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Introduction To Digital Twin

  • Difficulty Level : Hard
  • Last Updated : 27 Feb, 2020

Ever made a machine? If yes, then how many attempts it took to make it function flawlessly, to make it the ideal one? We guess a bonanza of these unsuccessful attempts. It’s not only you but every manufactory faces this troublesome situation. At times, a defect in a certain fragment might result in the nonfunctioning of the device. This will require dismantling the fragments, figuring out the corrupted part, fixing it and there you go back to day one.

Ever wanted if you could find out how the machine is going to function before assembling all components? What if we say that you can simulate your device on your desktop as the same as it is going to perform in the real world? No, we are not talking about video games but the exact replica of the device with all of its components from the micro atomic level to the macro geometric level. Yes, this lies within the realm of possibility and can be realized with the help of a “Digital Twin“. The next significant thing in industrial services will be about accurately foretelling the future of physical assets through their digital twins. You might not be acquainted with this term “Digital Twin” right now, but believe me, once you get a glimpse of it, you will be craving to know everything about a “Digital Twin”. Let’s start by outlining it.

There are plenty of definitions of a “Digital Twin” flooding all over the Internet but the simplest is: A Digital Twin is a real-time digital clone of a physical device. Still ambiguous? Let me make it unambiguous. A Digital Twin of any device/system is a working model of all components (at micro level or macro level or both) integrated and mapped together using physical data, virtual data and interaction data between them to make a fully functional replica of the device/system and that too on a digital medium. This digital twin of the physical system is not intended to outplace the physical system but to test its optimality and predict the physical counterparts’ performance characteristics. You can know of the system’s operational life course, the implication of design changes, the impact of environmental alters and a lot more variables using this concept. Talking about life course, it invites me to aromatize your awareness of the concept with its origin.

Brief History of Digital Twin

The concept and model of the Digital Twin was officially put forward in 2002 by Dr. Michael Grieves as the conceptual model underlying Product Lifecycle Management (PLM). The concept was being practiced since the 1960s by NASA. They used basic twinning ideas for space programming at that time. They did this by creating physically duplicated systems at ground level to match the systems in space. An example is when NASA developed a digital twin to assess and simulate conditions on board Apollo 13. The efforts were made keeping in mind only a particular mission and because of that, this concept didn’t gain recognition until 2002 after Dr. Grieves presented it with all the elements including real space, virtual space and the spreading of data and information flow between real and virtual space. The concept of integrating the digital and physical parts as one entity has remained the same since its emergence. Although the terminology has changed over the years till 2010 when it was subsequently called ‘Digital Twin’ by John Vickers of NASA in a 2010 Roadmap Report.
A Digital Twin consists of three distinct parts: The physical part, the Digital Part and the connection between the two. The ‘connection here refers to the data that flows from physical products to the digital/virtual product and information that is being available from the digital environment to the physical environment.

How Digital Twin Work?

With the above piece of information, you might have sketched an idea in your mind about the working of a “Digital Twin”. By now, after getting an approximate idea of a digital twin, you might have realized that to create a digital twin, we need physical data, virtual data and the interaction data between the two to map them together to make a digital replica of the system. Now, the question here arises is how is all this data collected? When it comes to discussing about working of a Digital Twin, we can only start by finding an answer to this question.
For the creation of a digital twin of any system, the engineers collect and synthesize data from various sources including physical data, manufacturing data, operational data and insights from analytics software. The sensors are connected to the physical product that helps to collect data and send it back to the digital twin, and their interaction helps to optimize the product’s performance using a maintenance team. The Engineers integrate Internet Of Things, Artificial Intelligence, Machine Learning, and Software Analytics with Spatial Network Graphs to gather all the relevant information and map it into a physics-based virtual simulating model and then by applying Analytics into these models, we get the performance characteristics of the physical asset. For most of the devices, the seamless exchange of data helps in getting the best possible analysis, the same is the case for digital twin. Therefore, a digital twin continuously updates itself from multiple sources to represent its near real-time status, working condition or position. It’s learning system, learns from itself, using sensors that conveys data of various aspects of its operating condition; from human experts, such as engineers with deep and relevant industry domain knowledge; from other similar machines; and from the larger systems and environment which it may be a part of. A digital twin also uses the data from past machine usage to factor into its digital model. The digital model created is then applied with analytics such as environmental conditions or interaction analytics with other devices to detect anomalies and the lifecycle of the physical counterpart. The twin then determines an optimal process that boosts some key performance metrics and provides forecasts for long-term planning which helps in optimizing the business outcome.

