A Digital Twin Concept for Manufacturing Systems

Biopharma Is Going Digital … Bit by Bit

The current practice of Architecture, Engineering, and Construction (AEC) management systems relies on time-consuming, manual methods for data collection, analyzing, and decision-making. This affects the productivity of the industry in all phases: design, construction, operation, and maintenance of their assets. Adoption of innovative technologies is the key solution for the AEC industry to overcome the challenges they face and follow other reinvented industries. Although recent advancements have been proposed for more productive data acquisition and decision support by leveraging digital tools, Building Information Modeling (BIM), Internet of Things (IoT), and Artificial Intelligence (AI), the fragmented approach of adoption and the absence of a connected workflow is a barrier. Digital Twin (DT) is a digital presentation of a physical entity that replicates the status and behavior of its physical counterpart. It can help to improve overall system effectiveness and reliable data-informed decisions. However, the concept of DT and its key features is still not well-understood in the AEC industry. Therefore, this paper aims to review and investigate the twinning requirements and characteristics of DT and discuss its structure and potential solutions in terms of AEC infrastructure and buildings assets operations and maintenance systems. The DT concept, main characteristics, and critical considerations for the AEC sector are highlighted and classification for DT levels is introduced and discussed.

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

Migrating the industry and society into digital area have gradually been increased in recent years. Digitalization has made our life more efficient and faster. As a result of digitalization, the Fourth Industrial Revolution, also known as Industry 4.0, was born. The Fourth Industrial Revolution, like previous revolutions, aims to facilitate life and improve the quality of life. Two terms are especially important for digitalization. These terms are Industry 4.0 and Internet of Things (IoT). Industry 4.0 and the IoT focus on the networking and automation of devices both used human being and industry. Digitalization and IoT technologies play an important role in improving energy efficiency. Digital twin (DT) technology is one of the most important key of Industry 4.0 technologies under digitalization. DT represents a digital copy of a physical system or entity. Physical objects and DT models created interact with each other. DT technologies have a wide range from manufacturing to healthcare sectors and from maritime transportation to the heavy industry. DT provides many conveniences for industries to grow with digitalization. Meanwhile, DT draws attention with its development in the academic field. In this study, an overview of DT is presented, a DT roadmap is proposed, some algorithms emerging in the concept of DT for industrial processes are explained and a case study is analyzed. This roadmap shows different methods to be applied in order to make accurate forecasting for the future applications and sustainable manufacturing with DT. As a result, DT concept is thought to attract much attention in the near future.

Objective of the paper is to describe and present results of using a "Digital Twin" in Drilling Operations (Planning and Engineering, Training and Operational Support) in the last 10 years for Operators worldwide. The concept of Digital Twin was first introduced by Michael Grieves at the University of Michigan in 2003 through Grieves’ Executive Course on Product Lifecycle Management. Winning a Formula 1 race is no longer just about building the fastest car, hiring the bravest driver and praying for luck. These days, when a McLaren technology group races in Monaco or Singapore, it beams data from hundreds of sensors wired in the car to Woking, England. There, analysts study that data and use complex computer models to relay optimal race strategies back to the driver. The McLaren race crew and the online retailers both harness data and use algorithms to make reasonable projections about the future, Parris explains. The concept is called Digital Twin [1]. A Digital Twin contains information such as a piece of equipment or asset, including its physical description, instrumentation, data and history. A Digital Twin can be created for assets ranging from a well to a piece of equipment to an entire oilfield. For example, a subsea system could have a Digital Twin via a simulation model of a subsea system's components, including the blowout preventer, tiebacks, risers, manifolds, umbilical and moorings. Drilling and extracting simulations can determine whether virtual designs can actually be built using the machines available," GE said. "Last but not least, real-time data feeds from sensors in a physical operating asset are now used to know the exact state and condition of an operating-asset product, no matter where it is in the world"[2].

