired by the self-adaptive systems field from software engineering. These attributes are self-learning, self-optimizing, self-evolving, self-monitoring, and self-protection. This new approach merges cutting-edge computing with pragmatic engineering needs. ADTs can enhance decision-making in both the design phase and real-time operation of industrial facilities and allow for versatile 'what-if' scenario simulations. Seven applications within the energy-intensive industries are described where ADTs could be transformative.

Product Customization based on Digital Twin and Cloud Manufacturing within a Decentralized Production System

Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer.The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems.This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing.

Hybrid knowledge based system supporting Digital Twins in the Industry 5.0

Digital twins are employed to monitor, analyze, and predict the behavior of a manufacturing system. In literature, the concept of digital twin is mainly associated with simulation models, and only a few works addressed the integration between physics-based and data driven models. This work extends this definition and describes how different kinds of knowledge can be integrated in a hybrid knowledge based system supporting digital twin. Following the Industry 5.0 paradigm, the hybrid system can improve the human-centricity, sustainability, and resilience of a manufacturing system. The hybrid digital twin is structured as an enterprise information system, which can be connected to the other information systems of the company, such as ERP, MES, and PLM, as well as other platforms of total quality management and total productive maintenance. A discussion on the application of the proposed approach in fusion welding, as an example of a very complex process, is also presented.

Digital Twin as A New Approach to Data Management: A Review

The concept of digital twin has gained substantial attention in both academic and industrial circles in recent years due to its potential for facilitating smart manufacturing and contributing to Industry 4.0. The digital twin concept revolves around the idea of replicating the characteristics and behaviors of real-world objects to provide accurate representations in virtual environments. It has been widely used in various industries for different purposes, such as virtual experimentation, prediction and performance optimization, monitoring, and control. However, the existing studies have not investigated the potential application of digital twin for data management in a structured and methodological manner. Therefore, this paper aims to review articles that explored the use of digital twin as a data management method at different levels and dimensions. Moreover, this review critically analyses studies that explored relevant methods that can support digital twin applications in a data management context. This study follows PRISMA guidelines and reviewed 51 academic articles from various academic databases. The primary findings indicate that the digital twin can serve as a comprehensive platform for collecting, processing, analyzing, and managing data from various sources in a unified approach. Based on the critical review, future directions in this research context are outlined.

Digital Twins of Biological Systems: A Narrative Review.

The concept of Digital Twins (DTs), software models that mimic the behavior and interactions of physical or conceptual objects within their environments, has gained traction in recent years, particularly in medicine and healthcare research. DTs technology emerges as a pivotal tool in disease modeling, integrating diverse data sources to computationally model dynamic biological systems. This narrative review explores potential DT applications in medicine, from defining DTs and their history to constructing DTs, modeling biologically relevant systems, as well as discussing the benefits, risks, and challenges in their application. The influence of DTs extends beyond healthcare and can revolutionize healthcare management, drug development, clinical trials, and various biomedical research fields.

Comprehensive equipment behaviour descriptionin production lifecycle using digital twin concepts and ISO standards of Equipment Behaviour Catalogues

A standardised description of machines and equipment as well as their behaviour is essential for the planning and operation of heterogeneous production environments. The article reviews standardisation approaches of Equipment Behaviour Catalogues from the ISO 16400 series as well as similar concepts for standardised asset and equipment description in context of creating, using and maintaining digital twins. In this context, an information model for the production life cycle is introduced to harmonise the required information and interfaces for their exchange in a manufacturing organisational context.

