Infinite opportunities from infinite data
Hannover Messe 2021 took place from April 12 to 16 in digital format.
Siemens participated with a unique virtual presence. In line with the motto "Infinite opportunities from infinite data", participants in the Digital Enterprise Virtual Experience were able to explore all of the Siemens highlights in a realistic 3D environment and be inspired by this first-rate digital experience.
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The Birth of Constant OptimizationToday’s most complex products and processes are designed, tested and calibrated in the virtual world before being manufactured in the real world. Model calibration involves creating and simulating software models of future products, eventually leading to the creation of “digital twins”.
Today, modern production facilities don’t simply exist in the physical world. Everything that is crucial for their functioning – whether it’s milling machines, assembly line robots, automated warehouses, or even air conditioning or lighting systems – sends signals to on-site computers and servers, continuously creating an instant inventory of its current state. This not only helps operators at control panels to know what’s happening in their factories. They can also use this information to improve productivity, ensure safety, and adjust to new requirements. There’s one tool we have today, though, that facilitates this process of continuous optimization like no other – it’s known as the “digital twin.”
A digital twin is a virtual double of a product, a machine, a process or of a complete production facility. It contains all the data and simulation models relevant to its original. Digital twins not only enable products to be conceived, simulated, and manufactured faster than in the past, but also to be designed with a view to improved economy, performance, robustness or environmental compatibility. The virtual twin of a product can also accompany it like a digital shadow through all the stages of the value chain – from design through production to operation to servicing and even recycling. It seamlessly and ideally links together the three Ps: product, production, and performance.
Digital twins enable better products to be developed in less time because simulation technology not only accelerates design, but also testing – long before any physical prototypes are produced. Virtual twins also boost design efficiency because they enable developers to try out and compare more configurations than would be possible with physical models.
Digital twins, for example, make it possible to increase the energy efficiency of a new building. In addition to visualizing all the geometric data of every element of a building, they can include schedules, budgets and data regarding a building’s energy supply, lighting, fire protection, and operations. As a result, it is no problem to optimize a building’s future climatic impact before ground has been broken.
What’s more, digital twins can keep on collecting data during a building’s – or any other product’s – operational lifetime. This can be information about physical stresses, components that have failed, or how an object – whether a milling machine, an aircraft, or a building – is used. Such information not only supports optimization during operations, it also aids designers, architects, and engineers in preparing the next generation of a product. “The aim of this development is a closed cycle that links the virtual world of production development and production planning with the real-world performance of a production system and the product itself,” says Dirk Hartmann, a leading expert on digital twins and simulations at Siemens Corporate Technology.
Digital Twins: Driven by Physics-Based ModelsKaren Willcox is a leading aerospace researcher and expert in simulation-based engineering who specializes in the aerospace industry. Her work on simplified simulation models has made it possible to accelerate the development and design of complex systems such as planes.
Why is the concept of the digital twin gaining in prominence right now?
Willcox: Today’s tremendous computing power, combined with powerful algorithms, makes it possible to build and use digital twins. Machine learning can help to identify meaningful patterns in the large amounts of data we can collect from complex systems, such as an aircraft, but we also need physics-based models to make our digital twin predictive and useful. Another innovation is new hardware architectures that allow us to collect and analyze data efficiently to then incorporate the data into digital twins. One example of this is neuromorphic chips that are lightweight and energy-efficient, and so may be well suited for analyzing data onboard the system itself while it is in operation.
Do simulations have to mirror real processes for a correct result? Or is it enough if they simply predict the correct outcome?
Willcox: The use of a black box model is only OK as long as it works, but the question is how would you know? A critical question is whether you can trust your model - and trust is higher when you understand what is actually happening. That's why physics-based models are essential.
What role does Siemens play in this field?
Willcox: I have met Siemens experts at workshops and conferences and have had exchanges with them. Siemens is instrumental in advancing these modeling technologies and leading the development and implementation of digital twins for real systems.
Will digital twins become part of our everyday life?
Willcox: Computer modeling and digital twins are becoming increasingly important. They improve the performance of a system, extend its life, and help reduce costs. Currently, this is mainly used for expensive and complex machines. In the future, however, the use of digital twins could become part of our daily lives, for example, when you optimize the energy management of your house. I expect that we will see more applications of digital twins in industry. To achieve this, however, will require changes to engineering curricula and training. Next-generation engineers and technicians need to be equipped not only to use these new instruments, but also to understand their potential and limitations.
Of course, Siemens uses digital twins for its own products, whether in the design of gas turbines, factories such as the company’s electronics plant in Amberg, Germany, or in new buildings such as the Siemens Building Technologies headquarters in Zug, Switzerland.
One of the major users of digital twin technology is Siemens’ new Operating Company Digital Industries (DI). For instance, technologies from DI helped to create the new “Solo” electric car from Canadian startup Electra Meccanica. The car, which was recently introduced on the North American market, was designed, simulated and manufactured with the help of Siemens digital twin software. Electra Meccanica was able to test and optimize all the vehicle’s elements – mechanical, electronic, software and system performance – in advance using digital twins. And they’re not the only ones – the futuristic lightweight form of a chassis for a prototype of a racecar from Californian company “Hackrod” also based on a Siemens digital twin.
But virtual twins don’t just cover products, they can also represent complete factories and their processes. Bausch + Ströbel, for example, a German manufacturer of automated packaging and filling systems for the pharmaceutical industry, relies completely on digital twins for special machines. These twins, which were developed using Siemens software, enable simulation and thus continual improvement. The company expects that this will enable it to considerably speed up development of its systems by 2020.
All of this leads to industry 4.0 – the frequently invoked digital transformation of industrial processes, which is already underway at Siemens’ Amberg Electronics factory. Every day, the plant’s sensors register millions of items of process information from production of Simatic controllers. Thanks to Mindsphere, the facility’s open IoT operating system, this information is assigned to a digital model of the factory, where it supports ongoing process optimization. The results are extremely high process precision and effectively zero rejects.
IT analysis and market research company Gartner predicts that half of all major industrial companies will be using digital twins by 2021. Expected growth rates are correspondingly high. Indeed, market research conducted by analysts at Grand View Research foresees that the market for digital twins will reach 26 billion dollars by 2025 – an annual growth rate of around 38 percent.
This growth is largely being driven by the increasing implementation of the Internet of things (IoT), intelligent data analysis and industrial process optimization – all Global Technology Fields in which Siemens plays a prominent role. And Siemens is prominently represented in many of the sectors in which digital twin technology is expected to succeed, such as manufacturing, health care, transport, energy, and infrastructures. “Our ambition is to not only profit from this trend but also to make a decisive contribution to shaping it. That’s why Siemens has declared simulation & digital twins to be a Company Core Technology – a field in which we as a company intend to be pioneers”, says Herman Van der Auweraer, head of this effort and a leading technology expert at Siemens DI.
Digital twins are increasingly being accepted in the development of products, plants, and entire systems. In fact, thanks to this trend, we can expect the technology to also be accepted for certifications such as security and environmental standards. In the foreseeable future, products will be delivered with their digital twins. That will permit users to, for example, test and predict how modifications to an electric motor, a building’s design, or a factory’s production process will affect energy use and efficiency. Indeed, digital twins may eventually become an integral part of our everyday lives by enabling individuals without previous technical knowledge to finally get simple answers to complex questions.
Digital Twins at Siemens
Picture credits from top: 6. and 7. picture : Oden Institute for Computational Engineering and sciences, 8. Hackrod