Evolution of Digital Twins

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What is a Digital Twin?

Digital Twin is the new buzzword in the world of digital transformation. Is it just a buzzword or the next emergent technology? The simplest and meaningful explanation is that digital twin is an executable digital representation of a physical object, product, process or system across its lifecycle. One can make a virtual replica of almost anything existing in the physical world like machines, buildings, factories, cities and interestingly will soon have humans in the wider scope. The twin can take real-world data as input and produce predictions based on simulation and analytics. Digital Twins is a concept, not a standalone product. Multiple technologies like IOT, 3D simulation, block chain, big data, edge computing, cloud computing and artificial intelligence are involved to make the concept a reality.

How does it help?

The virtual replica of any physical objects helps to create various simulations to study and analyze product performance on various parameters before a new launch or product improvisation life cycle. Take it as a computer program for creating simulations of physical objects to predict the performance challenges, improvements, possible innovations and valuable data insights. The program has capability to integrate artificial intelligence and software analytics for an enhanced outcome. These learnings are then applied to the real object.

The journey of digital twin is built by specialists, experts in data science or applied mathematics. These developers research the dynamics of the physical object or system and use that researched data to develop a virtual model that simulates the real-world object in digital space. The twin model constructed is able to receive input from sensors gathering data from a real-world counterpart. This allows the twin to simulate the physical object in real time, in turn providing valuable insights into potential product problems and performance challenges. It is always not important to have the physical object real time. The twin could also be designed based on a sample model. In such cases twin helps to provide the initial feedback on how the actual product can be improvised. It is like a pre-model before the actual product launch. The amount of data feed taken into account determines how simple or complex the twin can be in simulating a physical object.

Digital twin technology is becoming more widespread. According to Deloitte study, the global market for digital twins is expected to grow with 38% CAGR to reach $16 billion by 2030.

Future indicates that very soon humans will have a digital version of themselves. Ever wonder how the virtual version of humans actually going to help? Well, the digital version of our body will help keep a tab on our health status. One can run series of simulations to diagnose potential health problems and preempt them from actually happening. It is like the health check done not when the problem has occurred but identify proactively through the digital tests and work on controlling it from actually affecting the human body.

History of Digital Twin Technology

The term widely became a topic of discussion among data scientists and IT professionals after Gartner one of the world leaders in research and consultation named digital twins as one of its top 10 strategic technology trends for 2017 subsequently in 2018 and 2019 and hinting that the coming years will see objects being represented by digital twins, a dynamic software model of a physical thing or system". Digital twin is already being used in real life applications in both B2C and B2B markets.

However, the concept was first coined at NASA way back in 1960’s when they actually created digital simulations of space capsules on- ground to study and test the issues in orbit and design solutions. 

In the year 1991, David Gelernter in his fascinating book Mirror Worlds, first introduced the concept. However, Dr. Michael Grieves, chief scientist for Advanced Manufacturing gets the credit for actually applying digital twins to manufacturing in 2002 and presenting his research on the digital twin software concept. NASA’s John Vickers eventually introduced a new term “digital twin” in his 2010 Roadmap Report.

 

What are the advantages of digital twin?

Through digitization, there is ample data available which is practically impossible to interpret and worked upon manually. Due to inadequate resources and tools, rich data is being wasted with no conclusive output. The digital twin is a computer program that integrates with artificial intelligence, machine learning and various other technologies related to IIoT (Industrial Internet of Things). The digital twin helps creating various simulations of the product to provide valuable insights, fix product defects and enhance improvisations. The various customer pain points can be studied through digital twin technology and the cumulative insights can be presented as a dashboard for the team to evaluate and act upon. Digital twins are changing the way work is done in different industries with varying business applications. These virtual models have become fundamental in modern engineering to drive various innovations and improved performance. Learning from the twin has helped reduce failure costs in the real-world testing environment of physical objects and provide data backed advanced analytical, predictive capability to test processes and services. It is widely now used in large-scale products or projects like healthcare, automotive, urban planning, construction, manufacturing, aerospace, oil refinery management and retail industry.

To summarize, digital twin helps in

●       Better R&D

●       Product reengineering and recycling

●       Greater efficiency

●       Cost reduction

●       Reducing equipment downtime

Digital Twin vs. Simulation

Are Digital Twins and simulations the same? The terms simulation and digital twin are often used interchangeably, but there is a fundamental difference. Simulation undoubted is an integral part of digital twins, however the scope and use of digital twins is beyond simulations.

