Digital Twins, The Birth of Constant Optimization: Siemens
Today’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.
Maximum Efficiency - before the groundbreaking ceremony takes place.
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.