Principles and application areas of the digital twin concept
Digital Twins ( English: Digital Twins, alias: Cyber-Physical System) is the full use of physical models, sensor updates, operational history and other data, integration of multidisciplinary, multi-physics, multi-scale, multi-probability simulation process, in the virtual space to complete the mapping, so as to reflect the corresponding physical equipment of the whole life cycle process. The digital twin is a way to go beyond reality. The digital twin is a concept that transcends reality and can be seen as a digital mapping system of one or more significant, interdependent equipment systems.
The digital twin is a universally adaptable theoretical and technical system that can be applied in many fields, including product design, product manufacturing, medical analysis and engineering construction. The most intensive application in China is in the field of engineering and construction, and the most attention and research is in the field of intelligent manufacturing.
Digital twin catalogue in this paper
I、Digital twin concept and meaning
II、The principle of digital twin
III、The basic components of digital twin
IV、The meaning and role of the digital twin
V、The development process of digital twin
VI、Status of research on digital twin
VII、Application scenarios of digital twin
VIII、Digital twin and Digital Thread
IX、Digital twin standard system
I. Concept and meaning of digital twin
The US Department of Defense first proposed the use of Digital Twin technology for aerospace vehicle health maintenance and assurance. The first step is to create a model of a real aircraft in digital space and synchronise it with the real state of the aircraft through sensors, so that after each flight, the existing condition of the structure and past loads can be analysed in time to assess whether repairs are needed and whether it can withstand the next mission load.
The digital twin, sometimes also used to refer to taking a factory plant and production line and completing a digital model of it before it is built. The factory is thus simulated and emulated in a virtual cyberspace and the real parameters are passed on to the actual factory construction. And once the plant and production line are built, the two continue to interact with each other in daily operations and maintenance. It is important to note that Digital Twin is not a configuration management tool, not a 3D dimensional model of a finished product, not an MBD definition of a finished product.
The extreme demand for Digital Twin will also drive the development of new materials, and all anomalies that may affect the working condition of the equipment will be explicitly examined, evaluated and monitored. digital Twin integrates sensor data, historical maintenance data, and relevant derived data generated by mining from the embedded integrated health management system (IVHM). By integrating these data, Digital Twin can continuously predict the health of equipment or systems, their remaining service life and the probability of successful mission execution, as well as anticipate system responses to safety-critical events and reveal unknown problems in equipment development by comparing them with the system response of the entity. digital Twin may be able to mitigate damage or damage by activating self-healing mechanisms or suggesting changes to mission parameters to mitigate damage or perform system degradation, thereby increasing the probability of longevity and successful mission execution.
II. The principle of Digital Twin
The idea of the digital twin was first named the "Information Mirroring Model" by Michael Grieves of the University of Michigan, and later evolved into the term "digital twin". The term "digital twin" has evolved. Digital twins are also known as digital twins and digital mappings. The concept of digital twin is described by NASA in 2012 as: Digital twin is the integration of multidisciplinary, multi-scale simulation processes that make full use of physical models, sensors, operational history and other data. It serves as a mirror image of a physical product in virtual space, reflecting the full life cycle of the corresponding physical product. To facilitate the understanding of digital twin, Zhuang Cunbo et al. proposed the concept of digital twin, which is considered as the process of digitally defining and modelling the composition, characteristics, functions and performance of a physical entity using information technology. A digital twin is an information model that exists in a computer virtual space that is fully equivalent to a physical entity, and can be used for simulation analysis and optimisation of the physical entity based on the digital twin. The digital twin is the technology, the process, the method, and the digital twin is the object, the model and the data.
In the 21st century, both the US and Germany have proposed the Cyber-Physical System (CPS), or "information-physical system", as a core support technology for advanced manufacturing. the goal of CPS is to achieve the interactive integration of the physical world and the information world. Through big data analysis, artificial intelligence and other new-generation information technology, simulation and prediction in the virtual world can drive the operation of the physical world with optimal results. The essence of the digital twin is the mapping of the information world to the physical world, so the digital twin is a better interpretation of CPS and the best technology to achieve CPS.
