Imagine yourself standing in an under-construction property site. You will notice pillars getting erected, bricks being neatly laid and floors going up one by one.
Now, fire up its digital twin in a 3D interface. You will be able to see the entire building structure in a virtual setting, run simulations for optimizations, and leverage real-time data to study any design issues or tweak structural improvements, all with the intention of creating an aesthetically beautiful and sustainable structure. That’s a digital twin.
What is a digital twin?
A digital twin is a virtual mimic of a physical entity, process, place or even a person that replicates the working mechanism and characteristics of the physical counterpart, allowing users to interact with it and study future possibilities that helps unlock the true value of the physical asset.
Digital twins are continuously updated and used throughout the product’s lifecycle-from design to manufacturing and construction, to operation and maintenance, and even for future use or reuse. They learn, update and interact with the physical counterpart via exchange of data using AI, ML and IOT technologies. With the help of this digital twin technology, users can fend off issues in advance, reduce development time, explore valuable opportunities and get more bang for the buck in the process.
How does a digital twin work?
Conjuring up a digital twin model is a complex process and requires experts in the field of data science and mathematics to come up with an accurate representation of the physical prototype. The complexity of the virtual twin depends upon the objective with which it is built-whether it will just monitor and analyze the prototype; or allow the user to run simulations on it, understand issues and abnormalities, find out capable solutions based on advanced data analytics and help determine the capabilities of the product.
To ensure that the digital twin architecture is accurate, sensors are fitted on vital functional areas of the physical counterpart. These sensors produce data from the object’s real time performance and map this data back to the IT software, which simulates the real-world original in a digital space. The sensors gather data continuously from the physical counterpart over its entire lifetime. With the help of this data, the digital twin evolves and updates itself if any change is encountered in the physical counterpart, thus enabling companies to understand flaws in the product, mend it when and where needed and extract the best performance out of it.
What are the types of digital twins?
Digital twins can be segregated into four categories, depending on the area of application and what they simulate.
1.Component twins: These are the most elementary forms of digital twins and are created for individual components of a product or system such as a sensor or an electrical circuit.Digital twinning of a component helps to understand its physical, mechanical and electrical characteristics by simulating its behaviour and test how changes to the component can affect the working of the system.
2.Product twins: Asset twins are virtual mimics of products such as vehicles or machines and showcase how different individual components contribute together to the overall functioning of the system. The product twin shows the interaction of components with each other, allowing users to optimize where necessary, thus maximizing its performance and minimizing failures.
3.System twins: System twins are replicas of entire systems such as factories, power grids or transportation networks. They combine all units that make up the system and collect huge amounts of data produced by them in the system to improve collaboration between the units and maintain system integrity.
4.Process twins: Process twins are basically digital renditions of business processes such as a manufacturing process. With process twins, one can create various digital scenarios of a production process to analyse which scenario works better, weed out bottlenecks and update existing workflows. Process twins helps company leaders monitor key business metrics better in a data-driven manner and manage outputs better.
How are digital twins created?
To create a virtual architecture, a digital twin needs real time flow of data about the object, system or process by scanning the physical counterparts in the real world. Once the data is gathered, it is fed into the IoT systems and computational analytical models are created with the help of BIM, CAD and GIS software. The models are then displayed through 3D representations and AR techniques to simulate real-world conditions and visualize the outcomes on any device including smartphones, computers and AR/VR devices.
The digital twin deployment normallystarts small, viz the performance of a single component within a product but expands and evolves over time. This however happens in two ways. Firstly, a small number of digital twins are made together to give a complete picture of the asset, process or system. Secondly, more sophisticated capabilities are added to the existing digital twin to study predict future performance of the physical counterpart.
History of Digital Twin technology
The core idea of using a digital twincan be traced back to the 1960s when NASA pioneered the use of digital technology during the infamous Apollo 13 moon mission. NASA experts has set up simulators on the real spaceshipahead of departure so that they can run situational simulations when the space craft was space bound.
These simulators proved handy when the mission went jeopardy due to some critical functioning issues onboard the spacecraft. With the help of 15 computer-controlled models, engineers assessed and re-created conditions onboard the malfunctioned shuttle and guided the crew home, averting a potential disaster and loss of precious lives.
The concept of digital twins gained further recognition in the 2000s when Michael Grieves presented the development of a product lifecycle management centre in the University of Michigan in 2002. It contained all the elements that are in conjunction with a digital twin- a physical space, a virtual space and exchange of information between the two.
While the idea of digital twins became familiar in 2002, it became one of the top technology trends post 2017. In 2021, the global digital twin market was valued at $6.5 billion and is pegged to reach $125.7 billion by 2030. With digital transformation reverberating across the world, forward-thinking organizations are adopting digital twin technology to ace themselves in the field of design, construction and operations. The pandemic has accelerated this trend with companies using 3D powered digital twins for better collaboration, visualization and decision making.
