With the evolution of simulations and 3D tech, innovative technologies are starting to emerge. Digital Twin is an emergent technology gaining massive momentum in the industry. As the Fourth Industrial Revolution comes closer, digital twins’ technologies are maturing and evolving rapidly, increasing the utilization of practical applications of digital twins.
Moreover, with the incorporation of technologies like Artificial Intelligence (AI), Machine Learning (ML), or Big Data, companies are converging digital twin technology with emerging technologies like Augmented Reality (AR) and Virtual Reality (VR). As a result, it enables rapid design and development and allows smart solutions in production, sales, logistics, and the global supply chain.
Digital twins are a massive boon for rapid prototyping during the design and development of a product. Furthermore, due to the ability to enhance current manufacturing & product development, industries worldwide are incorporating digital twin technology in their business, product development, and even consumer experience. The current global digital twin market sits at 5.4 Billion US Dollars, but this slump is due to the COVID-19 pandemic shutting down many industries and production along with it. As a result, the world was simply not ready to adopt it rapidly.
However, with adaptation, digital twin technology is rapidly rising in applicability and usability and increasing accessibility even at the end-user side. With this in hindsight, researchers predict that the global digital twin market will cross 63 Billion US dollars by 2027. This estimation shows a high annual growth rate of 42.7%. Furthermore, it shows that the market, industries, and even consumers are moving towards the much-awaited digital transformation of Industry 4.0.
Read more: Reduce Costs with IoT and Digital Twins
To understand the practical applications of digital twins, we first have to understand the technology itself.
Know Your Technology: Digital Twins
Digital twins technology is evolving in both its technological reach/sophistication and its meaning. While the idea of digital twins is not new, it is undoubtedly going through a massive revelation in the industry currently. Furthermore, with technologies like 3D models, simulations are rising. As a result, digital twins are also gaining momentum in the industry.
The digital twin accurately represents a real-world physical object or an environment in a digital form. Do not confuse digital twins with 3D models or simulations. It is much more than that. Digital twins represent a subject (any object in the real world) not just in a static manner but in a dynamic way. It means that the digital twin will always represent the product/object throughout its lifecycle. The twin always reflects any change or modification on the real-world object or vice versa, in which the real-world object demonstrates a shift in the digital twin.
While 3D models just simulate some properties and structure of an object, the digital twin represents and accurately reflects all properties and characteristics of the real world. From design, materials, behaviors, and properties, the digital twin represents them all. So it becomes easier to reflect changes of both the digital twin and the real object. Furthermore, it remains accurate throughout the whole design phase, developmental phase, prototyping, or even after production for maintenance or repair, effectively reflecting all stages of a product.
Furthermore, unlike a 3D model, which is just an informational model, digital twins react and behave in a certain way similar to the real object in different environments and conditions. Due to this, the digital model is more dynamic and adaptive. Moreover, with AI at its core, digital twin technology enables communication, updating, and even learnability similarly to its real-world counterpart through the exchange of data among each other.
With technologies like AI with ML or data analysis, digital twins are becoming more accurate and smart. It also enables more flexible product phases for the design and development of a product. They help product developers explore different solutions freely without concerns relating to physical material costs or loss. Companies worldwide are rapidly adopting digital twin technology, enabling various applications and use cases to arm themselves with this type of revolutionary technology.
Here, we list some of these potential uses and practical applications of digital twins technology as shared by 13 different tech experts of the Forbes Technology Council.
1. To calculate product performance statistics and measures
Michael Campbell from PTC shares that with innovations enabling digital twins to be a comprehensive digital equivalent of a product or process in the real world, product developers or manufacturers can understand how the product is in use or performing. They can even track if the product or supply line may break down or is low in supplies. Campbell remarks that all this can lead to a better experience for the end consumer.
2. Simulating complex manufacturing scenarios
Eugene Khazin from Prime TSR remarks that digital twins have great use in the form of a precise virtual representation of a production supply chain. It will use advanced analytics and machine learning systems to predict and simulate different complex “what-if” scenarios without running these in actual production. As a result, manufacturers and production sites will utilize resources more efficiently and accurately to increase product quality.
