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Digital Twin in manufacturing: A categorical literature review and classification
Abstract
The Digital Twin (DT) is commonly known as a key enabler for the digital transformation, however, in literature is no common understanding concerning this term. It is used slightly different over the disparate disciplines. The aim of this paper is to provide a categorical literature review of the DT in manufacturing and to classify existing publication according to their level of integration of the DT. Therefore, it is distinct between Digital Model (DM), Digital Shadow (DS) and Digital Twin. The results are showing, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.
... In general, a DT fulfills some of these requirements inherently. It consists of a physical object and a virtual representation, where both parts are in a bidirectional data exchange to another [6]. Given, that both parts obtain the same state, the virtual representation gives opportunities for analysis and performance evaluation. ...
... As we stated in the introduction, a DT has certain characteristics which distinguish it from a pure simulation model. A detailed description of the difference between these concepts can be found in [6]. The main difference can be identified in the way data flows between the physical object and its digital representation. ...
... These components, like the AAS registry, as well as Software Development Kits in Java, C++, and C# are provided. The concept of the AAS was introduced by the Platform Industrie 4.0 5 and is now further developed by the Industrial Digital Twin association 6 . The AAS provides a standardized digital representation (meta-model) [13] of the asset and its interfaces [14]. ...
This study introduces DigiWind, an extensible digital twin platform specifically designed for the wind energy domain. The research aims to identify the fundamental requirements and architectural design for such a platform. Functional requirements are identified through a requirements engineering process using the use-case methodology. Existing digital twin and co-simulation platforms are reviewed, and the proposed DigiWind architecture is presented in detail. Three use cases are presented to highlight the integration of workflows and simulation models in the digital twin process. The DigiWind platform features a layered architecture with core implementations such as the template service, model assembly service, co-simulation service, and measurement data service. These components enable the automation of simulations, incorporation of historic measurement data, and data feedback and exchange. The platform supports various use cases including retrospective evaluation, performance monitoring, and scenario simulations. Additionally, a knowledge base and a versioning system ensure automation, documentation, and reproducibility of simulation results. The platform’s openness promotes collaboration among wind energy stakeholders and supports standardized models for co-simulation using the Functional Mock-up Interface. Overall, DigiWind offers a solution for developing, managing, and integrating digital twins in the wind energy sector, enhancing wind farm performance and operational efficiency.
... Depending on the integration level, Kritzinger et al. [13] structured DTs into four categories, as follows: ...
... A Google Scholar search for "Digital Twin classification" yielded 27,100 results. Singh et al. [13] classified Digital Twins according to several parameters, including creation time, integration level, role within the application, focused application parameters, hierarchical structure, and maturity level. This classification helped in understanding the technologies facilitating the development and effective implementation of Digital Twins, as well as their similarities and particularities. ...
Digital Twin (DT) technology has experienced substantial advancements and extensive adoption across various industries, aiming to enhance operational efficiency and effectiveness. Defined as virtual replicas of physical objects, systems, or processes, Digital Twins enable real-time simulation, monitoring, and analysis of real-world behavior. This comprehensive review delves into the evolution of DT technology, tracing its journey from conceptual origins to contemporary technological implementations. The review provides detailed definitions, a classification of different types of Digital Twins, and a comparative analysis of their architectures. Furthermore, it investigates the application of DT technology in diverse sectors, with a particular emphasis on medicine and manufacturing, exemplified by use cases such as personalized medicine. Moreover, the review highlights emerging trends and future directions in DT technology, underscoring the transformative potential of integrating artificial intelligence and machine learning to augment DT capabilities. This analysis not only elucidates the current state of DT technology but also anticipates its future trajectory and impact across multiple domains.
... No entanto, ainda existem desafios para a implementação de gêmeos digitais tendo em vista que existem uma variedade de definições, terminologias inconsistentes e nenhum procedimento padronizado para criar aplicativos de gêmeos digitais (Shaon, 2021). Em seu estudo, Kritzinger et al. (2018) Uma solução para superar os desafios e problemas foi desenvolver e implantar aplicações piloto para prova de conceito do Digital Twin. Neste contexto, o trabalho contribuirá para a compreensão de como os gêmeos digitais funcionam na prática e como devem ser implementados, e ao final do trabalho desenvolverá aplicações para prova de conceito desta tecnologia, que servirão como case de utilização futura para replicação em outros contextos. ...
... De acordo comKritzinger et al. (2018) existem três subcategorias a serem consideradas em aplicações Digital Twin, conforme ilustrado na Figura 2: Digital Model (Modelo Digital), Digital Shadow (Sombra Digital) e o Digital Twin (Gêmeo Digital), ambas se diferem em razão do nível de integração de dados entre o físico e o equivalente digital. ...
Este artigo descreve o processo de virtualização de um robô de 3 eixos com ventosa a vácuo (VGR - Vacuum Gripper Robot) para aplicações piloto de prova de conceito do Digital Twin. Buscou-se a representação da dinâmica de um ambiente de teste de placas incluindo as características do gêmeo físico, tendo como objetivo investigar a utilização do simulador de eventos discretos Flexsim® para virtualização. A pesquisa experimental foi realizada em laboratório, em parceria com a Universidade do Estado do Amazonas (UEA) e seu Centro de Tecnologia e Inovação (HUB), no Laboratório da Indústria 4.0 (I-4.0). O estudo utilizou o simulador Flexsim®, um CLP Delta AS228P e o ambiente físico de fábrica Fischertechnik®. O Digital Twin desenvolvido possibilitou a movimentação das peças entre os módulos do simulador físico de forma similar ao ambiente real. Os resultados comprovam o conceito de Digital Twin e demonstram os desafios de trabalhar com modelos de simulação que refletem as características de um ambiente real, bem como permitem melhorias no sistema desenvolvido e geram oportunidades para trabalhos futuros.
