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Digital Twin Technology

A plethora of new technologies have emerged over the last decade that influence the future of many industries, and many have played key roles in the advancement of many such as manufacturing. “Digital twins” first came about around at NASA: full-scale mockups of early space capsules, 20 years ago in the context of manufacturing and product lifecycle management (PLM). Since it's development and conceptualisation has attracted other industries that hope to reap some of the many benefits it offers, including urban planning, healthcare and construction. Many companies have stated that among the industrial internet of things and smart manufacturing technologies, Digital Twins are to play a pivotal role in the future of the manufacturing industry.

While the concept of the digital twin is not new, it is not simple either, its primary goal is to create a digital twin that represents the product and production systems, this involves merging virtual engineering models with the physical product or equipment in an environment that allows for change to the as-designed and as-built product, essentially it is a collection of digital data that represents a physical object.

The technology behind digital twins has expanded hugely since its inception and can now include large items such as buildings, factories was well as cities, and reports are being made to say that even people and processes can have digital twins. The concept and popularity of the term gained real popularity and exposure when Gartner included Digital Twins in its Top Strategic Technology Trends in 2017, stating that within just 3 to 5 years “billions of things will be represented by digital twins, a dynamic software model of a physical thing or system".

“with an estimated 21 billion connected sensors and endpoints by 2020, digital twins will exist for billions of things in the near future." - Gartner, Top Strategic Technology Trends 2018

Developers are able to research the physics that underlie the physical object or system to be mimicked and use that data in order to develop a mathematical model that simulates the real-world original in a digital space. The model is so comprehensive that it can actually receive input from sensors gathering data from a real-world counterpart, which allows it to simulate the actual object in real time, creating a huge range of potential uses for the technology. As a result, it can offer insight into performance predictions and even potential issues, but the twin can also be used to offer insight in the products development process if it is based off of a prototype, or can even be used as a prototype for the physical version.

Digital twins have a huge range of potential uses across many industries, in the automotive industry digital twins can be used to help model and predict performance and issues, as well as during planning stages, the technology can help designers and engineers model the car's form, as well as simulating its function and assess the ease of manufacturing. Automotive digital twins are really made possible because most cars are already fitted with sensors, but the future development and refinement of DT technology will become more important, especially with the expected rise in autonomous vehicles on the road. During a cars' operation, it will allow for closer monitoring, and while data from multiple products is collected, operators will be able to gain a better understanding of what data signals predict system failures, which will be highly valuable in the autonomous automotive industry.

Healthcare will utilise the possibilities of producing digital twins of people, band-aid sized sensors can send health information back to a digital twin used to monitor and predict a patient's well-being. But interpretations of a human's digital twin has seen to vary massively, while some intend to compare individuals to their sets of digital twins, which would allow a doctor to form a clearer understanding of what that patient’s data actually means.

In manufacturing, the digital twin can be used as a virtual representation of the current design, build, and maintenance of a physical product, which can be augmented by real-time process data and analytics based on accurate mathematical modelling of the physical product, production systems, or equipment, allowing for a huge support in the optimisation of a products performance.

Currently, there are a significant percentage of companies that implement Industrial Internet of Things already use digital twin technology, a larger percentage are planning to use the technology, in some form, in the future, as an important component of their predictive analytics strategy. Graphical Research, has predicted last month that the industry will see a massive spike in years to come, with the development of the technology itsself and its use and implementation becomes more viable. the prediction is for the European digital twin market to reach 9.5 billion dollars in 2026, growing at 30% between the years 2020 and 2026. While the market just reached $1 billion in 2019 in Europe alone, as the region approaches a highly automated future.

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