Twin Concepts Research Articles

Towards Digital-Twin-Driven Factory Planning – A Systematic Review

In factory planning, the interoperability problem poses high demands to information technology. The emerging concept of the digital twin, which refers to a coupled virtual representation of an asset, currently represents a promising approach to integrating planning information, methods, and tools. However, its exact definition and role in factory planning as well as the current development and implementation status needs clarification. Therefore, this paper provides an overview of the current state by systematically reviewing the literature on digital factory twins and their potential applications in factory planning. The findings highlight the potential of integrated information systems for creating digital factory twins and the challenges in defining modeling standards and reference architectures. Further, simulation capabilities are frequently leveraged by real-time data interfaces. Finally, we identify opportunities and directions for further research to aid the industrial adoption of digital factory twins.

Digital twin of earth: A novel information framework for managing a sustainable earth

<p>As the progress of human society rapidly enters a new stage, sustainable development has become an essential target. It is necessary to have an effective approach to managing the resources and ecosystems of Earth, with the target of promoting sustainable development while meeting the needs and requirements of both present and future generations. Digital twin technology provides a fresh perspective on sustainable management with a better understanding of the interconnectedness of Earth's systems and accurate simulation and prediction of future changes. By integrating various types of data and models, including climate models, geophysical models, and social media data, a digital twin of Earth can simulate and analyze various aspects of the planet. The digital twin of Earth leverages real-time multi-source data integration through advanced IoT and wireless sensor networks, feeding into a data lake architecture that supports the seamless integration and processing of heterogeneous data. The integrated data then informs dynamic models, combining numerical simulation models with intelligent algorithms for enhanced prediction and analysis. This approach emphasizes the integration of environmental, social, and economic considerations into decision-making that balances economic growth, social well-being, and environmental protection to achieve sustainable development. This paper introduces the definition and information framework of the new concept of a digital twin of Earth to promote the current research on Earth management based on digital transformation techniques.</p>

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.

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.

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 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.

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.

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.

Leveraging epoch‐era analysis and digital twin for effective system concept and execution: A CO2$_2$ storage salt‐cave project

AbstractWith the demand to reduce the release of CO‐rich gases into the atmosphere, the offshore industry is turning to systems for gas capture and storage. The construction of a cave for that purpose is planned in the salt layer of the Santos Basin, Brazil; however, numerous factors bring uncertainties to the project. We propose combining systems engineering methods and digital twin technology to enable system concept and execution aiming at value robustness. The system is modeled in mission, subsystems, components, and design attributes. System utilities and costs are estimated for a range of viable solutions based on the relevance of each attribute to the stakeholders. The alternatives are evaluated using the Epoch‐Era Analysis framework for analyzing the system's performance over time in changing future scenarios. The system model also outlines a digital twin concept and identifies how it might support salt cave design and operations. Finally, the potential of tuning and improving system evaluation based on gathered data is examined, and measures toward further digital twin development are recommended.

From framework to industrial implementation: the digital twin in process planning

AbstractIn today’s fast-paced market, companies are challenged to meet increasing customer demands and shorter product life cycles. To successfully respond to these demands, companies must produce a wide variety of different products. This requires the determination of necessary processes and resources for each product, which can be difficult for process engineers due to the high manual effort and expertise involved. The current state of research has not yet provided explicit definitions of the necessary knowledge and has not fully achieved complete process planning automation. To address this challenge, a digital twin is a valuable tool for automating and understanding process planning. This paper presents a digital twin concept for process planning. It automatically analyzes the product, determines production processes, and selects appropriate resources by linking information about products, resources, and processes. The effectiveness of the digital twin concept is demonstrated through verified and validated use cases, including the production of a compressor element.