... In the manufacturing sector, DTs find application in various focused areas, all aimed at enhancing efficiency, productivity, and competitiveness. Key applications within the manufacturing process include layout planning, maintenance, and production planning and control [48]. Layout planning involves continuous data acquisition 2.5. ...
... Hence, a need arose for a separate classification to distinguish these variations. Kritzinger [48] proposed a classification of DTs based on the level of data integration, which includes: ...
... Automation in industry is experiencing increasing modularization and digitalization. The ubiquitous availability of data and services opens up new perspectives such as the vision of self-organized, adaptive and partially selforganizing flexible production systems for ever shorter product life cycles and an increasing product variance [1,2]. As a result, production can be made more efficient and sustainable, e.g. through shorter setup times and the optimized use of energy and resources [3]. ...
... The use cases that arise over this time horizon are very diverse. An overview of possible use cases from literature and their basic chronological classification into the machine life cycle is shown in Figure 11 [2,4,5,7,29,34,44,47,60]. However, with the variance of these use cases addressed, two trends can be identified. ...
The concept of the Digital Twin, which in the context of this paper is the virtual representation of a production system or its components, can be used as a "digital playground" to master the increasing complexity of these assets. Central subcomponents of the Digital Twin are behavior models that can provide benefits over the entire machine life cycle. However, the creation, adaption and use of behavior models throughout the machine life cycle is very time-consuming, which is why approaches to improve the cost-benefit ratio are needed. Furthermore, there is a lack of specific use cases that illustrate the application and added benefit of behavior models over the machine life cycle, which is why the universal application of behavior models in industry is still lacking compared to research. This paper first presents the fundamentals, challenges and related work on Digital Twins and behavior models in the context of the machine life cycle. Then, concepts for low-effort creation and automatic adaption of Digital Twins are presented, with a focus on behavior models. Finally, the aforementioned gap between research and industry is addressed by demonstrating various realized use cases over the machine life cycle, in which the advantages as well as the application of behavior models in the different life phases are shown.
... Also, current advances in digital twin technology attempt to create a bidirectional connection between the twin and its counterpart [124]. This enables the digital twin not only to monitor but also to control its real-world counterpart. ...
... In the past, researchers conducted a plethora of surveys concerning the digital twin [19,115,124,136,138,162,218,223]. ...
In an era of rapid technological advancements, digital twins are gaining attention in industry and research. These virtual representations of real-world entities, enabled by the Internet of Things (IoT), offer advanced simulation and analysis capabilities. Their application spans various sectors, from smart manufacturing to healthcare, highlighting their versatility. However, the rise of digital technologies has also escalated cybersecurity concerns. Historical cyberattacks underscore the urgency for enhanced security operations. In this context, digital twins represent a novel approach to cybersecurity. Industry and academic research are increasingly exploring their potential to protect their assets. Despite growing interest and applications, more comprehensive research synthesis needs to be done, particularly in security operations based on digital twins. Our paper aims to fill this gap through a structured literature review aggregating knowledge from 201 publications. We focus on defining the digital twin in cybersecurity, exploring its applications, and outlining implementations and challenges. To maintain transparency, our data is documented and is publicly available. This survey serves as a crucial guide for academic and industry stakeholders, fostering digital twins in security operations.
... Different digital twins may serve distinct control purposes. To better investigate them, scholars have developed the typology of digital twins (e.g., [8,15,[25][26][27]), which offers a theoretical basis for understanding the status of current agricultural digital twin applications later in this study. To date, there are four representative classification methods. ...
... Level of data integration. One of the most well-known is Kritzinger et al.'s [26] three types, including digital model (DM), digital shadow (DS), and digital twin (DT). It was based on the level of data integration between the physical entity and its virtual counterpart. ...
Digital twin technology is expected to transform agriculture. By creating the virtual representation of a physical entity, it assists food producers in monitoring, predicting, and optimizing the production process remotely and even autonomously. However, the progress in this area is relatively slower than in industries like manufacturing. A systematic investigation of agricultural digital twins’ current status and progress is imperative. With seventy published papers, this work elaborated on the studies targeting agricultural digital twins from overall trends, focused areas (including domains, processes, and topics), reference architectures, and open questions, which could help scholars examine their research agenda and support the further development of digital twins in agriculture.
... DTs have become indispensable across industries, revolutionizing how organizations manage assets, optimize processes, and make decisions [4][5][6]. In manufacturing, they simulate production lines, predict maintenance needs, and enhance operational efficiency [7][8][9]. In healthcare, DTs of patients enable personalized treatment plans and predictive health monitoring [3,10,11]. ...
In this study, a digital twin model of a hydroelectric power plant has been created. Models of the entire power plant have been created and malfunction situations of a sensor located after the inlet valve of the plant have been analyzed using a programmable logic controller (PLC). As a feature of the digital twin (DT), the error prediction and prevention function has been studied specifically for the pressure sensor. The accuracy and reliability of the data obtained from the sensor are compared with the data obtained from the DT model. The comparison results are evaluated and erroneous data are identified. In this way, it is determined whether the malfunction occurring in the system is a real malfunction or a malfunction caused by measurement or connection errors. In the case of sensor failure or measurement-related malfunction, this situation is determined through the digital twin-based control mechanism. In the case of actual failure, the system is stopped, but in the case of measurement or connection errors, since the data are calculated by the DT model, the value in the specified region is known and thus there is no need to stop the system. This prevents production loss in the hydroelectric power plant by ensuring the continuity of the system in case of errors.
... (i) Digital Model; (ii) Digital Shadow; (iii) Digital Twin (adapted from[10]). ...
Context: Digital Twins (DTs) are used to augment physical entities by exploiting assorted computational approaches applied to the virtual twin counterpart. A DT is generally described as a physical entity, its virtual counterpart, and the data connections between them. Multi-Agent Systems (MAS) paradigm is alike DTs in many ways. Agents of MAS are entities operating and interacting in a specific environment, while exploring and collecting data to solve some tasks.
