Using Ptolemy II as a Framework for Virtual Entity Integration and Orchestration in Digital Twins

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

Digital twin technology is the talking point of academia and industry. When defining a digital twin, new modeling paradigms and computational methods are needed. Developments in the Internet of Things and advanced simulation and modeling techniques have provided new strategies for building complex digital twins. The digital twin is a virtual entity representation of the physical entity, such as a product or a process. This virtual entity is a collection of computationally complex knowledge models that embeds all the information of the physical world. To that end, this article proposes a graph-based representation of the virtual entity. This graph-based representation provides a method to visualize the parameter and their interactions across different modeling domains. However, the virtual entity graph becomes inherently complex with multiple parameters for a complex multidimensional physical system. This research contributes to the body of knowledge with a novel graph-based model reduction method that simplifies the virtual entity analysis. The graph-based model reduction method uses graph structure preserving algorithms and Dempster–Shaffer Theory to provide the importance of the parameters in the virtual entity. The graph-based model reduction method is validated by benchmarking it against the random forest regressor method. The method is tested on a turbo compressor case study. In the future, a method such as graph-based model reduction needs to be integrated with digital twin frameworks to provide digital services by the twin efficiently.

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.

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.

Industrial robots can be mechanical intelligent agents by integrating programs, intelligent algorithms and facilitating intelligent manufacturing models from cyber world into physical entities. After introducing the concept of cloud, their storage, computing, knowledge sharing and evolution capabilities are further strengthened. Digital twin is an effective means to achieve the fusion of physics and information. Therefore, it is feasible to introduce the digital twin to the industrial cloud robotics (ICR), in order to facilitate the control optimization of robots’ running state. The traditional manufacturing task-oriented service composition is limited to execution in the cloud, and it is separated from the underlying robot equipment control, which greatly reduces the real-time performance and accuracy of the underlying service response, such as Robotic Control as a Cloud Service (RCaaCS). Therefore, this paper proposes a digital twin-based control approach for ICR. At the manufacturing cell level, robots’ control instruction service modeling is conducted, and then the control service in the digital world is mapped to the robot action control in the physical world through the concept of digital twin. The accumulated operational data in the physical world can be fed back to the digital world as a reference for simulation and control strategy adjustment, finally achieving the integration of cloud services and robot control. A case study based on workpiece disassembly is presented to verify the availability and effectiveness of the proposed control approach.

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].

A closed-loop digital twin modeling method integrated with carbon footprint analysis

A digital twin (DT) is an electronic replica of a real-world item. It is built on top of asset-specific data items and is often enhanced using semantic technologies and simulation environments. The DT lays the way for anything from routine monitoring to hands-off administration of a physical entity. With the development of the metaverse concept of DT gains importance. As it helps to manage the physical entity in the metaverse. Therefore, it is beneficial to use DT for the detection and mitigation of different types of cyber attacks. In this context, we use the concept of DT for the identification and detection of DDoS attacks in the IoT network. Our proposed approach uses the concept of support vector machine (SVM) learning technique for the identification and detection of DDoS attacks. In our proposed approach, the DT of physical routers is stored in the metaverse, which makes it secure against any type of physical attack. Our proposed approach detected the malicious packets with an accuracy of 93.25% accuracy.

Digital manufacturing aims to make manufacturing more productive, resilient, and competitive by improving production efficiency and enhancing product quality. To achieve this, this paper proposes a novel digital twin framework for representing complex industrial machines, materials, and products in manufacturing production lines. In the framework, digital twins comprise a physical twin, a virtual twin, and digital threads interconnecting these. The physical twin incorporates relevant physical entities in manufacturing production lines, such as production machines, a material, or a product, as well as additional attached sensors needed for measuring the properties of the physical twin. The virtual twin, which contains the description of physical twin, such as the product’s properties, and AI models use the measurements collected from the production machine or the sensors in the physical twin to optimize production efficiency and ensure products consistency/quality. The digital threads provide for bidirectional communication using industrial protocols between the physical and virtual entities, and also between the AI model(s) and the orchestration component of the virtual twin. The paper also proposes a digital twin-based platform for digital manufacturing. The platform supports the creation and lifecycle management of digital twins for the production machines, materials, and products in each production line. In addition, the platform for digital manufacturing supports the development and management of digital manufacturing solutions that enhance the productivity and resiliency of entire production lines. The paper presents a case study from the composite airframe manufacturing sector that includes a sample framework-based implementation of a digital twin of an airframe part product. This product digital twin incorporates sensors that measure the temperature and viscosity of the composite product and AI model that used these real-time measurements to predict the product quality and reduce its curing time, ensuring both the product quality and production efficiency.

The mass digitization efforts associated with the e-COL+ project, focusing on French natural history collections, provide an invaluable opportunity to reassess the need to build a comprehensive 3D digital collection to complement existing 2D digital repositories. This article explores the importance of incorporating 3D data into digital collections, highlighting its significant impact on scientific research, conservation efforts, and public outreach campaigns. Although the e-COL+ project faces practical obstacles related to digitization, it also raises important theoretical questions about the complex interplay between physical and digital objects. Here, we challenge the concepts of digital clone and digital twin that are commonly used in the construction of digital collections. The digital twin concept more accurately captures the intricate and adaptive connection between physical and digital entities that results from the precise digitization techniques used and the evolving characteristics of the specimens found in natural history collections.

Metadata schema for virtual building models in digital twins: VB schema implemented in GPT-based applications

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.

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.

Supersystem digital twin-driven framework for new product conceptual design

Geometric parameter updating in digital twin of built assets: A systematic literature review