Have you ever crafted a machine? If so, envision the iterative process it took to achieve flawless functionality. We understand that the journey likely involved numerous attempts, a common challenge faced not only by you but by every manufacturer. Defects in specific fragments can lead to nonfunctionality, prompting dismantling, identification of the faulty part, and starting anew.
Ever wished you could predict a machine’s performance before assembly? Imagine simulating it on your desktop, replicating real-world behavior from micro-atomic to macro-geometric levels. This possibility is realized through a “Digital Twin.” The future of industrial services revolves around accurately predicting physical assets through their Digital Twins.
What is a Digital Twin?
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.
Example : 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:
1. The physical part ,
2. The Digital Part ,
3. 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.
Types of Digital Twins:
Digital twins can be categorized based on the scope and complexity of the physical entity they represent, forming a tiered hierarchy of functionality. This section outlines the four main types of digital twins:
1. Component Twins
These twins represent the most granular level, focusing on individual components within a system, such as sensors, actuators, or mechanical parts. They capture intrinsic properties, operational parameters, and behavior characteristics of these components, often relying on sensor data and basic physical models.
- For instance, a digital twin of a wind turbine blade might track temperature, vibration, and rotation speed to predict potential wear and tear.
2. Asset Twins
Moving up the scale, asset twins represent complete physical entities like vehicles, machines, or infrastructure elements. They integrate data and behavior from individual components (often derived from component twins) into a cohesive model, providing a holistic view of the asset’s performance, health, and potential future states. Asset twins employ more sophisticated models encompassing physics, thermodynamics, and other relevant domains, enabling proactive maintenance and performance optimization.
- For example, a digital twin of an airplane might track engine performance, fuel consumption, and flight dynamics to optimize flight paths and predict maintenance needs.
3. System Twins
System twins capture the interactions and dependencies between interconnected assets within a larger system, such as power grids, transportation networks, or manufacturing facilities. They leverage data and insights from asset twins but incorporate additional layers of complexity to account for emergent behavior arising from inter-asset dynamics. A digital twin of a power grid might simulate how the failure of one generator cascades through the system, impacting other components and potentially causing outages.
4. Process Twins
The most comprehensive digital twins, process twins encompass the entirety of a complex operation, including physical assets, human interactions, environmental factors, and logistical considerations. These twins model the entire workflow, enabling comprehensive optimization, scenario simulation, and proactive identification of potential disruptions.
- A digital twin of a manufacturing process might encompass everything from raw material procurement to finished product delivery, optimizing resource allocation, predicting bottlenecks, and ensuring on-time production.
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 in Digital Twin?
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.
- Engineers integrate Internet Of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Software Analytics with Spatial Network Graphs.
- The integration aims to gather relevant information and map it into a physics-based virtual simulating model.
- Analytics are applied to these models to extract performance characteristics of the physical asset.
- Seamless exchange of data is crucial for devices, including digital twins, to facilitate optimal analysis.
- Digital twins continuously update themselves from multiple sources, representing near real-time status, working condition, or position.
- Learning systems within digital twins use data from various sources, including sensors, human experts, similar machines, and the broader systems/environment.
- Past machine usage data is incorporated into the digital model.
- Analytics, such as environmental conditions and interaction analytics with other devices, are applied to detect anomalies and understand the lifecycle of the physical counterpart.
- Digital twins determine optimal processes to enhance key performance metrics and provide long-term forecasts for business optimization.
- The overall goal is to improve business outcomes by leveraging insights from the digital twin’s continuous learning and analysis.
Advantages of Digital Twins
Digital twins, virtual replicas of physical assets and processes, are rapidly altering the landscape of various industries. These data-driven models, constantly updated with real-time information, offer a multitude of benefits across diverse sectors. Here, we delve into some key advantages of embracing digital twin technology:
1. Enhanced Operational Efficiency:
- Predictive Maintenance: Digital twins, empowered by sensor data and artificial intelligence, can forecast equipment failures before they occur. This proactive approach to maintenance minimizes downtime, reduces repair costs, and extends the lifespan of valuable assets. Imagine a digital twin of a wind turbine predicting a potential blade malfunction, allowing technicians to address the issue before it causes costly downtime and energy production loss.
- Process Optimization: Real-time data gleaned from digital twins enables continuous monitoring and optimization of processes. Bottlenecks and inefficiencies are readily identified and addressed, leading to enhanced throughput, improved resource utilization, and overall production efficiency. Think of a digital twin of a manufacturing line pinpointing an area causing delays, allowing managers to quickly adjust production schedules and optimize resource allocation.