Objective: This paper presents the results of a systematic literature review (SLR) focused on the analysis of current proposals exploiting the synergies of DTs and MAS. This research aims to synthesize studies that focus on the use of MAS to support DTs development and MAS that exploit DTs, paving the way for future research.
Method: A SLR methodology was used to conduct a detailed study analysis of 64 primary studies out of a total of 220 studies that were initially identified. This SLR analyses three research questions related to the synergies between MAS and DT.
Results: The most relevant findings of this SLR and their implications for further research are the following: i) most of the analyzed proposals design digital shadows rather than DT; ii) they do not fully support the properties expected from a DT; iii) most of the MAS properties have not fully exploited for the development of DT; iv) ontologies are frequently used for specifying semantic models of the physical twin.
Conclusions: Based on the results of this SLR, our conclusions for the community are presented in a research agenda that highlights the need of innovative theoretical proposals and design frameworks that guide the development of DT. They should be defined exploiting the properties of MAS to unleash the full potential of DT. Finally, ontologies for machine learning models should be designed for its use in DT.
... Digitization is an essential technology underpinning various applications, such as preserving cultural heritage and artistic masterwork [Stanco et al. 2017;Zabulis et al. 2022], performing industrial assessments before mass production [Kritzinger et al. 2018;Min et al. 2019], running virtual laboratories for teaching and training [Davies 2019;Fogel and Kvedar 2018;Kvedar et al. 2016], as well as enabling realistic interaction in virtual/augmented reality [Bruno et al. 2010;Jiang et al. 2021;Khor et al. 2016;Speicher et al. 2017]. Creating realistic digital twins for physical objects requires quantitative knowledge of both their visual appearances (geometry, texture, etc) and physical properties (elasticity, pressure, etc) [Kapteyn et al. 2021;Kim and Park 2015]. ...
Accurately digitizing physical objects is central to many applications, including virtual/augmented reality, industrial design, and e-commerce. Prior research has demonstrated efficient and faithful reconstruction of objects' geometric shapes and visual appearances, which suffice for digitally representing rigid objects. In comparison, physical properties, such as elasticity and pressure, are also indispensable to the behavioral fidelity of digitized deformable objects. However, existing approaches to acquiring these quantities either rely on invasive specimen collection or expensive/bulky laboratory setups, making them inapplicable to consumer-level usage. To fill this gap, we propose a wearable and non-invasive computing framework that allows users to conveniently estimate the material elasticity and internal pressure of deformable objects through finger touches. This is achieved by modeling their local surfaces as pressurized elastic shells and analytically deriving the two physical properties from finger-induced wrinkling patterns. Together with photogrammetry-reconstructed geometry and textures, the two estimated physical properties enable us to faithfully replicate the motion and deformation behaviors of several deformable objects. For the pressure estimation, our model achieves a relative error of 3.5%. In the interaction experiments, the virtual-physical deformation discrepancy measures less than 10.1%. Generalization to objects of irregular shape further demonstrates the potential of our approach in practical applications. We envision this work to provide insights for and motivate research toward democratizing the ubiquitous and pervasive digitization of our physical surroundings in daily, industrial, and scientific scenarios.
... Digital twins in the industrial field can meet the forwardlooking needs of full product lifecycle management and enable product design, manufacturing, service, and other product lifecycle activities to be carried out efficiently (Kritzinger et al., 2018;Lo et al., 2021). Especially in the product manufacturing and service phases, digital twin technology can realize real-time monitoring and accurate prediction of product performance and ensure the consistency of product specifications and requirements (Javaid et al., 2023;Soori et al., 2023;Alnowaiser et al., 2022;Feng et al., 2023). ...
Tungsten Inert Gas (TIG) welding is a manufacturing process that utilizes argon as a shielding gas and tungsten as an electrode to join metals at high temperatures. The weld penetration is the key to determine the quality of the weld. However, the lack of sensing technology makes weld penetration difficult to predict, which imposes a major challenge to process stability and weld quality. To address this challenge, a digital twin-driven method is proposed for characterizing the melt pool morphology and melt penetration prediction. To achieve this, an analytical model of the melt pool with time-varying welding speed under the action of a double ellipsoidal circular heat source is first derived. The analytical model is solved using the numerical integration method. The prediction of melt depth and melt width is achieved by extracting isotherms. Meanwhile, a digital reconstruction of the welding scene was achieved by implementing the Neural Radiance Fields (NeRF) method. The target rendering of the melt pool and welding scene is accomplished by constructing voxels and meshes. Furthermore, VR is utilized as the interface for human–computer interaction, and a digital twin model of the molten pool morphology and welding scene is generated. The prediction model's accuracy is verified through welding experiments using 304L steel on a robotic welding system. The results show that in the 0–4 s stage, the penetration error is controlled within 7%. In the stage of 4–16 s when the speed changes, the maximum error of penetration is 16.59%. In terms of welding scene reconstruction quality, PSNR is 33.98 and SSIM reaches 0.9032. The method allows real-life simulation of different welding conditions and parameter combinations prior to welding, assessing their impact on the welding results, in order to find the optimal configuration of process parameters. It can also be remotely realized to monitor and control the melt penetration in real-time during the welding process. This method provides a new solution and a theoretical guidance system to solve the welding penetration control problems and it plays an important role in promoting welding intelligence.
... The analysis underscores critical research trajectories and reflects the transformative trajectory of manufacturing ecosystems, underscored by the integration of DT technologies and IM concepts. [22,39] Drawing from the extensive body of literature on I4.0 and insights from Plattform Industrie 4.0 [40][41][42][43][44][45][46], this work delineates the key considerations necessary for the effective integration of AAS and SM. The structure of this article is meticulously organized to facilitate a comprehensive understanding of these considerations. ...