A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

Engineering has many necessary fields, and Structural Health Monitoring (SHM) is one of the most important of them. Sometimes in industrial environments, it is difficult and even impossible to collect data containing different real damages. Therefore, the problem of data acquisition represents a primary challenge in designing damage detection systems. The application of digital twin methods based on simulated models and/or Machine Learning (ML) models is a practical way to solve this problem. In this approach, a digital twin is generated for a compromised structure, utilizing a physics-based model to analyze diverse damage scenarios. Subsequently, an ML model is trained using data extracted from the physics-based model, functioning as the digital twin. This research proposes a method based on a digital twin for detecting damages in structures. The data produced from a Floating Wind Turbine (FWT) model was used to evaluate the performance of the proposed digital twin-based method. For this purpose, the FWT structure was simulated using a numerical model to address the data collection problem in the face of various uncertainties, such as changing loading conditions. In line with the concept of digital twin and to reduce the computational time, a Deep Convolution Long Short-Term Memory Neural Network (DCLSTMNN) model was designed and trained only with the frequency data of various scenarios of the simulated FWT model under constant loads (deterministic loads, including constant wind speed and airy wave model) to learn the damage-sensitive features. Then, to demonstrate the robustness of the proposed model under different uncertainties, the DCLSTMNN model was evaluated using the frequency data of the simulated FWT model under variable loading conditions (including Kaimal wind model and JONSWAP wave theory). Some vibration response components unrelated to the nature of the FWT model were removed using the Complete Ensemble Empirical Mode Decomposition (CEEMD) method. Then, the reconstructed vibration responses were used to create the frequency data using the Frequency Domain Decomposition (FDD) technique. The study results show that the proposed digital twin-based method can detect the location and severity of damage more accurately than other comparable methods despite various uncertainties.

Digital twin of patient in clinical workflow

The concept of a digital twin being made use of in healthcare is an emerging research area. Digital twins in healthcare have the potential to enable more precise and personalised care, especially for patients experiencing chronic conditions. Previous work has identified digital twins as mathematical models. Digital twins have been classified as grey box, surrogate and black box models. Based on this classification, the black box models can handle data intensive and sophisticated problems. This makes black box models a candidate for constructing digital twins of patients. Such digital twins can then assist clinicians in clinical decision making. This has the potential to reduce cognitive burden on clinicians, to increase precision and personalisation of care through enhanced use of data, and to improve patient outcomes and cost implications. However, introducing such digital twins to healthcare would be a significant intervention that would alter traditional clinical workflows. As such, we present in this paper one of the first attempts of conceptual mapping of altered next generation primary care clinical workflows that would allow the incorporation of digital twins of patients in managing chronic conditions.

Direct Digital Manufacturing (DDM) Workflow for Printed Circuit Structures (PCS)

Digitally based manufacturing has been around for years and has been the basis of turnkey lights out manufacturing processes and systems. Moving forward is the concept of Direct Digital Manufacturing (DDM) which brings together conceptualizing the electronic device, developing a digital twin of the product, and digitally merging workflow with the manufacturing process. This would further extend through to physical product. This paper will illustrate DDM is the basis of developing the capability of conceptualizing, designing, and digitally manufacturing electronic devices and more specifically printed circuit structures or PCS and printed circuit cylinders, PCC. Unlike tradition PCB’s or multichip module technology, the goal is fully printed electronic devices that can merge structure with electronic functionality. This paper will explore the current state of practice in manufacturing workflow and extend to the emerging factory in a Box concept of multi-material printing of next generation electronic controller and packaging technology. This will also include exploring design tools that provide the capability of co-design mechanical function with electronic function and modeling the manufacturing process and electronic device using digital twin concepts and workflow enabling smart manufacturing of printed electronic devices and precision of DDM technology.

Digital twin modeling method of the temperature field of thermo-compression bonding blade based on generative adversarial networks

In this paper, a novel method of using deep learning to construct a digital twin of the thermos-compression bonding blade`s temperature field is presented. Usually, a heat transfer model based on numerical analysis can calculate the heat distribution in the welding process, but its complex modeling process and heavy computational effort are not appropriate for the deployment of the digital twin concept. The approach proposed in this paper is distinctive because it directly converts the collected process sensor data into the bonding blade's temperature field images and the bonding surface's temperature distribution curves for monitoring the welding process. This is achieved by training the generator and discriminator of the conditional depth convolutional generative adversarial network separately. The network is then capable of predicting the bonding blade's heat distribution within a certain range of welding parameters. Compared to methods based on numerical simulation, this approach is far more effective. Training and testing datasets were created to assess the suggested method's efficacy. Multi-scale structural similarity index, structural similarity index, peak signal-to-noise ratio, difference hash and mean absolute error were used to evaluate the simulations' quality. The results show that the simulation's results and the actual data agree quite well. The temperature changes that occur during the welding process may be quickly and accurately simulated using the proposed technology.