As the world faces increasing global challenges, innovation becomes crucial for creating a sustainable future. In the age of the Internet of Things (IoT) and Industry 5.0, emerging technologies such as Digital Twins and Extended Reality (XR) are showing promise in addressing these challenges. The article examines the concept of merging Digital Twins, which are virtual reproductions of actual assets, with technologies like Extended Reality (XR) and the Internet of Things (IoT), as well as its applications in optimizing processes, enhancing decision making, and decreasing waste in global enterprises. The convergence of digital twins with extended reality, IoT, and Industry 5.0 presents unprecedented opportunities for innovation in agriculture, healthcare, and industry. In healthcare, digital twins enable personalized treatments and simulations for surgical planning, while in agriculture, they optimize crop growth and resource utilization. In the industry, digital twins streamline production processes, predict maintenance needs, and reduce downtime. This comprehensive review delves into the present landscape, obstacles, and future prospects of digital twins, extended reality, and IoT in various industries, emphasizing the imperative of ongoing research and development to unlock the complete transformative potential of these technologies for a sustainable future in Industry 5.0.

As a key part of digital transformation, digital twin is an important concept for achieving smart manufacturing. However, there remains many challenges about the digital twin concept and how it can be implemented in real manufacturing systems, especially among small and medium-sized enterprises. Relevant standards that provide common terminology and implementation guidelines can help address these challenges. This paper discusses the concept, definitions, applications, and recent standardization efforts of digital twins. In particular, International Organization for Standardization (ISO)/Draft International Standard (DIS) 23247, Digital Twin Framework for Manufacturing, is introduced. The scenarios of three digital-twin use cases in manufacturing are explored in depth and the procedures of implementing such use cases according to the ISO/DIS 23247 framework are presented. The designs of these use case scenarios can be implemented at the National Institute of Standards and Technology (NIST) Smart Manufacturing Systems (SMS) Test Bed or on any manufacturing shop floor that has similar maturity level of digital transformation. In addition, the procedures of design and implementation of ISO/DIS 23247 can be used for developing additional applications of digital twins in manufacturing.

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

Chapter 3 - Five-Dimension Digital Twin Modeling and Its Key Technologies

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development.

