The World Awaits Your Arrival! — ‘Let’s Intertwine the Digital Twin’

What is Digital Twin?

In digital twin, twin refers to the virtual representation/image (generally 3D model) of a physical object or system across its life-cycle product, process or service. It’s a very important concept in the Internet of things (IoT). Digital Twin concept represents the convergence of the physical and the virtual world where every industrial product will get a dynamic digital representation. It refers to a digital replica of physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes.

It uses real-time data and other sources for learning, reasoning, and dynamically recalibrating to deliver improved decisions. It leads to the creation of a highly sophisticated virtual model that is the exact counterpart (or twin) of a physical thing. The ‘thing’ could be a car, a bike, a building, a bridge, a track, rocket, ship, city or even a jet engine. It’s just a replica of a living or non-living physical entity. The data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical entity. These ‘connected digital things’ generate data in real time, and this helps tasks in better analyze and predict the problems in advance.

Sensors connected to the physical product can collect data and send it back to the digital twin, and their interaction can help to optimize the product’s performance. Connected sensors on the physical asset collect data that can be mapped onto the virtual model. The concept of the digital twin can be compared to other concepts such as cross-reality environments or co-spaces and mirror models, which aim to, by and large, synchronize part of the physical world (e.g., an object or place).

Digital Twins integrate internet of things, artificial intelligence, machine learning, and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change. A digital twin continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position. This learning system, learns from itself, using sensor data that convey various aspects of its operating condition; from human experts. A digital twin also integrates historical data from past machine usage to factor into its digital model.

Digital Twins are created in the same computer-aided design (CAD) and modeling software used for product development. According to product lifecycle management (PLM), it requires three elements: the physical product in real space, its digital twin in virtual space and the information that links the two.

Use of a Digital Twin?

As Digital Twins are designed to mirror the entire system and processes, in near future algorithms will be created to collect and analyze this unstructured data, recording data, and reactions.

Digital Twins help to accomplish great tasks, like:

  • Visualizing products in use, by real users, in real-time
  • Building a digital thread, connecting disparate systems and promoting traceability
  • Refining assumptions with predictive analytics
  • Troubleshooting far away from equipment
  • Managing complexities and linkage within systems-of-systems

All this information along with AI algorithms is integrated into a physical model-based virtual model and by applying Analytics into these models we get the relevant insights regarding the physical asset. The consistent flow of data helps in getting the best possible analysis and insights regarding the asset which helps in optimizing the business outcome.

Conventions in Digital Twin:

Virtual models are widely used in the manufacturing of various products. Several types of simulators are in existence today.

  • Data model: A hierarchy of systems, sub-assemblies, and components that describe the structure of the digital twin and its characteristics
  • Analytics: Predict, describe, and prescribe the behavior (past, present, and future) of an asset or process via both AI and ML models
  • Knowledgebase: Data sources that feed analytics, expertise, historical data, and industry best practices
  • Component: It is a component of an asset, such as a bearing on a rotating piece of equipment. The component twin is typically a major sub-component that has a significant impact on the performance of the asset to which it belongs
  • Asset: This is a digital twin of an entire asset, such as a motor or pump. Asset twins can be collections of and informed by component twins. Asset twins provide visibility at the equipment level
  • System or Unit: The system or unit twin is a collection of assets that together perform a system- or network-wide function, such as an oil and gas refinery. A system twin provides visibility into a set of interdependent equipment. They collect massive amounts of operational data produced by devices and products in the system, gain insight and optimize all the processes
  • Process: A process twin is typically the highest-level twin that provides a view into a set of activities or operations, such as a manufacturing process. The process twin can be informed by a set of asset or system twins but focuses more on the process itself rather than the equipment. These models simulate manufacturing processes. The process can be further optimized with the help of product twins for every piece of equipment involved. Manufacturing operations will be safer, faster and more efficient
  • Product Twins: These are models of separate products. Before going to mass production, small prototypes were designed and tested. As a result, product Digital Twins help reduce production expenses and time-to-market, while improving quality
  • Industry-level dynamics: The digital twin is disrupting the entire Product Lifecycle Management (PLM), from manufacturing to service and operations. All this data is continuously communicating and collected by the digital twin. Advanced ways of product and asset maintenance and management come within reach as there is a digital twin of the real ‘thing’ with real-time capabilities
  • Firm-level dynamics: With the use of this technology, which they refer to as a “virtual twin”, has allowed many companies to create digital 3D prototypes of their different car models, engines, buildings, and products
  • Embedded Digital Twin: In manufacturing the Digital Twin is embedded in the device. By this improved quality, earlier fault detection and better feedback on product usage to product designer can be achieved seamlessly
  • Servitization: It is the process of adding value to their products and services by the companies to be unique in providing features, services, quality and customer retention aspects
  • Homogenization: It’s the consequence and an enabler of the homogenization of data. Due to the fact, that any type of information or content can now be stored and transmitted in the same digital form, it can be used to create a virtual representation of the product (in the form of a digital twin), thus decoupling the information from its physical form
  • Reprogrammable and Smart: A Digital Twin makes it possible to make remote adjustments through the digital component of a twin. It enables a physical product to be reprogrammable in a certain way. Furthermore, the digital twin is also reprogrammable in an automatic manner. Through the sensors on the physical product, AI technologies, and predictive analytics
  • Digital traces: These are used when a machine malfunction. So, it is essential to diagnose where the problem occurred. These diagnoses can be used in the future to improve them
  • Modularity: Modularity is particularly important in the manufacturing industry. In the sense of the manufacturing industry, modularity can be described as the design and customization of products and production modules