Development of a hybrid timber and aluminum based unitized façade system resilient to the future weather conditions in Europe via monitoring campaigns and computational models

The StaticusCare project aims to develop a hybrid timber and aluminum unitized façade system (HUF) equipped with a predictive maintenance system (PMS) for Nordic climates, which will be based on the digital twin concept and fed by an Internet-of-Things system. The use of timber in the structure elements of the façade system aims to reduce the typical system's CO2 footprint by 70–75 %, and the non-renewable energy consumption by 53–56 %. Nonetheless, ensuring that this novel system is durable in the Nordic current and future climate conditions is necessary. For this purpose, the HUF system will be installed in a two-floor building, monitored by a multi-sensor campaign, and replicated computationally to assess the energy use and indoor environmental quality, as well as the hygrothermal performance of the building elements for contemporary climate and under various climate change scenarios. The maintenance of the buildings with the façade system installed will be based on a PMS that is backed by an open-source python heat, air, and moisture transport (HAM) software. This one-dimensional software will be validated using a commercial one. To analyze specific problems, such as air infiltration and moisture entrapment, a two-dimensional HAM model will also be developed. In addition, building energy simulations will be performed to test several parameters affecting the indoor climate quality and energy use. Finally, the current outdoor weather files for the HAM simulations will be built based on multi-year datasets following the ISO 15927-4 methodology and the Perez model, whilst the future weather files will be based on multi-year datasets following the same methodology for two future scenarios. This multi-step methodology will allow to thoroughly test and design the HUF façade system whilst minimizing the risk, e.g., mold growth, for current and future conditions.

A digital twin concept for optimizing the use of high-temperature heat pumps to reduce waste in industrial renewable energy systems

In light of the global industrial sector's steadfast pursuit of sustainable solutions to mitigate carbon emissions and efficiently minimize energy inefficiencies, the significance of novel approaches to energy optimization becomes increasingly evident. This research presents a novel digital twin concept that is designed to enhance the efficiency and effectiveness of high-temperature heat pumps in industrial renewable energy systems. Through the utilization of real-time data and complex computer modeling techniques, our proposed digital twin model seamlessly presents a comprehensive perspective on energy flows. This approach identifies inefficiencies and delivers practical insights to mitigate waste. The incorporation of this approach into pre-existing eco-conscious renewable energy systems has the potential to greatly enhance the effectiveness, predictability, and long-term viability of industrial processes. The empirical findings, obtained from multiple case studies conducted in industrial settings, provide evidence of the potential benefits in terms of energy waste reduction, and maximize the durability of systems. The results of our initial studies provide a foundation for the utilization of digital twin technologies in the field of industrial renewable energy systems optimization, representing a significant advancement towards a more environmentally sustainable industrial landscape.

Digital twin for educational purpose, the case study of the U-building at the KTH in Stockholm

Nowadays, the Digital Twin (DT) concept is one of the most talked-about topics in the building sector. Previous research has addressed both the definition of DT concept and the development of applications for specific purposes, such as facility and asset management, built environment performance monitoring etc. However, the specificity of such applications reduces both its replicability and understanding. This results in the spread of confusion when approaching such concepts. For this reason, this study explores how DTs can be effectively used for educational purposes. The methods used include the integration of several systems and tools, highlighting different approaches for data integration and capture. The proposed methodology was tested on a real case study, the U-building on the KTH Campus in Stockholm, which is equipped with a Building Management System (BMS). The building is used as a pilot in master courses to allow students to produce a first attempt of using a DT, by connecting real-time monitored data and digital objects. The results show how efficient data communication helps towards the organization and assimilation of complex concepts. Furthermore, the testing of several approaches proves the replicability and flexibility of the method within different environments and for several uses.

Deciphering theAncient Greek civilization script based on the concept of GenerativeAI and digital twin

Deciphering theAncient Greek civilization script based on the concept of GenerativeAI and digital twin

Data Management in Biorefineries: Conceptual Thoughts on Lean Digital Twinning

Biorefineries offer a new solution for the sustainable production of chemicals, materials, fuels, heat, and power while reducing dependence on non-renewable resources and helping to mitigate climate change. To ensure a broad implementation of biorefineries, efficiency is vital in the planning phase and must be monitored during operation. However, optimizing biorefinery processes can be challenging due to the inherent complexity of biomass feedstock, its variability, and the several technology combinations available. Digital mapping or modeling through approaches such as Digital Twins can lead to a better understanding of the processes involved. A Digital Twin fed by continuous process data can contribute to optimizing biorefinery processes, improving efficiency and helping to minimize adverse impacts. However, acquiring all relevant process parameters can be challenging due to the need to implement sensors and the volume and quality of the generated data. Here, we present the concept of Lean Digital Twin for biorefinery optimization to achieve efficient operation and monitoring while keeping overall data acquisition, processing, and storage as simple as possible. We show that implementing data-driven optimization strategies can help ensure the sustainability of biorefineries.

