In manufacturing, this digital map of machines and processes is used to optimize production, from system layout to design and manufacturing of product. A successful digital twin must provide a virtual mirror image of every process and event in its domain and do so in real time. It can be spread across as many types and formats of data as the user is able to handle. What's interesting about this type of simulation, is that the data does not have to be centralized in one particular software program or environment. Data is then used for predictive maintenance, optimization, simulation, and troubleshooting. This way if it requires maintenance or replacement, the part can be ready on the ground when the plane lands.ĭigital twins gather data about a machine's well-being from thousands of sensors, compiling the information into a readable or viewable model and sending it to the person or system in charge. The system uses this information to find defects or faults during the flight. For instance, General Electric uses a digital twin for collecting flight data from aircraft engines between London and Paris. Industrial examples include wind turbines, locomotives, heavy equipment, and other pieces of expensive machinery. The connections allow data to flow back and forth from the physical system, through sensors and networking, to the digital model, enabling it to respond and update. He defines it in three distinct parts: the physical system, the digital model and the connections between the two. ![]() The term "digital twin" is attributed to NASA's John Vickers in a roadmap report from 2010. Enabled by massive and recent increases in data storage, computing power, networking, and sensor technology, a digital twin is a real-time digital copy of a physical system. Conceptualized in David Gelernter's book Mirror Worlds, in 1991, digital twins promise to optimize manufacturing in ways that, until now, have been impossible.
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