3. Removing risks from different experimentations and analysis
Kathleen Brunner from Acumen Analytics Inc states that digital twin technology is a game-changer saying that it can eliminate the need to perform various experiments and studies with actual equipment or processes. Digital twins offline can enable multiple investigations of various complex and what-if analyses of different scenarios. Practical applications of digital twins allow optimization of other parameters and outputs with a digital representation or replica interface that responds to human and environmental inputs. These digital experiments significantly de-risks these physical experimentations by deeming them unnecessary.
4. Improving software products
Vince Padua from Axway explains that one way for the practical application of digital twins is to leverage actual customer usage data. This data can improve enterprise software products through its analysis. The data collection can include whether users are using a particular feature and how they receive notifications or collaborate with other users. Developers can create a digital twin of the customer experience using this data, while Artificial Intelligence can determine and predict the fastest and most efficient ways to solve various issues.
5. Real-Time information sharing and analysis
Gerald Rousselle from One Concern shares that digital twins can produce new functionalities since they represent the physical world in a form that computers can understand. He says that a GPS in mobile can be a digital twin of the natural world to provide accurate and real-time direction and navigations to your destinations.
6. Creating valuable digital assets
Ghufran Shah from Metsi Technologies Ltd explains that there is a lot of hype around cryptocurrency and non-fungible assets/tokens or NFTs. He clarifies that NFTs are a way to represent a physical asset such as a picture, video, or even a music clip in a digital format. Once a physical object is mapped into an NFT, a unique identity of this asset can now live forever within the blockchain. These assets can even gain monetary value and become valuable collectible.
7. Facilitating hybrid teaching methods
Zeng Fan from the University of Miami Herbert Business School says that the schools and universities are equipping classrooms to accommodate virtual conferencing tech for virtual teaching due to the pandemic. This technology is similar to one of the practical applications of digital twins, face-to-face and digital/virtual class deliveries. This technology can also be in use for recording asynchronous digital course content.
8. Improving vehicle safety
Stefan Kalb from Self Engine explains that it's costly to use real cars and crash test dummies to get actual life data about car crashes, potentially saving lives. If digital twins technology is used, it can collect sensor data from inside a car as in the real world. This data, over time, can go through analysis and study and perform numerous cost-effective and efficient car crash simulations. These simulations can provide data that can improve the safety of real-live cars.
9. Supporting sustainable clothing practices
Julia Dietmar from Vue.ai explains that an excellent example of digital twin technology can be a “digital passport” for different pieces of clothes that are manufactured. Such “passports” can contain various information such as product attributes, raw materials, factory information, and even previous owner information. It can prove to be very useful for sustainable clothing practices.
10. Collecting and providing input for databases
Vitaly Kleban from Everynet says that the lack of ML and data analytics data is a genuine concern, even putting multimillion-dollar investments at risk. But digital twins can serve as an interface between real-world hardware and sensors to collect data from the physical world. The practical applications of digital twins can even prove to be a key to providing enough data for ML systems.
11. Preventing sports injuries and enhancing athletic performance
Laurie McGraw from AMA explains that the NFL has a digital twin for every player through field cameras and sensors. It can recreate every move or body posture of the players. This level of sophistication has huge potential regarding injury prevention and even improving player and game performances. These types of data and information can prove to be very useful for more than just elite athletes.
12. Providing personal assistance
Kerrie Hoffman from getting Digital Velocity and Focal Point Business Coaching state that smartphones are already digital twins of every person. Smartphones are already acting as our digital twins since they provide various functionalities like “Swipe to Pay '' when entering a coffee joint or providing alternate routes when there is a traffic jam ahead.
13. Optimizing traffic flows
Joaquin Lippincott from Metal Toad explains that practical applications of digital twins in the transportation sector are enormous. With smart vehicles and smart cities, planning and real-time adjustments to traffic are possible, optimizing traffic flows and saving time. Such technology may be dangerous, but we can test, optimize, and later implement such technology much more safely with digital twins.
Building intelligent infrastructure with digital twins has helped several companies to collect, extract, and analyze data. Digital twin technology or virtual twin is overgrowing with increasing accessibility and adaptability. As Industry 4.0 comes closer, technologies surrounding digital twins are also maturing and continue to develop. With the incorporation of technologies like the Internet of Things (IoT), data analysis, and Artificial Intelligence (AI), digital twins enhance R&D innovation with intelligent services like automation, self-monitoring, and real-time optimization. It enables rapid design & development and smart solutions in production, sales, logistics, and overall supply chain.
