A Digital Twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. The concept was first introduced in 2002 by Michael Grieves at the University of Michigan. The first practical definition of digital twin originated from NASA in an attempt to improve the physical model simulation of a spacecraft in 2010. NASA continues to heavily use digital twins for its space exploration initiatives including its rovers on Mars. Digital twins were until very recently limited to the aerospace and heavy machinery market, but this is changing and there are now a variety of use cases in diverse sectors such as smart cities, healthcare, insurance and utilities.


HOW IT WORKS?
There are three tests to certify a virtual copy to be a true digital copy: First is the vision test where the virtual visual inspection would include disassembling the product and seeing completely realistic representations of its component parts; second is showing that the virtual product reacts realistically to testing, such as a digital wind tunnel or simulated tests; and the third test is about getting information from the virtual product through a physical inspection, just as a user would from an actual product. This coupling of the virtual and physical worlds allows analysis of data, monitoring of systems, and simulation of real-world conditions to proactively respond to changes, prevent downtime, improve operations, and develop new revenue models. Digital twin technology covers the entire lifecycle of the product or service from conceptualization till it retires. The genesis of digital twin was in the form of 3D CAD models designed for the new product introduction. However, the proliferation of digital levers like cloud connectivity, IoT, augmented reality (AR), and machine intelligence provided a significant momentum for large-scale implementation of digital twins in various industries. The digital twin has all the real-time performance data, sensor data, and inspection data along with the history of the maintenance performed, configuration changes, parts replacement, and warranty data, leading to reduced lifecycle ownership cost of the asset and valuable product intelligence for superior product innovation.

APPLICATIONS:

Space Sector
NASA was first to apply the concept of digital twin in the space sector in the 1970s during the Apollo 13 program, where engineers on the ground needed to be able to rapidly account for changes to their vehicle while being exposed to the extreme conditions in space, and with lives on the line.

Manufacturing
Digital Twin is poised to change the current face of the manufacturing sector. Digital Twins have a significant impact on the way products are designed, manufactured and maintained. It makes manufacturing more efficient and optimized while reducing the throughput times.
Digital Twins can be used in the automobile sector for creating the virtual model of a connected vehicle. It captures the behavioural and operational data of the vehicle and helps in analysing the overall vehicle performance as well as the connected features. Digital Twins also helps in better in store planning, security implementation and energy management in an optimized manner.

Smart Cities
The smart city planning and implementation with Digital Twins and IoT data helps enhancing economic development, efficient management of resources, reduction of ecological foot print and increase the overall quality of a citizen's life. The digital twin model can help city planners and policymakers in the smart city planning by gaining the insights from various sensor networks and intelligent systems. The data from the digital twins help them in arriving at informed decisions regarding the future as well.

Health Care
A patient's digital twin is designed to capture continuous data from the individual about various vitals, medical condition, response to the drug, therapy, and surrounding ecosystem. Each patient's data is stored at Azure or AWS public cloud and fed to the Digital Twin platform. Historical and real-time data of each patient helps ML algorithm to predict future health conditions. With lifestyle, daily food habits and blood sugar data of a chronic diabetes patient, the model alerts the patient for medications, food habit changes, doctor consultation etc. Thus, Digital Twin leverages a large amount of rich data from various IoMT devices and uses AI-powered models to develop more personalized and better care plans.

ADVANTAGES

  • Accelerated risk assessment and production time
  • Predictive maintenance
  • Real-time remote monitoring
  • Better team collaboration
  • Better financial decision-making

DISADVANTAGES

  • The success of this technology is dependent on internet connectivity.
  • The security is at stake.
  • The digital twin concept is based on 3D CAD models and not on 2D drawings.
  • Digital twin will be required across entire supply chains.
  • The challenges involved here include globalization and new manufacturing techniques. Managing all these design data for digital twin among partners and suppliers as the physical product evolves will be a challenge.

CONCLUSION
Digital twins have become important to business today. By producing a replica of the physical assets of a product or service in an Industry, digital twin helps in analysing the data, lends a platform to check the functioning beforehand so as to develop a solution for any potential problems.