2. Data-Driven Decision-Making:
- Rich Insights: Digital twins provide a comprehensive overview of asset performance, system behavior, and operational data. This wealth of information empowers informed decision-making, enabling businesses to optimize resource allocation, experiment with different scenarios, and predict future outcomes with greater accuracy. Picture a digital twin of a power grid offering insights into energy consumption patterns, allowing utility companies to make informed decisions about resource allocation and grid management.
- Reduced Uncertainty: By simulating various scenarios and potential disruptions in the digital twin environment, businesses can mitigate risks, test new strategies, and make informed decisions with greater confidence. Imagine testing a new marketing campaign in a virtual space before launching it in the real world, minimizing potential risks and maximizing its effectiveness.
3. Fostering Innovation and Collaboration:
- Accelerated Product Development: Digital twins can be used to virtually test and refine new product designs before physical prototypes are built. This iterative approach reduces development time and costs, fosters innovation, and brings products to market faster. Imagine engineers using a digital twin of a new electric car design to optimize its aerodynamics and battery performance before building a physical prototype.
- Enhanced Collaboration: Shared digital twin models can facilitate seamless collaboration between different teams and stakeholders. This improves communication, streamlines workflows, and fosters a more integrated approach to operations. Think of a construction project where architects, engineers, and contractors all have access to a shared digital twin of the building, promoting better coordination and project efficiency.
4. Sustainability and Environmental Impact:
- Resource Optimization: Digital twins can help optimize energy consumption, reduce waste generation, and minimize environmental impact by identifying inefficiencies and opportunities for improvement within processes and systems. Imagine a digital twin of a manufacturing plant suggesting ways to optimize water usage and reduce resource waste, contributing to a more sustainable production process.
- Sustainable Design and Development: By virtually testing and optimizing product designs for energy efficiency and reduced environmental footprint, digital twins can contribute to the development of more sustainable products and processes. Picture a digital twin of a new building design helping architects optimize its energy efficiency and minimize its carbon footprint before construction begins.
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 digital twin market is set to skyrocket, projecting a staggering annual growth rate of almost 38 percent and reaching $15.7 billion by 2023, as per MarketsandMarkets research. However, constructing a digital twin presents challenges, including the absence of clear standards, the need for user training, and governance planning. Beyond these hurdles, digital twins hold transformative potential in healthcare, offering insights that surpass traditional medical limitations. Simulated organs and city replicas could revolutionize medicine and enhance search capabilities. Human digital twins, powered by real-time data from wearables and unique genetic codes, promise solutions to health and crime issues. Major players like Bosch, Microsoft, IBM, and GE are investing in this technology, signaling its significance in shaping the future, while those lagging behind risk facing company downturns.
Frequently Asked Questions for Digital Twins
What is digital twin in simple words?
A digital twin is a dynamic, data-driven virtual replica of a physical entity or system. It’s like a mirror image in the digital world, constantly updated with real-time information from sensors and other sources. This information lets you virtually monitor, analyze, and even predict the behavior of the real thing, from machines in factories to entire power grids.
What are the 4 types of digital twins?
- Component twins: Digital representations of individual parts or components of a system or product.
- Asset twins (or product twins): Virtual representations of the entire physical product rather than its individual parts.
- System twins: Comprehensive digital models of an entire system or network of systems.
- Process twins: Digital twins that focus on modeling and simulating operational processes within an organization or system.
What is a real example of a digital twin?
- Rolls-Royce: Digital twins of airplane engines help airlines optimize flight routes, predict maintenance needs, and improve engine performance.
- General Electric: Digital twins of wind turbines monitor their health and performance, leading to increased energy production and reduced downtime.
- BMW: Digital twins of car production lines optimize logistics, identify bottlenecks, and improve overall manufacturing efficiency.
What is a digital twin in an IoT system?
In IoT, a digital twin is a live virtual copy of a physical object or system, syncing with real-time sensor data. It allows remote monitoring, analysis, and predictive maintenance, optimizing the physical counterpart. In smart manufacturing, for instance, a machine’s digital twin offers real-time insights for proactive maintenance and enhanced efficiency.
How does NASA use digital twin?
NASA uses digital twins to simulate and predict the behavior of complex systems, such as spacecraft, satellites, and Earth’s climate. Digital twins are virtual replicas of physical systems that can be used to test and optimize designs, monitor performance, and predict failures before they occur. NASA’s Earth System Digital Twins (ESDT) program is an example of how digital twins are used to model and simulate Earth’s complex systems