With the ability to enhance current manufacturing & product development, industries worldwide are incorporating digital twin technology. We can already see this accelerating adoption of digital twins across the industry. Although the global twin market was currently at 5.4 billion US Dollars in 2020, much of its slump is due to the worldwide pandemic. In addition, several industries shut down due to lockdowns and social distancing being the new norm during 2020 because of COVID-19. Nevertheless, the digital twin market is slowly rising again, with a tremendous rise expected after 2021. As a result, the global digital twin market will likely reach 63 billion US Dollars by 2027 due to a high growth rate of 42.7% annually.
What is Digital Twin?
While the idea of building intelligent infrastructure with digital twins is not entirely a new concept, due to its current exponential rise and growth, digital twins are undoubtedly growing more and more prominent. Along with the advancement in IT and digital technology infrastructure, digital twins are also evolving rapidly. In general, the concept of digital twinning represents a physical object or environment in a digital form that possesses its accurate characteristics and behavior. While 3D models and simulations also can describe an object or environment, twins systems do more than that.
A digital twin generally represents a physical object or environment not just in a static manner but in a dynamic form. A digital twin represents every phase of the lifecycle of a physical object or environment. A digital twin represents a physical object or environment from its design phase to manufacturing and maintenance and changes due to re-resign, iteration, and refining the object.
Hence, a digital twin is less of a 3D model rather more like an information model. Unlike traditional 3D models, building intelligent infrastructure with digital twins needs a more dynamic and adaptive approach. They can evolve and change over time concerning changes and enhancement in information and data. Digital twins can communicate, update and even learn similarly to their physical counterparts through data exchange with Artificial Intelligence at its core.
Artificial Intelligence with technologies like Machine Learning and Deep Learning enables a digital twin to behave as accurately as possible in contrast with its physical counterpart. Due to this dynamic nature of digital twins, they are in use to explore solutions, detect and prevent problems even before they happen and essentially plan for the future. Armed with these intelligent and smart solutions, companies and organizations worldwide rapidly adopt these technologies in their operations and global supply chain.
Building Intelligent Infrastructure with Digital Twins
Digital twins have five levels of sophistication. Ranging from a level 1 twin that can describe and visualize the product to a level 5 twin model that can operate autonomously, different levels of digital twin require different levels of infrastructure. For instance, a level 1 twin does not require advanced Artificial Intelligence or Machine Learning systems, but a level 5 twin does need them. Level 2 digital twin is an informative twin that needs to incorporate additional operational and sensory data. Furthermore, level 3 is a predictive twin that can use these different data to infer and make predictions. On the other hand, the Level 4 digital twin is a comprehensive twin that can consider and simulate future scenarios to predict and learn from them.
Building digital twin technology includes converging technologies like IoT, data analysis, design & development of the twin either in 2D or 3D, and incorporating AI and technologies like machine learning and deep learning. The digital twin infrastructure is not only in a digital form but also in physical form. This is because a digital twin simulation model resides in a digital format and connects the physical world alongside it. This connection is the representation of both digital models and physical models such that they represent and replicate each other. Every change in the digital or physical model must be synchronized, and both should also respond to each other’s differences.
The actual connection is made through digital models. We can link the physical world with the virtual world by twins modeling and simulating the physical world to map and represent it in digital form. On the other hand, we can connect the virtual world with the physical one by replicating any changes and updates made in the virtual world in the physical world itself. It will ensure that neither the digital form nor the physical form is not synchronized.
In digital twin technology, synchronization must be in real-time when building intelligent infrastructure with digital twins. Real-time synchronization and simulation of the product is the following infrastructure for digital twins. Whenever a product is in the developmental phase of production, the status of the digital twin must also reflect that. The changes occurring in the digital twin must also be replicated in the physical product. Therefore, the changes in materials, processes, environmental, and every other change must be synchronized across physical and digital forms.