Industry 4.0 (I4.0) epitomizes the nexus of technological evolution and manufacturing process enhancements, underpinned by four critical pillars: Cyber-Physical Systems (CPS), Digital Twins (DT), the Industrial Internet of Things (IIoT), and Cloud Computing (CC). Through the lens of the German Reference Architectural Model Industrie (RAMI) 4.0, these components are integral to fostering Smart Manufacturing (SM) practices. In conjunction, the Asset Administration Shell (AAS) facilitates seamless inter-company communication and enables adaptability to fluctuating market demands. This paper delves into the structural intricacies of AAS and SM within the RAMI 4.0 framework, unveiling three distinct implementation strategies. Furthermore, it meticulously scrutinizes challenges that hinder the full realization of I4.0 potential, such as interoperability, security concerns, and standardized communication protocols. By examining these models and identifying current barriers, the study illuminates pathways for future research, particularly in enhancing the integration of AAS and SM systems, ensuring robust security measures, and advocating for the development of universal standards. This exploration aims to contribute to the ongoing discourse on I4.0, proposing avenues for advancing manufacturing technologies and operational frameworks in alignment with the principles of RAMI 4.0.
... • Yellow Group, a collection of technologies that operate as data capturers in the field, is known as Field Data Capturing Technology (FDCT) (Alizadehsalehi & Yitmen, 2016;Demiralp et al., 2012). • Blue Group, known as Industry 4.0 Drivers (I4.0 Drivers) technologies (Begić & Galić, 2021; IBM, n.d.; Kritzinger et al., 2018), consists of AI, Cloud, Big Data keywords and 9,627 duplicates were found. As a result, the data that passed the identification stage was 12,657 papers for further observation at the screening stage. ...
Fragmentation caused by increased project complexity surely harms the construction industry’s performance, resulting in low productivity and waste. Construction 4.0 technology is expected to overcome productivity but causes fragmentation due to its partial implementation. Technology integration can provide more significant innovation. In recent years, there has been some research on technological integration. The study’s focus is solely on the interaction between several technologies, with no further explanation of how these technologies interact with one another, and is currently in the conceptual stage. As a result, the purpose of this study is to visualize the integration of construction technology in the form of a comprehensive and up-to-date framework among Construction 4.0 technologies. The framework is constructed using link analysis from the database gathered through the systematic literature review using PRISMA Method. It highlights the dominant role of BIM in integrating Construction 4.0 technology, despite the most extensive integrated set including only seven out of the 21 identified technologies from previous research. An evaluation was conducted to assess the latest advancements, leading to a proposal for developing a series of Construction 4.0 technologies. This research is expected to encourage and motivate stakeholders and academics to adopt and implement integrated Construction 4.0 technology, address productivity challenges and overcome fragmentation.
... The concept of the digital twin has evolved since its debut in 2002. In subsequent developments, various definitions of digital twins have emerged [18][19][20]. Grieves and Vickers [21] pointed out that digital twin was referring to a set of information that describes an asset completely, from its most general geometry, to the most concrete behavior. Subsequently, digital twin has evolved into a broader concept that refers to a virtual representation of manufacturing elements such as personnel, products, assets and process definitions, a living model that continuously updates and changes as the physical counterpart changes to represent status, working conditions, product geometries and resource states in a synchronous manner [22]. ...
The production requirements of multi-variety and high-quality products pose significant challenges for implementing intelligent production lines that integrate process sensing, adaptive processing, and precise decision-making. Digital twin is the key enabling technology for intelligent production lines, but it lacks the perception of multidimensional process information to enable adaptive production. Thus, this paper proposes the adaptive digital twin motivated by a knowledge model with process information interactions. First, the process information related to product quality is located, and the process information is digitally modeled to implement the virtual-real mapping. Second, multidimensional feature extraction and fusion of process information digital model are performed to form adaptive adjustment capabilities for multi-variety production. Finally, the formal modeling of the adaptive adjustment capability enables the construction of the knowledge model that drives the adaptive digital twin. In adaptive digital twin, the virtual and real mapping of process information facilitates the knowledge model to continuously update the domain knowledge, which enables the adaptive decision-making ability to dynamically adapt to different processing requirements in the physical model. As a fusion technique developed in intelligent production lines, the modeling of adaptive digital twin has been well applied and validated in commutator finishing lines.
... Particularly under the influence of Industry 4.0 and the Internet of Things (IoT), digital twins have become a key technology in manufacturing, urban planning, healthcare, and more [19]. In the manufacturing sector, digital twins are utilized to create virtual copies of products to optimize design, test performance, and predict maintenance needs [20]. In urban planning, the application of digital twins aids city administrators in more effectively monitoring and managing urban infrastructure [21]. ...
Musical cultural heritage, as an important component of cultural heritage, possesses significant cultural value and inheritance significance. With the development of society and the passage of time, these precious traditional musical cultural heritages inevitably face the dilemma of gradual depletion or even disappearance. In the digital age, effectively protecting and inheriting these musical cultural heritages has become an urgent problem to be addressed. Therefore, this paper proposes an application method based on digital twin technology, exploring how to protect and inherit musical cultural heritages through digital twin technology. By leveraging digital twin technology, a virtual museum dedicated to showcasing the richness and historical connotations of music cultures is created, preserving and simulating the soundscapes of historical music eras. Through the integration of audio archives, 3D modeling, and interactive displays, users can immerse themselves in the experience of historical music in the digital space. This paper evaluates the feasibility and cultural preservation value of this digital music history museum through the creation of music digital twin technology instances and user survey feedback and discusses the prospects of digital twins in the field of musical cultural heritage.
... To meet dimensional fidelity, real-time metrology capabilities are essential and may include structured light scanning. The concurrent use of dimensional assessment, physics-informed digital twins [28], and plastic deformation offers many important new manufacturing strategies. Examples include bending to correct shape and/or to induce beneficial residual stresses and local forging of materials to improve properties or meet dimensions. ...