Augmented therapeutic tutoring in diligent image-assisted robotic interventions

This paper concerns the aptitudes of medical staff to explore new therapies and training exercises for image-assisted robotic diligent interventions. This exploration can be carried out using physical, digital, or both copies of patients. Such physical and digital phantoms should approximate real living tissues through realistic biological properties. Such a realistic assessment could be achieved through strategies to reduce physical and numerical uncertainties. The concept of physical-virtual matched pairs is used in image-assisted robotic interventions to enable such reduction. The present commentary aimed to analyze and illustrate possibilities for increasing the capabilities of medical personnel to explore new therapies and training exercises for image-assisted robotic diligent interventions. In this context, the manuscript focused on the use of the physical-virtual digital twin (DT) concept to monitor image-assisted robotic interventions, thereby reducing the complexity and uncertainties involved in such a procedure. Extensions involve robotic operations assisted by human-in-the-loop DT, artificial intelligence (AI), and augmented reality (AR). The various topics covered in this commentary are supported by a review of the literature.

Digital Twin of a battery-based EV charging station to support services

Digital Twin (DT) concepts and applications have been evolving to explore the potential to support Product-Service Systems (PSS). At the same time, DT newest developments include Electric Vehicle’ (EV) battery systems. However, the literature on Digital Twins of EV charging stations as service systems is still limited. This work presents a Digital Twin information architecture of an EV charging station aiming to support the related services. The proposed information architecture has been applied to a novel battery-based EV mobile charging platform under development. The prototyped application utilizes battery charge and discharge data to enable a scheduling service. Other services, such as reservation, SoX estimation, and lifecycle management, are also discussed. Results contribute to the expanding literature on the relationship between DT and PSS, focusing on mobility electrification. Such a service-oriented perspective of the Digital Twin application has the potential to foster innovative business models and highlight value for stakeholders.

A Hybrid Digital Twin Scheme for the Condition Monitoring of Industrial Collaborative Robots

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models’ information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots.

Malzeme Mühendisliğinde Gelişmiş Malzeme Davranışı Tahmini ve Üretim Süreci Optimizasyonu için Dijital İkizler ve Uygulamaları

The advent of Industry 4.0 and the digital revolution have brought forth innovative technologies such as digital twins, which have the potential to redefine the landscape of materials engineering. Digital twins, virtual representations of physical entities, can model and predict material behavior, enabling enhanced design, testing, and manufacturing of materials. However, the comprehensive utilization of digital twins for predictive analysis and process optimization in materials engineering remains largely uncharted. This research intends to delve into this intriguing intersection, investigating the capabilities of digital twins in predicting material behavior and optimizing manufacturing processes, thereby contributing to the evolution of advanced materials manufacturing. Our study will commence with a detailed exploration of the concept of digital twins and their specific applications in materials engineering, emphasizing their ability to simulate intricate material behaviors and processes in a virtual environment. Subsequently, we will focus on exploiting digital twins for predicting diverse material behaviors such as mechanical properties, failure modes, and phase transformations, demonstrating how digital twins can utilize a combination of historical data, real-time monitoring, and sophisticated algorithms to predict outcomes accurately. Furthermore, we will delve into the role of digital twins in optimizing materials manufacturing processes, including casting, machining, and additive manufacturing, illustrating how digital twins can model these processes, identify potential issues, and suggest optimal parameters. We will present detailed case studies to provide practical insights into the implementation of digital twins in materials engineering, including the advantages and challenges. The final segment of our research will address the current challenges in implementing digital twins, such as data quality, model validation, and computational demands, proposing potential solutions and outlining future directions. This research aims to underline the transformative potential of digital twins in materials engineering, thereby paving the way for more efficient, sustainable, and intelligent material design and manufacturing processes.