What are the benefits of a digital twin?
1. Prior risk assessment: A digital twin helps companies test the ins and outs of a product and validate its performance and integrity before it is launched in the real world. With the replica of the product, experts can identify and assess any product infirmities before the product goes into production. They can simulate various scenarios, observe the reaction of the system or product in those situations and identify corresponding alleviation strategies. This enhances risk assessment, speeds up development processes and saves a lot of time and money.
2. Better R&D: With the abundance of data gathered with the help of a digital twin, companies can engage in effective research and design of products to get expected performance outcomes. Insights collected from this exercise can be used in refining a product and bring out the best value from it.
3. Prognostic maintenance: A digital twin’s IoT systems generate big data in real time helping businesses to identify any flaws within the system instantly. This helps companies to schedule their maintenance cycles more proactively, thus improving production line efficiency and keeping a tab on maintenance costs.
4. Better financial decision making:The availability of huge amounts of real-time financial data and advanced analytics also helps in taking better financial decisions. A digital twin can integrate financial data such as the cost of materials, labour and aid in adjustments to make the manufacturing process financially sound.
5. Long term value: With digital twins gaining momentum across sectors, company owners can strategize how the performance of their assets will affect output in the long term. In case of town planning, digital twins can foretell how population explosion, changing demographics and dwindling natural resources can affect the foundation of future cities.
What are the challenges of digital twins?
Despite the immense opportunities they provide and benefits they offer, digital twin technology hasn’t witnessed widespread adoption across the world. Many industries are yet to understand the nuances of a digital twin.Engineers and IT specialists working on this, need to gain sound knowledge about the system and how to implement it; or maximum output cannot be achieved. They need to sensitize themselves about the equipment, the compute infrastructure and the data storage infrastructure.
Besides, a significant initial investment has to be coughed up to set up this infrastructure. Investing in this technology without understanding what benefit will come out of it, is a disaster. So, how the digital twin technology will fit into the existing workflows and general operations of the business must be looked into before implementing it.
Why are digital twins important now?
A survey by Gartner showed that 13% of organizations that have implemented IoT, have already started using digital twins whereas 62% plan to use it within a year. This statistic stands testament to the fact that digital twins are fast becoming a nomenclature to drive innovation and performance across industries. Product engineers and technicians are upbeat about this burgeoning tech fad that brings the most advanced monitoring, analytical and predictive capabilities at their fingertips.
Companies who invest in digital twin technologies will witness a 30% increase in cycle times of critical processes, 10% increase in revenue and improve product quality by 25%. In the next five years, there will be millions of things that will be represented through digital twins. These replicas of the physical world will not only lead to better decision-making but also improve customer experience by understanding customer needs, enhance existing products, services and operations and drive innovation of new businesses. Digital twintechnology is crucial to the development of Industrial 4.0 that will lead to complete automation of industries, foster data exchange, enhance manufacturing processes as well as de-risk product rollouts.
What are the applications of digital twins?
Digital twins are used in a wide variety of industries for a wide range of applications. Some examples are as follows.
Manufacturing: Digital twins have today become a prerequisite in all stages of manufacturing as they can replicate a product’s entire lifecycle. From guiding products from their design phase to its completion, digital twins make manufacturing more productive and save time and money.
Automobile:Another example where digital twins are making the right noise is automobiles. Auto industry experts are using digital twin technology to create virtual twins of vehicles to analyse and improve their performance. By collecting operational data from the vehicle and checking its status in real time, digital twins help auto enthusiasts to test and implement new product models.
Retail: Augmenting customer experiences has become the cornerstone of retail players and digital twins are playing a crucial role in that by creating virtualreplicasfor customers. This helps create bespoke experiences for customers and increases brand value and loyalty in the eyes of the consumer.
Power generation: Power generation companies today benefit highly from the use of digital twins as they can make virtual copies of the turbine and related power machinery to assess their performance and generate possible improvements.
Healthcare: Surgeries and other medical procedures have been upended with the adoption of digital twins as doctors can perform them in a simulated environment using digital twins of organs. They can even predict the location of a disease and evaluate the risk factor of the patient before doing a physical operation.
Urban Planning: Town planners and civil engineers involved in planning of cities and towns today are readily making use of digital twins to visualize and guide themselves on creating more eco-friendly and environmentally sustainable cities. Making a virtual twin of a city offers constructive insights and solutions to complex challenges that are faced by modern cities.