Although the concept of Digital Twins has been present in the literature for more than two decades, it is only in recent years, thanks to technological advancements, that its implementation in multidisciplinary fields such as Industry 4.0, cybersecurity, healthcare, and next-generation communication networks has become feasible. However, it is still far from being a consolidated and regulated paradigm, primarily due to the presence of numerous unaddressed challenges, such as security, deployment, management, and standardization. This doctoral thesis focuses on the applicability of the digital twin paradigm to the specific context of communication networks, known as Network Digital Twins. This approach combines the characteristics and inherent requirements of the general paradigm with the specific challenges related to modeling communication networks. While technological advancements are facilitating the use and development of digital twin-based solutions, they are also reshaping the way networks are designed and deployed, thereby complicating the attainment of global solutions due to factors such as heterogeneity in network equipment or virtualization technologies. Although there are currently various approaches of the paradigm applied in areas such as the Internet of Things, augmented reality, healthcare, or unmanned autonomous vehicles, these cannot be directly extrapolated to the realm of communication networks due to the high dependence of modeling on the specific application context. Therefore, the main objective of this doctoral thesis is to contribute to the development of a methodology enabling the creation and deployment of network digital twins, considering the limitations and inherent challenges within this field. Through the analysis of the state of the art, the characteristics and requirements associated with Digital Twins (DT) are initially identified, followed by a review of the specific case of Network Digital Twins (NDT), focusing on the identification of unresolved needs and issues. As a result of this study, a methodology is proposed that seeks to standardize the creation and deployment of Network Digital Twins, as well as their integration with Physical Twins, through various phases. After formally defining the methodology, the first phase leads to the second contribution, which focuses on the problem associated with acquiring the necessary data for modeling both the topology and the behavior of network devices. This issue stems from the heterogeneity of equipment and virtualization technologies, as well as the various network topologies. In response to this, the design and development of the DANA system (Digital Agent for Network Data Acquisition) is proposed, capable of collecting data regardless of the type of end device or network in the physical twin. This system is a key component of the NDT ecosystem, present in both the physical twin environment and the digital twin environment after its creation. This allows, in subsequent phases, intervention on the twins to facilitate the joint coevolution of the ecosystem, a fundamental characteristic of the paradigm. In relation to the need to carry out the implementation of the proposed methodology and based on the enabling technologies reviewed during the study of the state of the art, the design and development of a platform capable is performe, allowing both the creation and deployment of the complete NDT ecosystem, such as the implementation of a bidirectional communication channel capable of guaranteeing both data collection and information exchange, in order to enable the joint co-evolution of both environments. RESUMEN Pese a que el concepto de Gemelos Digitales lleva presente en la literatura desde hace más de dos décadas, es en los últimos años, cuando gracias a los avances tecnológicos se está pudiendo llevar a cabo su implementación en campos multidisciplinares como la industria 4.0, ciberseguridad, salud o redes de comunicación de nueva generación. No obstante, aún está lejos de ser un paradigma consolidado y regulado, debido a la existencia de múltiples desafíos sin abordar, como la seguridad, el despliegue, la gestión o la estandarización. Esta tesis doctoral se enfoca en la aplicabilidad del paradigma de gemelos digitales al contexto específico de las redes de comunicación, conocido como Network Digital Twins. Este enfoque combina las características y requisitos inherentes al paradigma general con los desafíos específicos relacionados con el modelado de las redes de comunicación. Si bien los avances tecnológicos están facilitando el uso y desarrollo de soluciones basadas en gemelos digitales, también están transformando la forma en que se diseñan y despliegan las redes, lo que dificulta la obtención de soluciones globales debido a factores como la heterogeneidad en el equipamiento o en tecnologías de virtualización de red. Aunque actualmente existen diversas aproximaciones del paradigma aplicadas en áreas como el Internet de las Cosas, realidad aumentada, salud o vehículos autónomos no tripulados, estas no pueden extrapolarse directamente al ámbito de las redes de comunicación debido a la alta dependencia del modelado con el contexto específico de aplicación. Por tanto, el objetivo principal de esta Tesis Doctoral es contribuir al desarrollo de una metodología que permita la creación y despliegue de gemelos digitales de redes, considerando las limitaciones y desafíos inherentes a este campo. A través del análisis del estado del arte se identifican inicialmente las características y requisitos asociados a los Gemelos Digitales (DT), seguido por una revisión del caso específico de Gemelos Digitales de Red (NDT), con un enfoque en la identificación de necesidades y problemáticas no resueltas. Como resultado de este estudio, se propone una metodología cuyo objetivo es tratar de estandarizar, a través de diversas fases, la creación y despliegue de los gemelos digitales de red, así como su integración con los gemelos físicos. Tras definir formalmente la metodología, la primera fase conduce a la segunda contribución, centrada en la problemática asociada a la adquisición de los datos necesarios para el modelado tanto de la topología como del comportamiento de los equipos de la red. Esta problemática radica de la heterogeneidad de equipos y tecnologías de virtualización, así como las diversas topologías de red. En respuesta a esto, se propone el diseño y desarrollo del sistema DANA (Digital Agent for Network Data Acquisition), capaz de recolectar datos independientemente del tipo de dispositivo final o red del gemelo físico. Este sistema es un componente clave del ecosistema NDT, presente tanto en el entorno del gemelo físico como en el del gemelo digital tras su creación. Esto permite, en fases posteriores, la actuación sobre los gemelos para facilitar la coevolución conjunta del ecosistema, una característica fundamental del paradigma. En relación con la necesidad de llevar a cabo la implementación de la metodología propuesta y basado en las tecnologías habilitadoras revisadas durante el estudio del estado del arte, se realiza el diseño y desarrollo de una plataforma capaz de llevarla a cabo, permitiendo tanto la creación y despliegue del ecosistema NDT completo, como la implementación de un canal de comunicación bidireccional capaz de garantizar, tanto la recolección de datos como el intercambio de información, con el fin de posibilitar la coevolución conjunta de ambos entornos.