Tools and Technologies:

  • IoT: Using a variety of sensors, lots of information is gathered from the physical world. Therefore, IoT devices are essential to supply the data by using Sensors. Industry 4.0 is sometimes referred to as the ‘fourth industrial revolution’. It’s a current trend in which manufacturing is increasingly automated and reliant on the capture and exchange of data, often referred to as ‘Big Data’, via the IoT and cloud computing. Industry 4.0 creates what has been described as the ‘Smart Factory’
  • 3-D Simulation: Simulation is an integral part of Digital Twins. Physical asset needs to be simulated in the digital world. Simulation helps for deploying physical assets (R&D, prototyping) and optimizing existing assets (fine tuning)
  • Data Management: A robust data management layer (a standard RDBMS, Bigdata, if the volume is way too high and even Blockchain) to store and manage the data generated
  • Analytics: One of the foremost uses for digital twin technology is to optimize, reduce cost and improve quality. Therefore, a strong analytics layer — monitoring, management, and prediction — will be critical
  • Augmented Reality: It is the integration of digital information with the user’s environment in real time. In AR, the digital twin must be able to follow the product’s location and movement. Images overlaid with real-time sensor data can be used in AR applications to facilitate product maintenance and service
  • Digital Thread: Digital Thread, on the other hand, is a framework that plays an imperative role in the effective functioning of Digital Twins. It manages data by implementing seamless communication channel across platforms. Digital Thread supports in digitizing the physical assets, but it is more about traceability on their lifecycle.

Developer Take-A-Ways!

  • Siemens Digital Twin Software
  • ANSYS Physics-based Simulation
  • AutoCAD
  • SAP Digital Twin
  • IBM Digital Twin
  • Lanner Digital Twin Software
  • Seebo Digital Twin Software

Functions of Digital Twins:

In general, virtual models are used to monitor, analyze and improve their physical prototypes. Figuratively, their functions can be divided into three stages:

  • To See — at this stage, sensors, and devices collect data to picture the situation
  • To Think — at this stage, the smart software analyzes collected data and, if there are any issues, finds several possible solutions to each one
  • To Do — at this stage, intelligent algorithms choose the most appropriate solution and implement them to address the problems

How to Create a Digital Twin Model?

First, engineers build a virtual copy of a physical object (system or process) using computer modeling techniques. Then, a lot of sensors are connected to the object to collect a variety of data in the online mode. The data is uploaded to the cloud, where AI algorithms analyze it and determine possible scenarios. Thus, we get a working model that can improve the efficiency of production.

Applications of Digital Twin concept:

  • Manufacturing, Automobile, Digital, Retail, Healthcare, Smart Cities, Industrial IoT
  • Smart Devices and Smart Home Applications
  • Monitoring Aircraft and Jet Engines
  • Wind turbines, offshore vessels etc.,
  • HVAC control systems & Locomotives
  • Utilities (Electric, Gas, Water, Waste Water Networks)

Closing Thoughts:

Digital twin technology is the next step in the development of the global economy. It allows people to monitor, analyze and optimize the performance of various systems and helps them make better decisions. Digital twin technology can already be found in action in different industries. Here are some vivid examples. Wind Farms, Space Exploration, Oil and Gas Industry etc.,

The ultimate vision for the Digital Twin is to create, test and build equipment in a virtual environment” — Unknown