Digital Twins and IoT
IoT sensors are a critical part of the digital twin functioning. They work as an enabler of digital twins by providing the connection and access to intelligence in the physical world. IoT sensors are embedded in a physical product and offer full visibility into the nitty-gritties of that product and its full lifecycle. The sensors help with the enormous amounts of data that is needed for the digital twin tools to analyse the effectiveness, accuracy and efficiency of the product without affecting the real-world product. For example, manufacturing plants have IoT sensors implanted in various applications that understand and simplify the process flow, track the products, manage the work in process and optimize the material flow movement in the warehouse and also the inventory level.
Besides looking after the operability and performance of a system, product or process, IoT sensors reduce asset downtime and preventsystem failure through timely maintenance requests. With regular software updates, patches and reboots for the deployed assets, IoT sensors can enable mission-critical systems to optimize resources and further asset utilization.
Digital twin vs Simulation
Digital twins and simulations may use the same virtual models to reproduce a product or system’s functions, but they are not the same. Digital twins offer richer detail and more accurate representation of the product which is conducive for study. Besides, in a simulation, one can study only a particular process whereas a digital twin creates a virtual environment that allows one to study multiple simulations backed by real-time data along with a continuous flow of information between the twin and the sensors that collect the data. This allows the digital twin to make changes to the active simulation in real time. Further, a digital twin can mature through the product’s lifecycle as data is continuously collected and analysed, something that is not possible with a simulation.
The differences don’t stop there. A simulation can showcase what can happen to an actual product, but a digital twin can replicate of what can happen to the product in a real world. Also, since there is a two-way flow of information where the sensors feed the digital twin with predictive data and get feedback and insights, thus helping product engineers analyse how the product will react to changing demands, requirements and business conditions.
Finally, we talk about the scope of use that is offered by simulations vs digital twins. While a computer-based simulation allows design engineers to test various scenarios against set parameters for a product design, a digital twin works against the areas of business workflow. This allows to find uses outside of design, helps improve process and products, and make better business decisions. In a nutshell, digital twins are dynamic as they are in constant communication and data exchange whereas simulations are static.
Digital twin vs BIM
BIM methodologies and processes have disrupted organizations by creating digital representation of assets and delivering robust, efficient and innovative working models. They have become a valuable tool for modernising operations and connecting information and ideas between different stakeholders.
BIM helps to establish a clear project vision before even the design of the project sees light, or work starts on the site. However, it still fails to demonstrate its value through an asset’s lifecycle. In order to continuously deliver greater value beyond design and construction, collaborative data models and real-time insights are needed. This is where BIM lacks and digital twins score a brownie point and become extremely useful.
Digital twins use BIM data and display it in detail-mapping out the assets, spaces and systems used within a building. They provide stellar insights into how assets work, track their history and coalesce project data with updated performance information. While BIM stresses on the best practices of building design, feeding data into a model;a digital twin takes the BIM data (design, construction and operation), unifies it and simplifies it for collaboration between the design and construction phases.
How digital twins help in predictive maintenance?
Digital twins are evolving with time and industries across sectors are discovering their potential and practical usability. As we have discussed earlier, digital twin applications consist of the amalgamation of different sets of technologies including IoT sensors to 3D CAD files to augmented reality, that make up the whole ecosystem. With this white-hot technology that runs on continuous data feeding, the concept of predictive maintenance becomes not only tenable but a practical one.
Let’s see how. With digital twin, sensors can monitor an equipment or system at the component level, understanding and analysing the operation to its minutest detail. If a component breaks down, that information is recorded and relayed by the digital twin to the system processor. As this happens over and over again, a pattern develops, allowing the digital twin to predict when and where the next breakdown is going to occur with superior accuracy.
With predictive maintenance service offered by digital twin, industries can make the most of their data to increase customers uptime. Since you already know about your equipment’s performance limits through predictive data, you can unlock business benefits, increase ROI and productivity and serve more customers, as your equipment is fixed before it breaks down. This also increases the good will of the organization and increases customer satisfaction, leading to higher renewal rates and better net promoter scores.
All these wholesome benefits are not only an outcome of what you know through predictive insights, but also what you learn through data gathered over time. You can adjust your equipment’s workload without requiring any expert technician to monitor the equipment. This level of visibility and transparency into a product’s life stream is only possible with the help of digital twins.
Digital twins in Engineering
Engineering professionals are one of the first to adopt digital twin technology. Using this tech, engineers can visualize highly detailed, digital versions of their facilities and equipment, analyse their strengths and weaknesses and understand effects of changes made before implementing them.
Digital twin software has become a critical tool for engineering organizations, who are increasingly relying on them to optimize product structure, fit and function as well as enhancing the quality of the product to a considerable extent. For instance, offshore oil and gas installations projects have involved predictive digital twins to cut the weight of the steel structures and make them as lightweight as possible without compromising on safety. With the help of real time data feeds and machine learning prowess, digital twins have achieved incredible accuracy, giving engineers confidence about the future possibilities that can be accomplished through this next-gen tech.