Apart from this, the digital twin infrastructure also requires data analysis for deep learning and intelligent systems. Artificial Intelligence generally powers these intelligent systems along with Machine Learning and Deep Learning capabilities. This is necessary for smart analytics and prediction. ML and deep learning systems must be capable of analyzing substantial amounts of data. This data must be representing the actual physical product in real-world environments. Such data are generated and collected by sensors placed in the physical world and physical development.
The data collection is a crucial metric for a system to detect anomalies or errors through analysis in the digital twins platform. Usually, ML systems process these data types and perform pattern recognition to make predictions or suggestions. Thus, these systems enable self-monitoring, predictive maintenance and diagnosis, alert systems for possible future errors, and detection of abnormalities or inconsistencies in the product.
Due to this, the data must be accurate and representative of the actual physical product and environment with great precision. These types of data also are helpful for the corporations or organizations for their product analysis and study.
These infrastructures together enable all the digital twin advantages. The convergence of these technologies is a complex task. Nevertheless, the resultant solution offers an intelligent system that can track past system analytics to predict future solutions and real-time product optimization. Companies are rapidly advancing towards implementing digital twin technologies in their platforms and systems to leverage such benefits.
Building the Infrastructures
Building digital twin infrastructures is a very complicated and complex process. Since digital twins incorporate various technologies together, it is tough to integrate these technologies to work together flawlessly. Only with such integration can one enable proper digital twin technology and can leverage its benefits.
Since the technologies part of the model twins infrastructures are different, companies must be willing to take on R&D for every technology when building intelligent infrastructure with digital twins. Moreover, if not for flawless integration, the technologies must at least be working together, which is a challenging task. However, technology is rapidly growing, and so is its accessibility and ease of use. Hence, integrating these technologies is increasingly easier to enable the tech stack for digital twins.
With the power of the cloud, technology today is dependent mainly upon real-time computing. With the help of the cloud, companies can leverage virtually endless amounts of computing to enable various services, including digital twins. Furthermore, cloud computing allows companies to build intelligent systems that are ideal for integrating multiple infrastructures of the digital twin technology.
One of the most prevalent uses of cloud computing is Artificial Intelligence. Due to the nature of Machine Learning and deep learning, immense computing power is necessary to develop these systems. Cloud computing shines brightly in this field due to its vast pre-built infrastructure and network of computer systems. In cloud computing, these computer systems are connected through an extensive network of servers and processing systems. Cloud computing service providers serve this network of different systems as a single system with enormous computing power.
Alongside this, a system for efficient and accurate modeling of the physical world with high-performance systems for real-time optimization and synchronization is mainly necessary. Moreover, deep learning and data analytics with intelligent AI systems to enable smart solutions with automation at its core is also imperative. Furthermore, a unified system integrating all these technologies is crucial while building an infrastructure for digital twins.
Companies like FS Studio pioneer product innovation and transformative R&D technology through already established and proven digital infrastructure. Since deploying and building intelligent infrastructure with digital twins is very complex and challenging for companies and organizations, FS Studio provides innovative and smart solutions for these problems. Consequently, companies can focus on their primary product innovation rather than shifting their resources towards building a digital infrastructure.
Challenges of creating digital twins are increasing exponentially, especially with the advancement of technologies like simulation, modeling, and data analysis, digital twins of objects and environments are increasingly becoming more accessible and adaptable across various industries. Furthermore, with the integration of Artificial Intelligence with Machine Learning & Deep Learning, digital twins will transform industries across different spectrums, including the manufacturing industry.
The Fourth Industrial Revolution, or FIR or Industry 4.0 in short, is the automation of traditional manufacturing, production & other related industries with the digital transformation of traditional practices through modern technologies. Thus, industry 4.0 will be the age of digital technologies. Machine to Machine communication (M2M) and the Internet of Things (IoT) will work together to enable automation, self-monitoring, real-time optimization, and the production industry’s revolution.
Digital twins will be at the forefront of Industry 4.0. With its power of rapid designing & development, iteration & optimization in almost every engineering process & practice, digital twins will enable new opportunities and possibilities. In addition, digital twins will transform various manufacturing & production processes, drastically reduce time & costs, optimize maintenance and reduce downtime.