Over the last few decades, globalization has weakened the US manufacturing sector. The COVID-19 pandemic revealed import dependencies and supply chain shocks that have raised public and private awareness of the need to rebuild domestic production. A range of new technologies, collectively called Industry 4.0, create opportunities to revolutionize domestic and local manufacturing. Success depends on further refinement of those technologies, broad implementation throughout private companies, and concerted efforts to rebuild the industrial commons, the national ecosystem of producers, suppliers, service providers, educators, and workforce necessary to regain a competitive, innovative manufacturing sector. A recent workshop sponsored by the Engineering Research Visioning Alliance (ERVA) identified a range of challenges and opportunities to build a resilient, flexible, scalable, and high-quality manufacturing sector. This paper provides a strategic roadmap for regaining US manufacturing leadership by briefly summarizing discussions at the ERVA-sponsored workshop held in 2023 and providing additional analysis of key technical and economic issues that must be addressed to achieve dynamic, high-value manufacturing in the USA. The focus of this presentation is on discrete manufacturing of production of structural components, a large subset of total manufacturing that produces high-value inputs and finished products for domestic consumption and export.
... The scientific and industrial communities identified the role of DTs to enable new interaction forms between physical and digital layers [20], in particular in relation with IoT and IIoT [21]. Traditionally DTs have been massively adopted in different machine-oriented production stages and use cases [5], in particular related to manufacturing [22], [23], [24] and product design [25]. Only recently the idea of adopting DTs to build a human replicas has been investigated both as general concept in [26] and in specific domains such as industrial training [27], health [28], fitness [29] and ambient assisted living [30]. ...
Industry 5.0 embodies the vision for the future of factories, emphasizing the importance of sustainable industrialization and the role of industry in society, through the key concept of placing the well-being of workers at the center of the production process. Building upon this vision, we propose a new paradigm to design human-centric industrial applications. To this end, we exploit Digital Twin (DT) technology to build a digital replica for each entity on the shop floor and support and augment interaction among workers and machines. While so far DTs in automation have been proposed for machine digitalization, the core element of the proposed approach is the Operator Digital Twin (ODT). In this scenario, biometrics allows to build a reliable model of those operator’s characteristics that are relevant in working contexts. Biometric traits are measured and processed to detect physical, emotional, and mental conditions, which are used to define the operator’s state. Perspectively, this allows to manage and monitor production and processes in an operator-in-the-loop manner, where not only is the operator aware of the state of the plant, but also any technological agent in the plant acts and reacts according to the operator’s needs and conditions. In this paper, we define the modeling of the envisioned ecosystem, present the designed DT’s blue-print architecture, discuss its implementation in relevant application scenarios, and report an example of implementation in a collaborative robotics scenario.
Note to Practitioners
—This paper was motivated by the problem of designing human-cyber-physical systems, where production processes are managed by concurrently taking into account operators, machines and plant status. This answers the needs of the novel Industry 5.0 paradigm, which aims to enhance social sustainability of modern factories. To this end, we propose an architecture based on digital twins that allows to develop a digital layer, detached from the physical one, where the plant can be monitored and managed. This allows the creation of a digital ecosystem where machines, operators, and the interactions among them are represented, augmented, and managed. We discuss how the proposed architecture can be applied to three relevant scenarios: remote training and maintenance, line operation and line supervision. Moreover, the implementation in a collaborative robotics scenario is presented, to provide an example of the proposed architecture can be implemented in industrial scenarios.
... Initially, DT was adopted as conceptual basis in the domain of aeronautics and aerospace [14]. But its potential to increase their competitiveness, productivity, and efficiency has been identified by many industries [15] resulting in the rapid development of this technique [16]. Due to its novelty, there was lack of clarity on what digital twin actually due to this vagueness in the definition it was difficult for the industries to adopt DT so in the year 2021 ISO published set of reference architecture for the digital twins in the Standard ISO: 23247. ...
Robotics & Automation book series aims at a key theme on Automation, Robotics and Applications. It is intended to provide a stimulating forum for researchers, scientists, engineers and practitioners to publish their latest research findings, ideas, developments and applications in all aspects of automation, robotics and sensors. The subjects include Adaptive Control Systems, Mobile and Autonomous Systems, Agriculture and Field Robotics, Robotics and Industrial Monitoring, Artificial Neural Networks in robotics or automation, etc, and their applications. More and more research will be centred on building robots and automation systems that can make a difference in the quality of human life. The days are not far when humanoid robots will be common in many homes and offices.
... When considering the communication between the physical and the virtual object, three main categories, i.e., digital model, digital shadow and digital twin, have been proposed (Kritzinger, Karner, Traar, Henjes, & Sihn, 2018). These systems differ in how the information traverses between the virtual object and the physical object that it represents. ...
Meeting food safety requirements without jeopardizing quality attributes or sustainability involves adopting a holistic perspective of food products, their manufacturing processes and their storage and distribution practices. The virtualization of the food supply chain offers opportunities to evaluate, simulate, and predict challenges and mishaps potentially contributing to present and future food safety risks. Food systems virtualization poses several requirements: (1) a comprehensive framework composed of instrumental, digital, and computational methods to evaluate internal and external factors that impact food safety; (2) nondestructive and real-time sensing methods, such as spectroscopic-based techniques, to facilitate mapping and tracking food safety and quality indicators; (3) a dynamic platform supported by the Internet of Things (IoT) interconnectivity to integrate information, perform online data analysis and exchange information on product history, outbreaks, exposure to risky situations, etc.; and (4) comprehensive and complementary mathematical modeling techniques (including but not limited to chemical reactions and microbial inactivation and growth kinetics) based on extensive data sets to make realistic simulations and predictions possible. Despite current limitations in data integration and technical skills for virtualization to reach its full potential, its increasing adoption as an interactive and dynamic tool for food systems evaluation can improve resource utilization and rational design of products, processes and logistics for enhanced food safety. Virtualization offers affordable and reliable options to assist stakeholders in decision-making and personnel training. This chapter focuses on definitions and requirements for developing and applying virtual food systems, including digital twins, and their role and future trends in enhancing food safety.