Need of Digital Twins

Without any doubt, constructing a digital twin would be purposeless if there were no practical reasons for pursuing it. It is already noted that one cannot make a perfect machine in just one try and it costs bags of money and a whole lot of time experimenting on physical products. On another note, Digital twins and IoT(InternetOfThings) together with artificial intelligence help us analyze data and monitor systems to scrutinize and solve these problems. Where making a change in a physical product could be backbreaking, a digital replica can be swiftly revised to demonstrate amendments and to run simulations. If the outcome of the revised system does not comply with our needs after testing it on Digital Twin prior to physical machine, this would help us with no wastage of physical resources along with the time savings. By monitoring the status of a system or process and using multiple tides of data in real-time to study its digital twin, engineers gain deep knowledge on how to enhance product lifecycles, streamline maintenance and sharp optimization. Using a digital replica of the physical system not only accelerates development in various aspects but also helps to analyze, observe and navigate to every minute detail with so much precision that there is no space for errors and inaccuracies ensuring the optimal production output. Yet another benefit is that digital twins allow experts to work on projects even when they are not in direct contact with the physical twin. It ensures the safety of a well being with no risk of tragedy. Digital twin also helps engineers to work on equipment that?s already in space and completely inaccessible to them, without the hassle that comes with the physical accessibility of such types of equipment. Any update or alteration can be first tested for its outcomes and repercussions to avoid any calamity by directly implementing it in the physical world. In a nutshell, digital twins have the power to reshape the universe.

Applications of Digital Twins

We are in the early development stages of the Digital Industrial Era where the Digital Twin as yet is in it?s infancy. Despite this, we can catch sight of tremendous transformations that lie ahead of us. These Digital Twins epitomize asset ‘memories’ and even ‘group consciousness as they turn out to be the ‘living models of physical entities. We are witnessing the major applications of Digital Twins in the following sectors:

1. Manufacturing: Not only the emergence of Digital Twins helps us manufacture high-grade products. But also we can salvage money and time both, which would otherwise be wasted on the production. It facilitates these firms to test new designs expeditiously. Talking about Virtualised Testing of a new supply chain, its a breeze, whereas testing the physical equivalent involves shutting down production, losing profits, which on the other hand can be like opening a Pandora’s box. Since digital twins can give a real-time view of what’s happening with equipment or other physical assets, they have been very helpful in manufacturing.

2. Automotive: As automobiles, especially cars, become progressively integrated with IoT and digital technology, the ability to replicate every detail becomes increasingly indispensable. With the help of digital twins, it has become a piece of cake for engineers to predict the performance of the machines. We can construct a digital twin of all sorts of autonomous vehicles and track the vehicle from the day of its creation to the day it goes to the junkyard. Engineers can test new safety features in the digital world, without any need for the new physical vehicle to test changes. For a similar reason, smart car producers are testing their self-driving AI in digital environment too.

3. Healthcare: A digital twin can help virtualize a hospital system to create a safe environment and test the impact of potential changes on the performance of the system. Furthermore, Digital Twins in the healthcare sector can identify faults with the various equipment (which is often very expensive and needs to operate at optimum levels) involved in various medical fields. Not just that but digital twin has helped doctors to carry out difficult surgeries. Take an example of cardiologists, they used digital twins of the patient’s heart to precisely determine the positioning of leads that would work best on this specific patient that too before surgery decreasing the risk of failure.

4. Retail: The implementation of this concept of a Digital Twin plays a key role in augmenting the retail customer experience by manufacturing a simulation that could accurately represent how a specific model of a product takes place in an individual’s life. Not only this but also it lets you test if there is any potential in a new design of the product to cut back expenses without having to make large scale physical changes to your entire product range which can reduce the market price of the product. Having an exact digital copy of your physical asset can lead to trendsetting innovations. Once the innovation works well for digital model, one can start investing in physical assets.

5. Smart Cities: Cities have numerous moving and interconnected building blocks. With a well suited advanced model, civil engineers, governments and other related companies can test new solutions in the best possible way. This tool can prove highly advantageous for analyzing the different forms of transport and pedestrian movement patterns and for sound planning to ensure that their requirements are met. When prepared with Machine Learning, this model can test possible solutions to problems like traffic management in no time. This model would be beneficial in yet another troublesome situations. Such as, in the case of a fire emergency, firefighters can have access to the 3D model of the building. With the help of Augmented Reality and AI, firefighters could know where people are and how to predict fire’s behavior.

Future Of Digital Twins

The global market for digital twins is expected to grow very rapidly. Talking in numbers, by almost 38 percent annually, reaching $15.7 billion by 2023, according to MarketsandMarkets research. But this is not as easy as it seems as agencies face many challenges in constructing a digital twin. Its construction is only the tip of the iceberg, the actual challenge lies in lack of clear standards for implementing them, a need to train people to use them and a plan for governance. Digital twins hold the potential to change healthcare immensely in the future. They will allow the power to push past the limitations of medicine, and utilize data as a tool to truly understand the human body. Simulated organs can change how medicine works, in a hyper-personal and less invasive way. With the view of the forthcoming, a digital twin of cities is a possibility which could make search engines capable of finding anything in the physical world. Human beings will also have their digital twins, which will collect real-time information from wearables and contain a user?s unique genetic code. Using this information, many concerns such as health and crime issues can be solved. But all along with the emerging technology, we will have to work through crappy stages before we get the good stuff. A lot of big names such as Bosch, Microsoft, IBM, GE and many more have started investing in this technology and the ones who lag may suffer a downfall for their companies.

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