III. The basic components of the digital twin
In 2011, Professor Michael Grieves in "Almost Perfect: Driving Innovation and Lean Products through PLM" gave three components of the digital twin: the physical product in physical space, the virtual product in virtual space, and the interface for data and information interaction between physical and virtual space.
At the Siemens Industry Forum 2016, Siemens argued that the components of the digital twin include: the digital twin of the product, the digital twin of the production process, and the digital twin of the equipment, and that the digital twin reproduces the entire enterprise in a complete and realistic way. Zhuang Cunbo from Beijing University of Technology also gave the main components of the digital twin from a product perspective, including: product design data, product process data, product manufacturing data, product service data, and product decommissioning and end-of-life data. Both Siemens and Zhuang Cunbo from Beijing University of Technology have presented the components of the digital twin from a product perspective, and Siemens is promoting its digital twin-related products in manufacturing companies based on its product lifecycle management (PLM) system.
Tang Tang et al. from Tongji University proposed that the components of a digital twin should include: product design, process planning, production layout, process simulation, yield optimisation, etc. The composition of this digital twin includes not only the design data of the product, but also the production process and simulation analysis of the raw product, which is more comprehensive and more in line with the requirements of a smart factory.
Tao Fei et al. of Beijing Aerospace first gave a definition of the workshop digital twin from the perspective of workshop composition, and then proposed the composition of the workshop digital twin, which mainly includes: physical workshop, virtual workshop, workshop service system, and workshop twin data. The physical workshop is a real workshop, which receives production tasks from the workshop service system and executes them according to the execution strategy optimised by the virtual workshop simulation; the virtual workshop is an equivalent mapping within the computer of the physical workshop, which is mainly responsible for the simulation analysis and optimisation of production activities, and the real-time monitoring, prediction and regulation of the production activities of the physical workshop; the workshop service system is a general term for all kinds of workshop The shop floor service system is the umbrella term for all types of software systems in the shop floor, mainly responsible for the shop floor digital twin driving the operation of the physical shop floor, and receiving production feedback from the physical shop floor.
IV. The significance and role of the digital twin
The most important inspiration of Digital twin is that it enables feedback from real physical systems to digital models in cyberspace. It is a feat of reverse thinking in the industrial world. One tries to take everything that happens in the physical world and stuff it back into digital space. Only full life tracking with loop feedback is a true full life cycle concept. In this way, the coherence between the digital and the physical world can be truly guaranteed in the full lifecycle context. Various types of simulation, analysis, data accumulation, mining and even artificial intelligence based on digital models ensure its applicability to real physical systems. This is what Digital twin means for intelligent manufacturing.
Intelligence in intelligent systems starts with perception and modelling, followed by analytical reasoning. Without Digital twin's accurate modelling of the real production system, the so-called intelligent manufacturing system is water without a source and cannot be implemented.
V. The development process of the Digital Twin
Many of the key technologies for realising Digital Twin have already been developed, such as multi-physical scale and multi-physical quantity modelling, structured health management, high performance computing, etc. However, realising Digital Twin requires the integration and fusion of several of these cross-disciplinary and cross-disciplinary technologies in order to effectively assess the health of equipment, which is a significant departure from the vision of individual technology development . It is therefore conceivable that a highly disruptive concept such as Digital Twin will be difficult to achieve sufficient maturity in the foreseeable future, making it particularly necessary to establish milestone targets for intermediate processes.
The US Air Force Research Laboratory's (AFRL) Spiral 1 programme, released in 2013, is an important step in this direction, with $20 million in commercial contracts with General Electric (GE) and Northrop Grumman to carry out this work. The programme uses the existing USAF-equipped F15 as a test bed, integrating the most advanced technology available with the actual capabilities currently available as a benchmark, thus identifying gaps that still exist in the virtual entity. Of course, for a concept as catchy and memorable as Digital Twin, many companies have been eager to pull it out of the highly sophisticated realm and into the public eye.
