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Digital twins --- key drivers of digital and intelligent transformation in manufacturing


Digital twin - the

A key driver for the intelligent transformation of manufacturing

With the development of information technology and the advancement of manufacturing technology, people's material life is becoming richer and richer, while the demand for intelligent and personalised products is also increasing. How to improve production efficiency, shorten time-to-market, adopt a more flexible and flexible production model, improve resource and energy utilisation and respond quickly to intelligent, personalised and changing market demands are the main issues facing global industry today. In order to solve these problems, the world's major industrial powers have put forward their own strategic vision plans for intelligent manufacturing, such as Germany's "Industry 4.0", the United States "Manufacturing Renaissance Plan", and China's "Made in China 2025" strategy. China has proposed the "Made in China 2025" strategy. As one of the core technologies for building the metaverse, digital twin technology, together with industrial IoT, 5G communication, big data, cloud computing, artificial intelligence, 3D visualisation and a series of other technologies, can build virtual mirrors of real-world objects, simulate, emulate, predict and assist in decision-making in terms of geometry, physical model and behaviour, thus effectively solving the above problems and realising the vision of intelligent manufacturing. The digital twin

1. The origin of the digital twin

"The concept of the "twin" originated with NASA's Apollo program. The concept of the digital twin was introduced by Dr Michael Grieves in 2002 at a workshop at the University of Michigan and NASA. In his view, modern product systems, production systems and enterprise systems are essentially complex systems as their complexity increases. In order to optimise and predict the performance of complex systems, we need an observable digital model, a multi-physical field, comprehensive digital representation of a product, in order to facilitate the maintenance and reuse of digital information throughout the product's life cycle in the design, manufacturing, and operational processes, by analysing and mining the product or equipment's state data, sensor data, and operational history data to enable state diagnosis, behavioural prediction, and intelligent scheduling. In addition, through the accumulation of database instances, industrial big data analysts can evaluate specific series of equipment and its components and feed back to product and process designers for continuous improvement of products and processes, creating a closed-loop digital twin. It was not until 2010 that the term 'Digital Twin' was officially introduced in a NASA technical report; in 2012, NASA and the US Air Force jointly published a paper on digital twins for future vehicle development. 2015 - 2020 is the nascent period for digital twin applications In 2012, NASA and the US Air Force jointly published a paper on digital twin applications for future aircraft development. In the past two years, digital twin has entered a period of rapid development. Digital twin is integrated with AI, AR/VR and other emerging technologies and is widely used in various industries.

2. Typical features of the digital twin

The essence of the digital twin is information modelling, which aims to build digital models of real-world physical objects in the digital virtual world. However, the information modelling involved in the digital twin is no longer based on the traditional underlying information transmission format, but is an overall abstract description of the external form, internal mechanisms and operational relationships of physical objects. The difficulty and effectiveness of the application are exponential compared to traditional modelling, mainly because the digital twin can have multiple variants, i.e. different forms of digital models can be built according to different applications and scenarios.