... It is related to the transition from intelligent production based on knowledge The approaches to using simulation models as a DT proposed in the literature, including, in particular, the references indicated above, obviously differ in scope and functionality, both with respect to the model itself and the scope and direction of data exchange between data sources and the simulation model. Depending on the possibility of updating the state of the simulation model objects and the direction of data exchange in [16], the authors divided digital mappings into three categories of digital representation of physical objects (a classification of digital twins): ...
One of the goals of developing and implementing Industry 4.0 solutions is to significantly increase the level of flexibility and autonomy of production systems. It is intended to provide the possibility of self-reconfiguration of systems to create more efficient and adaptive manufacturing processes. Achieving such goals requires the comprehensive integration of digital technologies with real production processes towards the creation of the so-called Cyber–Physical Production Systems (CPPSs). Their architecture is based on physical and cybernetic elements, with a digital twin as the central element of the “cyber” layer. However, for the responses obtained from the cyber layer, to allow for a quick response to changes in the environment of the production system, its virtual counterpart must be supplemented with advanced analytical modules. This paper proposes the method of creating a digital twin production system based on discrete simulation models integrated with deep reinforcement learning (DRL) techniques for CPPSs. Here, the digital twin is the environment with which the reinforcement learning agent communicates to find a strategy for allocating processes to production resources. Asynchronous Advantage Actor–Critic and Proximal Policy Optimization algorithms were selected for this research.
... IoT technologies and machine learning algorithms are used in Industry 4.0 to intelligently collect and analyze data [57]. Data sources includes raw material, machine, and customer information (e.g., information about sales or complaints). ...
This book series will be the great platform to share ideas and knowledge among the Industry experts, researchers and academics. Several current and upcoming frontier technologies, innovative solutions, research results, as well as enterprises related to internet of things and their applications will be published.
... In the landscape of industrial automation technology, Digital Twins emerge as a pivotal innovation, enabling the efficient implementation of various use cases of digital manufacturing such as remote monitoring, predictive analytics or simulating future behavior [1,2]. The concept of Digital Twins has revolutionized the way production systems can be perceived, monitored and optimized [3]. ...
The ongoing digitization of the industrial sector has reached a pivotal juncture with the emergence of Digital Twins, offering a digital representation of physical assets and processes. One key aspect of those digital representations are simulation models, enabling a deeper insight in the assets current state and its characteristics. This paper asserts that the next evolutionary step in this digitization journey involves the integration of intelligent linkages between diverse simulation models within the Digital Twin framework. Crucially, for the Digital Twin to be a scalable and cost-effective solution, there is a pressing need for automated adaption, (re-)configuration, and generation of simulation models. Recognizing the inherent challenges in achieving such automation, this paper analyses the utilization of knowledge graphs as a potentially very suitable technological solution. Knowledge graphs, acting as interconnected and interrelated databases, provide a means of seamlessly integrating different data sources, facilitating the efficient integration and automated adaption of data and (simulation) models in the Digital Twin. We conducted a comprehensive literature review to analyze the current landscape of knowledge graphs in the context of Digital Twins with focus on simulation models. By addressing the challenges associated with scalability and maintenance, this research contributes to the effective adaption of Digital Twins in the industrial sector, paving the way for enhanced efficiency, adaptability, and resilience in the face of evolving technological landscapes.
... Since then, various attempts have been made to define the DT, but no prevalent definition exists in academia. Most researchers are of one opinion regarding the DT: It consists of three fundamental components, namely the physical object, the virtual replica and the data exchange between the two (Kritzinger et al., 2018;Tao et al., 2018;Wright & Davidson, 2020). In recent research, DTs are applied to various manufacturing industries and furthermore to the healthcare sector, agriculture and smart cities (Singh et al., 2021). ...
The benefits of Information Systems for sustainability are praised with solutions to climate change becoming more urgent. The sustainability effects of newcomers in this field, such as Digital Twins, have not yet been studied holistically, which could conceal potential harmful effects. Therefore, we aim to investigate the sustainability effects of Digital Twins based on a systematic literature review. We reviewed 26 case studies and categorized the effects of Digital Twins in the three dimensions of sustainability. We show that Digital Twins indeed can have positive sustainability effects, such as reducing carbon emissions or energy consumption. However, none of the studies measured the resource consumption associated with Digital Twins, which makes a conclusive sustainability assessment difficult. Besides synthesizing the effects, this review comprises an overview of the applications of Digital Twins in the context of sustainability. Lastly, we identify research gaps which lead to various implications for theory and practice.
... The Digital Twin (DT) provides substantial efficiency benefits for various use cases (UCs) of digital manufacturing, including digital production forecasting, optimization, and troubleshooting [1,2]. However, creating a DT in an efficient way remains a key challenge [3]. ...
Creating behavior models for Digital Twins manually requires significant effort, which hinders their widespread use. One possible solution involves using automated approaches to create behavior models. There are several approaches available in literature. Different articles assume that automated creation of behavior models is superior to manual creation, but to provide comprehensive proof, an expert benchmark is necessary. This article conducts such a benchmark with 10 experts setting up 9 different behavior models. The recorded times are compared with an automated method for creating behavior models. The manual creation takes up to 54 times more time compared to the automated method.
... The use of a Digital Twin (DT) can significantly improve the efficiency of automated production systems. This is achieved through digital production forecasting, optimization, and troubleshooting [1][2][3]. However, automating the creation of a DT remains essential due to the challenge it poses [4,5]. ...