GE has made it a key concept in the Industrial Internet, seeking to provide a complete view of how machines in the physical world actually operate through the analysis of big data, while the radical PLM vendor PTC has made it a key part of its "smart, connected products": every movement of a smart product is brought back to the designer's desktop, enabling a real-time view. Digital Twin has suddenly given designers a new dream. It is guiding people through that wall of reality and imagination, freely interacting and walking between physical and digital models.
VI. State of the art in digital twin research
In the Apollo project, NASA used digital twins of space vehicles to simulate and analyse the flight status of space vehicles, monitor and predict their flight status, and assist ground controllers in making the right decisions. From NASA's application of the digital twin, the digital twin is mainly to create a virtual body or digital model equivalent to the physical entity. The virtual body can perform simulation analysis of the physical entity, monitor the operational status of the physical entity based on the real-time feedback information of the physical entity's operation, and improve the simulation analysis algorithm of the virtual body based on the operational data collected from the physical entity, so as to The simulation analysis algorithm of the virtual body can be improved based on the collected operational data of the physical entity, thus providing more accurate decisions on the subsequent operation and improvement of the physical entity.
Professor Michael Greaves of the University of Michigan introduced the concept of "digital representation of physical products" in 2003, stating that a digital representation of physical products should be able to represent physical products in an abstract way and be able to test physical products under real or simulated conditions based on the digital representation. This concept is not called a digital twin, but it has the composition and functionality of a digital twin, i.e. the creation of an equivalent virtual body of a physical entity, which is capable of simulating and testing the physical entity. The theory proposed by Professor Michael Greaves can be seen as an application of the digital twin to the product design process.
The National Institute of Standards and Technology (NIST) proposed the concepts of MBD (Model-Based Definition) and MBE (Model-Based Enterprise) in 2012. The core idea is to create digital models of enterprises and products, and the simulation analysis of digital models should be carried out throughout the entire product life cycle, including product design, product design simulation, machining process simulation, production process simulation, and product repair and maintenance. MBE and The concept of MBD extends the connotation of the digital twin to the entire product manufacturing process.
After 2015, countries around the world have proposed national-level strategies for transforming manufacturing. One of the core objectives of these strategies is to build a Cyber-Physical System (CPS), which enables the interaction and integration of the physical factory and the virtual factory with information technology, thus realising intelligent manufacturing. From the connotation of CPS and digital twin, they both aim to describe the state of integration of information space with the physical world. CPS is more inclined to the verification of scientific principles, while digital twin is more suitable for the optimization of engineering applications and more capable of reducing the cost of building complex engineering systems. Tao Fei and Zhang Meng from Beijing University of Aeronautics and Astronautics put forward the concept of digital twin workshop based on digital twin, and analyzed from the workshop management elements, the development of digital twin workshop needs to go through the three stages of production elements, production activities, production control limited to physical workshop in turn, the relative independence of physical workshop and digital twin workshop, and the interactive integration of physical workshop and digital twin workshop, before it can gradually mature. According to Tang Tang and Teng Lin of Tongji University, digital twin is the integration of the manufacturing process of an enterprise, the digitization of the entire process of product design to maintenance, the visualization of the production process through information integration, the formation of a closed loop from analysis to control and then to analysis, and the optimization of the entire production system. The most important value of the digital twin is prediction. When problems arise in the manufacturing process, the production strategy can be analysed based on the digital twin, and then production can be organised based on the optimised production strategy.
VII. Digital twin application scenarios
The application scenarios of digital twin from several aspects of product lifecycle management, engineering lifecycle management and workshop control systems are as follows:
1. At the earliest, NASA used digital twins to simulate and analyse space vehicles, detect and predict them, and assist ground controllers in making decisions.
2. Professor Michael Grieves and Siemens mainly use digital twins for full lifecycle management of product data. The digital twin is used to simulate and analyse product design, product functions, product performance, processing processes, repair and maintenance, etc.
3、The engineering and construction software suppliers represented by Autodesk apply digital twin technology to the construction of buildings, factories and infrastructures, treating buildings and infrastructures as products for full lifecycle management.