Simulations are typically used during the design phase of a product's lifecycle, trying to showcase and forecast how a future product will look and function. They may not have a direct equivalent of a real-world object but just help understand what may happen in the real world. Digital twin on the other hand is a replica/virtual model of a real-world object and is built from the inputs from IoT sensors on real equipment and dynamically changes with time. It is not just the predictable model of what may happen but what is happening real time. So that makes it an integral part of the overall product life cycle real-time - design, execute, change, decommission.

In these times when data is humongous, comprehending the data for the accurate outcome is the trickiest and time-consuming task. Digital twin real-time insights help prevent costly system tests and failures and manage untimely maintenance issues. The decision-making ability becomes concrete and result oriented.

Tesla uses digital twin for every car manufactured. Huge data from the cars, are fed into the simulation models for real time analysis and improvisations. Not too far away, will we see the car itself raise an alarm for a checkup to the garage without the owner intervention.

Types of Digital Twins

There are four types of digital twins based on the complexity of what is being twinned. Abstraction and hierarchy are two ways to manage the complexity of design.

●        Component or Parts twinning

●       Product or Asset twinning

●       System or Unit twinning

●       Process twinning

 

The core of the all the four types are the same i.e. they are the virtual replica of a product or process to help predict and prevent potential damages and suggest improvisations. The difference lies in the area of application 

Part or component Twins - This is a twin of a single component of the entire unit. They are the smallest example of a physical functioning component that has a direct impact on performance and functionality. 

Product or Asset Twins - It is next higher level in digital twin categorization. It is the simulation of two or more components working together and their interactions are evaluated. They allow to explore the entire system. 

System or Unit Twins - This is a simulation of multiple system assets and check their performance. The system twin can be used for various types of applications. 

Process Twins - It represents an overall production facility. It is a high-end view of all systems working together and providing complete visibility of the complex system or a factory overview. The process becomes successful only once the parts, asset, system twin does their job as designed. 

Overall, each level is a lower or higher representation of digital twin but each one is equally important for a smooth functioning and success of a machine.

 

Digital Twin on the Cloud

At an industrial scale, digital twins have gained momentum with the adoption of cloud and IOT devices. The digital twin requires two-way connection between the physical asset and the virtual representation. For most industries, the challenge with the digital twin is not the technology but the deployment and investment in the huge infrastructure to support the solution. While large enterprises could afford to experiment with the digital twin the other small and medium enterprises struggle to use the digital twin and take advantage of the learnings from this technology.

The affordable on demand cloud service providers are the total game changers and removing the barrier. Cloud service providers are offering digital twin related products, add data continuity and data safety as the key functionality. Microsoft, for example, offers Azure Digital Twins, described as “an Internet of Things (IoT) platform that enables one to create a digital representation of real-world things, places, business processes, and people.”

A digital twin in a cloud-based environment can be remotely accessed by industry experts in any part of the world and allows them to collaborate and contribute by providing valuable data insights to the respective factory processes. One big threat related to data security is well taken care on cloud. The cloud platform layer security channels with 128- bit encryption and the risk of data theft is completely eliminated.

Knowledge retention is another big challenge in the huge industrial setups. Designing and decoding of processes are dependent on skilled employees and any exits/ shuffle would make the unlearn-relearn a time-consuming process. Cloud enables integrated data continuity so knowledge retention is seamless in the event of a loss of human or systems.

Digital twin on the cloud gives SME’s an edge and humongous opportunity to optimize their business processes.

Conclusion

It is not just the buzzword but the next step in the digital transformation journey. The Digital Twins’ adoption has just begun. With its multiple advantages as highlighted earlier, it will have wide acceptance across industries. It will play a vital role in research and development space. Digital twins will enable optimized automation of processes, which will result in efficiency gains. Data processing capabilities will be improved significantly helping big and small organizations across various domains increase productivity, improve performance, reduce operational costs and understand the customer journey statistically. 

If you want to know how Nebula Cloud is helping organizations in building cloud based digital twin environments across various industries and accelerating digital transformation, please write to sales@nebulacloud.in