There are several approaches in the literature for the low-effort creation of behavior models. Some of these approaches use a library that must be created in advance. This article presents a method for creating such a library of behavior models with minimal effort. Different sources of information can be used with appropriate pre-processing. Behavior models for a library are constructed from the basic building blocks of each discipline using an existing method. The behavior models of each discipline can be assigned to different modelling depths.
... SEAMS '24, April 15-16,2024, Lisbon, AA, Portugal © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0585-4/24/04 https://doi.org/10.1145/3643915.3644108 the physical system using actuators (e.g., controllers) [11]. Digital twins can be used for descriptive ("what happened"), predictive ("what will happen") and prescriptive ("what if") analyses [21] of the twinned physical system, by exploring its models. ...
... However, the term DT encompasses a multitude of meanings in the academic and industrial sphere [51]. For the present work, the definition to be used in the following is derived from the work of Kritzinger et al. [52]. The authors presented a three-term methodology to differentiate between the various applications of DTs. ...
Additive manufacturing processes are prone to production errors. Specifically, the unique physical conditions of Laser-Directed Energy Deposition (DED-L) lead to unexpected process anomalies resulting in subpar part quality. The resulting costs and lack of reproducibility are two major barriers hindering a broader adoption of this innovative technology. Combining sensor data with data from relevant steps before and after the production process can lead to an increased understanding of when and why these process anomalies occur. In the present study, an IoT-based data mining framework is presented to assess the stability of processing Ti6Al4V on an industrial-grade DED-L machine. The framework employs an edge-cloud computing methodology to collect data efficiently and securely from various steps in the part lifecycle. During manufacturing, multiple sensors are employed to monitor the essential process characteristics in situ. Mechanical properties of the 160 printed specimens were obtained using appropriate destructive testing. All data are stored on a central database and can be accessed via the web for data analytics. The results prove the successful implementation of the proposed IoT framework but also indicate a lack of process stability during manufacturing. The occurring part errors can only be partially correlated with anomalies in the in situ sensor data.
Navigating the industrial landscape of 2024,
manufacturing companies grapple with multifaceted
challenges arising from the persistent impacts
of the COVID-19 pandemic, global conflicts, and
demographic shifts. This report introduces the
NEONEX Industrial Trend Tree, serving as a visual
roadmap for strategically preparing businesses for
the dynamic landscape of 2024 and beyond. The
Industrial Trend Tree for this year encompasses
five core trend branches: Connected Supply
Chain, Data Analytics, Robotics & Automation,
Smart Factory, and Sustainability. Each core trend
branch incorporates three significant key trends
organized by their expected adoption timeline.
In the current year, data-driven value stream
design stands out as a low-hanging fruit, with an
implementation time of one year or less and a
high transformational impact. Over the horizon of
one to three years, product lifecycle management
emerges as a crucial and impactful consideration
for manufacturing companies. Looking ahead to
the next three to five years, trends such as end-toend
object traceability, holistic factory digital twins,
artificial intelligence-based end-to-end product
development, autonomous dark factories, and
circular economy and lifecycle practices promise
transformational changes in operational paradigms.
The timely growth of data collection and virtual technology has led to advancements in digital twin (DT) technology, which has since become one of the primary research areas and provides enormous possibilities for various industry fields. Based on ultra-fidelity models, DT is an efficient method for realizing the merging of physical and virtual environments. Scholars investigated associated theories and modeling techniques, critical technologies for realizing continuous links and communication between physical and virtual entities, and other researchers concentrated on providing frameworks for the practical application of DT. This article aims to summarize common industrial cases to determine the present status of DT research. This paper discusses the concept of DT technology, the properties that define it, and the DT framework used in the industry. It also reviews the DT application and the findings of associated studies. Some present challenges in DT development, and potential DT prospects are discussed. Finally, we utilize the 6-degrees-of-freedom parallel robot as a case study to illustrate a future application of DT.
The correlation between Lean Six Sigma performance strategy (LSS) and contemporary innovations, including Digital Twin (DT) technology (DLSS) has been scrutinized in this study. The merger's mission is to overcome some of the organizational tensions. Environmentally speaking, it's critical to cut waste and save resources. On a social level, demonstrated boosts to labor and machine efficiency through scrutinizing analysis, simulation, and virtual-physical replication. Economically speaking, improved quality, lower prices and lead times, continuous improvement, and performance enhancement techniques are all part of the worldwide manufacturing scene. Actually, harnessing the suitable DT application amongst available applications is a noteworthy procedure. Hence, this study evaluates DT applications by constructing a decision framework. The constructed framework depends on decision methodologies of multi-criteria decision-making framework (MCDM) and the vague theory of type 2 neutrosophic sets (T2NSs). The entropy and Complex Proportional Assessment (COPRAS) of MCDM are working in cooperation with T2NSs to recommend optimal DT application which merges with LSS toward sustainability of the organization's industrial.
En el contexto del metaverso, los sistemas de captura digital 3D pueden desempeñar un papel crucial para dar vida a los avatares, personajes y escenografías dentro de entornos virtuales.
This paper presents a literature review on methods for enabling real-time analysis in digital twins, which are virtual models of physical systems. The advantages of digital twins are numerous, including cost reduction, risk mitigation, efficiency enhancement, and decision-making support. However, their implementation faces challenges such as the need for real-time data analysis, resource limitations, and data uncertainty. The paper focuses on methods for reducing computational demands, which have not been systematically discussed in the literature. The paper reviews and categorizes methods and tools for accelerating the modeling of physical phenomena and reducing the computational needs of digital twins.
In this paper, we present an example of complete integration of eXtended Reality technologies within a demonstration laboratory showcasing Industry 4.0/5.0 compliant machinery in realistic scenarios of use. We describe the design choices and the implementation of the augmented and virtual reality applications developed and potentially usable to support different real-world tasks, featuring advanced gesture-based interaction modes. We also describe the optimized communication architecture used to synchronize data between the cyber-physical factory environment with all its components, its industrial digital twin, and the augmented and virtual replica of the factory.