4. Tao Fei and others from Beijing University of Aeronautics and Astronautics applied digital twin to the construction and control of workshops, mainly involving digital twin-based product design, digital twin-based virtual prototyping, digital twin-based workshop rapid design, digital twin-based process planning, digital twin-based workshop production scheduling optimization, digital twin-based production logistics precise distribution, digital twin-based workshop equipment intelligent control, workshop human-machine interaction based on digital twin, assembly based on digital twin, testing/inspection based on digital twin, manufacturing energy management based on digital twin, product quality analysis and traceability based on digital twin, fault prediction and health management based on digital twin, product service system based on digital twin, etc.
VIII. Digital Twin and Digital Thread
Digital Twin is a concept that is both interrelated and distinct from Digital Thread.
Digital Twin is a digital representation of a physical product so that we can see what might happen to the actual physical product in this digital product. This is then reflected in the product definition model through an in-line digital inspection/measurement system and fed back into the simulation analysis model. By relying on Digital Thread, all data models can communicate in both directions, so that the state and parameters of the real physical product are fed back into the digital model via the CyberPhysics CPS, which is integrated with the intelligent production system, so that the digital model is consistent across all parts of the life cycle, enabling dynamic, real-time assessment of the current and future functionality and performance of the system. In turn, the data collected by the increasing number of sensors and machine linkages is interpreted and utilised during the operation of the equipment, allowing for the integration of later product manufacturing and operation and maintenance requirements into the early product design process, forming an intelligent closed loop for design improvement. However, it is not the creation of a full aircraft finite element model that gives a Digital Twin, that is only one angle of the problem; all real manufacturing dimensions must be fed back into the model in production, and then PHM (Health Predictive Management) is used to collect the actual forces on the aircraft in real time and feed back into the model before it can become a Digital Twin.
Digital Twin describes the model of each specific link connected through the Digital Thread. It can be said that Digital Thread is the result of the integration of the various components, together with intelligent manufacturing systems, digital measurement and inspection systems and Cyberphysics fusion systems. Through Digital Thread the models of the whole life cycle are integrated, these models are seamlessly integrated and synchronised with the actual intelligent manufacturing system and digital measurement and inspection system and furthermore with the embedded CyberPhysical Fusion System (CPS), so that we can see what might happen to the actual physical product on this digital product.
Digital Twin is a digital shadow of the physical product, integrated with external sensors to reflect Digital Twin is a digital shadow of the physical product, reflecting all the characteristics of the object from the micro to the macro level and showing the evolution of the product lifecycle. Of course, not only the product, but also the systems that produce it (production equipment, production lines) and the systems in use and maintenance must also be built up with Digital Twin on demand.
IX. Digital Twin standards system
The Digital Twin standard system can consist of the following parts:
1. Basic common standards: including terminology standards, reference architecture standards and applicable guidelines, which focus on the conceptual definition, reference framework, applicable conditions and requirements of Digital Twin and provide support for the entire standard system.
2. Digital twin key technology standards: These include physical entity standards, virtual entity standards, twin data standards, connectivity and integration standards and service standards, which are used to regulate the research and implementation of key digital twin technologies, ensure the effectiveness of key technologies in digital twin implementation, and break the technical barriers to collaborative development and module interchangeability.
3. Digital twin tool/platform standards: These include two parts: tool standards and platform standards, which are used to standardize the functions, performance, development, integration and other technical requirements of software and hardware tools/platforms.
4. Digital twin measurement and evaluation standards: including four parts: measurement and evaluation guidelines, measurement and evaluation process standards, measurement and evaluation index standards and evaluation use case standards, which are used to standardize the testing requirements and evaluation methods of the digital twin system.
5. Digital twin security standards: These include physical system security requirements, functional security requirements and information security requirements, and are used to standardize the technical requirements for the safe operation of personnel and the safe storage, management and use of various types of information in the digital twin system.
6. Digital twin industry application standards: considering the technical differences in the application of digital twin in different industries/fields and different scenarios, on the basis of the basic common standards, key technical standards, tools/platform standards, measurement and evaluation standards and security standards, the implementation of digital twin in specific industry applications such as machine tools, workshops and construction machinery and equipment will be regulated.