The digital twin is simply a virtual representation of a real object. It is a system of codes and values meant to represent the digital equivalent of tangible objects. The concept of a digital twin is born out of the wish to create a meeting point where the real world meets a perfectly accurate mirror image via a digital channel.
While many point back to NASA’s use of prototype space capsules to simulate space, and analyze for possible issues. The advent of the digital twin concept, into public consciousness, is positively due to a statement in 2017 by Gartner Inc., that digital twins rank highest amongst a set of technological trends, which are set to form the core of future technology in a few years – by having a digital copy for up to a billion real things in the nearest future. Thus, it unites other trends of the famed Industry 4.0 which are: Machine Learning, Artificial Intelligence, Big Data, and of course, the Internet of Things, in such a unique way, that exposes its importance and role in the prospects of manufacturing and engineering.
The one clear purpose of digital twins, is to improve customer satisfaction for production companies, by gathering data from the experiences of the real-life model during the life cycle of the real objects in real-time. This, exposes a trait in the digital concept that declares a resemblance with the waves of the Internet of Things (IoT).
By monitoring the object through its digital twin, companies can investigate inherent faults in their products, or predict subtle dangers to the health of the device. With this knowledge, they can give their customers notifications of possible failures, as well as use the gathered data for improvements or as a base for innovative and new designs. On the plus side, all this will be accomplished using very little financial effort.
The blueprint of the Digital twins
To materialize the idea of an exact but digital copy of a real identity, sensors are used in cooperation with spatial network graphs to simulate the digital models. While the input of machine learning and other elements of Industry 4.0 ensures that the digital twin develops an appetite for updating itself exactly the way the physical identities do. The information gathered is in form of the working condition of the digital twin’s real counterpart, as well as its position and status. This implies that, the digital twin does not only stores information as it happens to the real body, but it keeps a history of the physical body’s data.
It goes without saying that it takes elite engineers to create digital twins for now. However, the process requires a large pool of data, spanning the dimensions, manufacturing and operative values, as well as software analysis of the physical asset.
Neglecting the obvious problem of privacy invasion and unnecessary intricacy, that limits its use for many devices, digital twins are instrumental in minimizing the costs of maintenance for many other machines.
Bigger corporations like oil companies and industrial facilities are sure to capitalize on the fact that digital simulations mean that there is no loss of material, while there is flexibility on the number and types of experimentations we can carry out. Perhaps the association that have exploited this the most is predictably the aforementioned NASA.
By using digital twins, they have conveniently made alterations and adjustments on active space equipment, by carrying out offsite simulations with the digital twin of the equipment and transmitting the successful variations to the engineers closest to the equipment in space to make the physical adjustments.
Also, Chevron has publicly announced the integration of digital twin technology in its oil plants and facilities to cut down maintenance costs by at least a few millions of dollars. Also, they can reduce the amount of downtime involved in carrying out maintenance procedures. In time, more gadgets will follow the lead of Siemens, as it plans to reduce the time it takes to get new products to market with digital twin tech.