Further research has shown the promise of digital twin technology in case of infrastructure monitoring needs. Regular checks with digital twin software to monitor the usage of critical infrastructure like dams, power stations and bridges and ensure its safety and functionality, can help engineers plan when repairs are needed, enabling them to avoid potential risks and save significant costs.
Besides this, digital twins come handy for civil engineers while working on futuristic projects like settingup a new city or township. They can harp on the minutest details about town planning by collecting information from IoT sensors on things like roads, parks, areas of congestion, community centres etc. and make beneficial changes before embarking on such projects.
Digital Twins in Manufacturing
Manufacturing plants at a single glance may look complex with highly complicated, mind-boggling pieces of machinery interacting with each other. Understanding how various components of the plant are functioning and where adjustments are required to improve productivity can take lengthy man-hours and enormous effort. But when you have thousands of sensors attached in critical functioning areas of the plant that are constantly streaming data toa digital twin software, you can easily accumulate insights about the plant’s overall performance and even at component level. To take it further, IoT sensors can even gather granular data viz. structural thickness, temperature and other conditions at the plant.
With digital twins, manufacturers at any production facility can build prototypes and test new products at a fraction of the cost. Take an electric supercar for instance, where the entire car, its mechanics, aerodynamic features, software, electrical components and even its physical behaviour can be mimicked in a digital twin. 3D engineers can also place a physical component in the driver and passenger seats to assess constraints like ergonomics and human-machine interaction. They can create and test every component of the car as many times as necessary without any limitations to verify the inter-system components. Not only that, with the digital models, engineers can push limits and put the car through virtual crash tests and road driving scenarios. This helps in determining inefficiencies and address them on the spot. It also reduces operational costs and decreases time to market. Putting the whole story short, digital twin has become a key enabler for efficiencies across the whole manufacturing chain.
Digital twins in healthcare
The healthcare industry is not far behind to adopt digital twin technology. As per reports, the global healthcare digital twin market size was valued at USD 426.6 million in 2021 and is pegged to grow at a CAGR of 25.6% from 2022 to 2030. Digital twins in healthcare have the potential to transform not only the business operations but also the improve the lives of the global human population by building digital representations of healthcare data including human physiology, lab test results and medical devices.
By deploying digital twin technology, hospitals can create a virtual twin of the entire facility to review operations, detect bed occupancy rates, optimize staffing requirements and smoothen coordination of internal processes within the hospital. Digital twins provide a safe environment to make changes in hospital operations and processes andtake quick data-driven decisions to reduce the time taken to treat critical patients.
One of the most important use cases of digital twin in the healthcare industry is building a model of a patient’s body, physiology, lifestyle habits and medical history to create customized medicine and treatment plans. Digital twins can use vital data such as blood pressure, oxygen levels and dietary charts at patient level and create personalized simulations to track the reaction of the patient to different treatments and come out with a full-proof treatment plan in the end.
Further, digital twins can aid in the design, development, testing and monitoring of drugs and asset facilities like imaging devices, respirators, surgical devices etc. Healthcare experts can carry out alterations in design or materials and test them out in a simulated environment before manufacturing. This will iron out equipment failures, save money and make healthcare products more robust.
The future of digital twins
Digital twins will continue to reshape the foundations of engineering, manufacturing, healthcare, automobile and every other industry on the planet. A fundamental change is seen increasingly in asset-intensive industries where operating models are getting disrupted and the integration of assets, equipment, processes and systems with digital twins is becoming a new normal. According to data from Statista, by mid 2020s, the global market for digital twins in just the manufacturing industry is expected to reach more than $6 billion, a tenfold increase from 2020. And other industries will follow suit too with their virtual replicas.
With extensive research, education and marketing by technology vendors in this sphere, digital twin technology is getting fast adopted by companies worldwide. According to Gartner, by 2022, one-third of the companies worldwide that have implemented IoT would have deployed at least one digital twin in production. With increasing demand to boost efficiency and productivity in various industries, experts are finetuning IoT infrastructure and harnessing AI and machine learning capabilities to improve the design and workflows of their existing facilities. In the future, almost every product or equipment will have its own digital twin as long it is generating data which can be captured and analysed for more path-breaking progress.
Besides, 3D technology is slowly and steadily entering the metaverse realm that promises to create a virtual world where people will socialize, shop, play work with each other and even visit places. This will unlock a new economy in the coming years with innumerable opportunities.
Companies like Nike, Samsung, Hyundai, Coca Cola have already broken into this space and are considering engagement strategies to reach audiences of all ages with digital avatars. It remains to be seen how quickly other companies and organizations can get their feet wet in this exciting new technology. While it may be difficult for many to wrap their heads around it, taking time to learn about how digital twins and how they can fit into the virtual scheme of things, can be beneficial for them in future.
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