Example tasks supported with the tools in public demonstrations allow users wearing Microsoft HoloLens 2 or Meta Quest 2 headsets to monitor the status of the prototype production line and operate on it, locally or remotely.
An example video showing the applications is available in the supplementary material.
The digital transformation of organizations in the industrial sector is primarily driven by the opportunity to increase productivity while simultaneously reducing costs through integration into a cyber-physical system. One way to fully tap the potential of a cyber-physical system is the concept of the digital twin, i.e., the real-time digital representation of machines and resources involved – including human resources. The vision of representing humans by digital twins primarily aims at increasing economic benefits. The digital twin of a human, however, cannot be designed in a similar way to that of a machine. The human digital twin shall rather enable humans to act within the cyber-physical system. It therefore offers humans a power of control and the opportunity to provide feedback. The concept of the digital twin is still in its infancy and raises many questions in particular from an educational perspective. The contribution aims at answering the following questions and refers to the example of team learning: Which and how much data should and may the digital twin contain in order to support humans in their learning? To what extent will humans be able to control and design their own learning? How may skills, experiences, and social interactions of humans be represented in the digital twin; their growth and further development, respectively? With cyber-physical systems transcending corporate, national, and legal boundaries, what learning culture will be the frame of reference for the involved organizations?
Digital Twin (DT) technology has seen an explosion in popularity, with wind energy no exception. This is particularly true for Operations & Maintenance (O&M) applications. However, this expanded use has been accompanied by loose, conflicting, definitions that threaten to reduce the term to a buzzword and prevent the technology from meeting its full potential. A number of attempts have been made to better define and classify DTs, however, these either oversimplify the term or tighten criteria, leading to the exclusion of many DT applications. A new definition framework dubbed the Digital Twin Family Tree is therefore proposed. This widens “Digital Twin” to a general umbrella term for the technology, accompanied by specific definitions. DT Tags are also used to provide individualised characteristics for implementations. A sector-specific definition was devised for component and system monitoring and predictions in wind energy O&M dubbed a CS-DT and suitable DT Tags created. The proposed framework was used to review existing research in literature, demonstrating the potential for increased understanding, explainability, and accessibility of DTs for expert and non-expert stakeholders.
The objective of this chapter is to describe the strategic action line related to digital technologies for production processes and systems (LI7). In particular, this chapter proposes research and innovation priorities aimed at innovative digital architectures for the monitoring, control and management of manufacturing activities and related assets, modelling new products/services and production processes, use of Al, Big data and adequate Cybersecurity systems. More specifically, the research and innovation priorities are based on the assumption that criteria need to be defined for the management and transformation of raw production data into strategic information for decision makers, identifying the information to be collected from each digital access point by means of suitable enabling technologies and then delivered as appropriate. Digital platforms and cybersecurity also play a significant role in the definition of dynamic supply chain models.
The aim of this chapter is to analyse and compare European, Italian and regional industrial policies aimed at promoting the research and innovation activities, with focus on manufacturing sector. The analysis is based on secondary data collected from websites, documents issued by related governmental bodies and grey literature which are compared along scientific topics of interest. Moreover, the chapter discusses how these policies are expected to have an impact on industrial competitiveness and how these policies are interconnected each other. A comparative analysis of the regional and national priorities is also proposed as the result of an iterative collaboration with regional actors. The chapter closes with the analysis of the role of the cluster in supporting industrial policies.
The objective of this chapter is to describe the strategic action line related to high efficiency and zero defect production (LI4). In particular, this chapter proposes research and innovation priorities aimed at studying models for efficiency in terms of: zero-defect technologies designed to reduce non-conformances, monitoring processes during the various phases like quality management, maintenance and internal logistics of a manufacturing system, upgrading and improving the capacity of equipment and industrial goods; robustness/flexibility as the capacity to face disruptions, due to the precarious supply of incoming materials and parts, and to the specific properties of the material (anisotropy, low rigidity, etc.); smart systems for optimized use of available resources (equipment, human operator, knowledge) and for the control and management of production systems through models (CPS, empirical models, etc.).
When creating a roadmap, it is important to contextualise the sector under consideration. The work in this chapter is based on identification of relevant indicators which are analysed with a comparative approach both along the time horizon and with other countries and sectors. For this reason, this chapter is based on the extraction of data from International, European, national and regional dataset and describes the Italian manufacturing industry, exploring which are the most relevant sectors, which is the position comparing with European and international countries, and a focus is made on the machine tools sector. The system competitiveness is also analysed in terms of capability to bring innovation to Italy and to the sustainable development goals. The chapter closes with an analysis of the reaction of manufacturing to disruptions like the pandemic crisis and a proposal for a systemic recovery.
Way beyond its industrial roots, robotics evolved to be a highly interdisciplinary field with a variety of applications in a smart world. The eRobotics methodology addresses this evolution by providing platforms where roboticist can exchange ideas and collaborate with experts from other disciplines for developing complex technical systems and automated solutions. Virtual Testbeds are the central method in eRobotics, where complex technical systems and their interaction with prospective working environments are first designed, programmed, controlled and optimized in simulation before commissioning the real system. On the other hand, Industry 4.0 concepts promote the notion of “Digital Twins”, virtual substitutes of real world objects consisting of virtual representations and communication capabilities making up smart objects acting as intelligent nodes inside the internet of things and services. Combining these two approaches, Virtual Testbeds and Digital Twins, leads to a new kind of “Experimentable Digital Twins” breaking new ground in the simulation-based development and operation of complex technical systems. In this contribution, we describe how such “Experimentable Digital Twins” can act as the very core of simulation-based development processes streamlining the development process, enabling detailed simulations at system level and realizing intelligent systems. Besides this, the multiple use of models and simulations in various scenarios significantly reduces the effort for the use of simulation technology throughout the life cycle of complex technical systems.