Since its first mentioning in the literature, the concept of Digital Twin has gained traction in both industry and academia. However, there are still many open challenges when applying Digital Twins to industry-scale use cases. Applying Model-Driven Engineering techniques to the creation and maintenance of Digital Twins (also referred to as Model-Driven Digital Twin Engineering) promises automation and consistency throughout the life cycle of a Digital Twin. The exemplar provided in this paper can be used to identify open challenges when it comes to Model-Driven Digital Twin Engineering, and to demonstrate how approaches can solve them. This exemplar applies Digital Twins to an indoor air quality management use case, where CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , temperature, and humidity values of rooms within a building are measured. These values can be used to derive actions to improve work productivity and reduce the risk for virus infections. We describe three applications that make use of this Digital Twin (i.e., runtime visualization, physical simulation, and ML-based predictions), and provide an online repository with the artefacts of this exemplar.

Hybrid knowledge based system supporting Digital Twins in the Industry 5.0

The concept of Digital Twin (DT) is increasingly getting attention due to its support for digital transformation as part of Industry 4.0. A core component of a DT is the virtual entity (VE) that is meant to ‘mirror’ the physical entity (PE). A particular challenge for developing this VE is the integration between the different models that typically make up such a VE. These models, covering different disciplines or aspects of a system, commonly are developed by different engineers and expressed in different formalisms given their strengths and weaknesses. This makes integration between these models and formalisms a challenge. Such integration has two aspects: the communication among the models, and their orchestration, which concerns the order of model step execution and data exchange. In this paper, we consider the suitability of Ptolemy II, an open source framework based on an actor-oriented structure, to implement such integration. We use the Ptolemy II framework to reproduce the implementation of a DT of an autonomous scaled-down truck, which originally required a more manual configuration and execution and was developed in a monolithic way. The results show that Ptolemy II can be used to implement different communication technologies as actors, and show the expressiveness of the orchestrators by reproducing the existing DT’s behavior. This shows Ptolemy II in principle to be suitable as an integration and orchestration tool for models in the context of DTs. As future work, we plan to apply the framework to more complex DTs using different communication technologies, so as to validate its broader suitability for VE orchestration in DTs.

The development and progress in information and communication technologies will transform traditional products into smart products and allow to offer novel smart services [1]. Herein, the digital twin (DT) concept is regarded as a key technology to create value with smart services [2]. Although the research and applications of DTs emerge continuously many concerns are to be scrutinized [3]. The lack of a shared conceptual framework for DTs with an unambiguous terminology [4] complicates cross-functional discussions. Therefore, a systematization of the main dimensions of DTs is proposed in the form of an ontology and a conceptual framework thereof derived. The research questions addressed in this paper are a) «Which dimensions are used to classify and structure DTs in academic literature?», b) «What are the fundamental differences or specifications within these dimensions?» and c) «How do these different specifications relate to each other?» The focus of the research is on the objective to find classification systematics that are a) representing the entire spectrum of DTs, b) universally valid in all DT related domains and c) applicable in research and practice. A systematic literature review on the relevant aspects of DTs was conducted and the findings iteratively advanced within workshop sessions with academic experts. DTs are considered as integrators of physical and digital worlds as well as internal and external value creation. Further, the creation of DTs requires per definition the use of digital data. Hence, the proposed ontology and conceptual framework for DTs include the following main dimensions to consider for every DT: Data resources, external value creation and internal value creation. The main subdimensions of the data resources are the data sources to obtain the data, the data categories and the data formats. The main subdimension of the external value creation are the attributes of the services as the basis of the value propositions, the level of smartness of the connected products and the actors on the different levels of the ecosystem. The main subdimensions of the internal value creation are the lifecycle phases of products, the product management levels and the different generations of both. The proposed ontology and conceptual framework support researchers and practitioners in positioning and structuring their intended DT activities and communicating them to internal and external stakeholders. The holistic view on the data resource dimension further allows to easily deduct the needed data for certain applications or deduct possible applications from already available data.