Conciliarity in World Council of Churches Texts. Intersections, Interactions, Specific Emphases

The Synod of Bishops on Synodality has placed the church as a synodal community at the core of Catholic theological reflection. The conciliar or synodal nature of the church is also a topic of ecumenical dialogue. This essay surveys the theme of conciliarity in some WCC texts over the past 50 years, arguing that discussions of conciliarity reflect the interrelation between contemporary ecclesial events and theological developments. This diachronic analysis also indicates that while for two decades conciliarity has been regarded as a hopeful model for achieving communion and collaboration between the churches, the concept has gradually lost its appeal. The twin concept of synodality emerged in the latest ecclesiological convergence texts with a different focus. The study reveals the shift from an understanding of conciliarity centred on the people of God and baptism to one increasingly focusing on ordained ministry. At the same time, the topic of synodality, which remerged recently in Catholic theology, indicates the reception of some ideas developed in ecumenical dialogue.

Application of digital twin technology in Russia: Development opportunities and constraints

Aim. To identify trends in the development of the digital twin concept in Russia.Objectives. To analyze the experience of applying such an innovative tool as a digital twin at Russian enterprises and consider the benefits of its implementation; to characterize the aspects of regulation and state support in the field of digital twin application in Russia; to reveal the main problems hindering the development of digital twin technology, as well as to propose posible solutions to the identified problems.Methods. The authors used empirical methods, in particular, the analysis and generalization of data from scientific and methodological literature; methods of observation and comparison. The article also reflects the results of scientific research of Russian and foreign scientists.Results. It is established that today the method of digital twins in the economy is becoming more and more popular. Thanks to the introduction of digital twin technology, companies manage to speed up production processes, increase their transparency, reduce costs, improve the quality of manufactured products and, in general, achieve greater efficiency, competitiveness and investment attractiveness. The main constraints to development include insufficient funds required to implement digital transformation, lack of necessary experience and highly qualified specialists, low level of legal framework in the field of digital twins and high level of risks associated with data security. For successful implementation of digital transformation, it is advisable to study and generalize successful examples of digital twins implementation both in foreign and domestic companies, to create a unified management system for this area, to develop a motivation system for companies applying digital twin technology, to increase the level of information security.Conclusions. In Russia, innovative technologies are a priority for ensuring technological leadership and entry of domestic companies into international markets. Despite lagging behind some foreign countries, Russia has great potential for the formation of a national market and the introduction of digital twin technology, especially among large corporations and high-tech enterprises. But for this purpose it is necessary to eliminate the factors that hinder the development of the digital twin method, such as high risks associated with cybersecurity, insufficient funding in the use of technology, the lack of specificity in some cases in the field of legal regulation and a number of others.

Digital Inheritance of Traditional Mongolian Robes of the Nayman Tribe

Abstract The Mongolian robe is the crystallisation of the wisdom of Mongolian people after a long period of social practice and one of the intangible cultural heritages of humanity. With the gradual disappearance of the nomadic lifestyle in the steppe, it is not easy to protect and pass on traditional Mongolian dress culture and traditional handicraft technology. This study used the digital twin concept to construct a digital twin to try on clothes for a model in a virtual environment, to realise a mirror image presentation of the target physical entity, and to use effective modern technology to protect and pass on the traditional clothing. The experiment used an AlphaM4 3D body scanner to obtain a 3D human body model, as well as reverse engineering technology to process the initial data, such as noise reduction, streamlining, hole patching and smoothing to build a personalised human body model. The traditional black cotton robe of the Nayman tribe researched in the field was mapped by the planar structure design method, and its structure and 2D samples were recovered. A 3D model of the traditional cotton robe of the Nayman tribe was established based on 3D simulation technology to achieve a 3D virtual display effect, enable better dissemination and inheritance, and give a new vitality to non-heritage clothing in the digital era. The research results can provide fresh ideas and technical support for the digital estate and development of other traditional costumes, as well as contribute to the construction of digital resources for intangible cultural heritage.