While digital twin technology is not entirely new, its growth and adoption are skyrocketing across various industries in recent years, while the challenges of creating digital twins are also rising. As a result, the valuation of the global digital twin market was sitting at 5.4 billion US Dollars in 2020. Furthermore, although its market was experiencing a slump in 2020 due to the COVID-19 pandemic, it will undoubtedly recover and experience exponential growth again. Consequently, researchers expect that the global digital twin market will reach 63 billion US Dollars by 2027 while rising at the growth rate of 42.7% annually.
Over the last decade, the evolution of the manufacturing and production industry has been mainly focusing on reducing costs, increasing quality, becoming flexible, and reaching customer needs across the supply chain. The manufacturing industry is adopting different modern technologies to achieve these goals. Millennium digital technologies have also been part of this technology stack due to the innovation and opportunities it brings to the table.
Different companies and organizations are using twin tech accordingly in different scales and nature. Due to this, the technology in use varies across the industry, such that some industries use the latest bleeding-edge systems while others use legacy and proven techniques. Companies generally use the latest tech when it becomes available to use the latest features and functionalities. On the other hand, proven legacy systems are in use due to their stability and ease of use.
Likewise, different uses of twinning sims in various industries possess other challenges. Apart from this, integration technologies like the Internet of Things (IoT), cloud, big data, and different approaches to digital twin integration will only increase the challenges for digital twins in terms of the sheer complexity of implementation. However, this also presents an enormous opportunity for industries to adopt and align these technologies to suit different needs to solve these complexities and challenges. Subsequently, companies like FS Studio solve the challenges of creating digital twins, providing a platform for the manufacturers or companies to work on without dealing with complexities.
Generally, the goal of any twin manufacturing is to create a twin or model of a real-world object in digital form. Furthermore, the aim is to make indistinguishable virtual digital twins from the actual physical object. Therefore, from the perspective of a manufacturer or a product development company, a digital twin technology will create an actual physical product experience in digital form. Hence, a digital twin for a product, object, or environment will consistently provide information and expertise throughout the whole product cycle.
A virtual twin can also serve companies for feedback collection alignment, useful for the product or the design team. Results from various tests may provide results that can be useful too. The design/engineering/manufacturing team can compile this information, feedback, and results for multiple purposes from the digital twin model. Furthermore, this compilation can also provide additional insights into the product, which can be very useful to tweak, change or even redesign the product entirely. This digital approach will consume much fewer resources, effort, and costs than the traditional physical approach. Moreover, these changes will also be reflected on the twin's systems instantly as the teams make these changes. This will ultimately allow crews to perform true real-time optimization of a product or a manufacturing process.
It will drastically improve the efficiency of designing and developing a product or a process. In addition, digital twins also enable higher flexibility across the overall design and development process. Furthermore, this flexibility comes at a lower cost and additional agility in manufacturing or product development. Hence, digital twin technology becomes very appealing for manufacturers and product developers due to these advantages and benefits.
One of the main challenges of creating digital twins remains to be the convergence of existing data, processes, and products in the digital form to be easily accessible and usable for the current or future teams in involvement. Moreover, such convergence may also change a company’s complete organizational structure from their R&D technology and product innovation to sales and promotion. Furthermore, incorporating technologies like IoT, the actual development of 2D or 3D models & simulations, and data analysis for consistent process, quality & authentic experience of the product remains a very complex process.
Apart from this, the actual use of digital twins created is also another challenge. The infrastructure and platform needed to use such digital twins are also essential, albeit complex, things to build. For example, suppose a team can create a car’s digital twin for a car manufacturer company. But problems with digital twins are that there is no actual use of the digital twin except for visualizing the vehicle. Even for proper visualization of the car across teams, different platforms and tools are necessary to often serve niche use cases of the company.
For instance, a car company needs a motor, brake, acceleration, air dynamics, and other niche simulations for the digital twin of their car. The technology stack should be able to perform various maneuvers a vehicle performs on the road. Aerodynamics and gravity simulation is a massive deal for car manufacturers. Integrating these simulations is also a monumental task.
Along with this, for the actual process of testing and developing products, the platform has to simulate various objects, environments, and conditions necessary for such functions. Alongside this, the platform should also be able to report errors & statistical data on simulations running while constantly monitoring and diagnosing the product during its testing or development. Collaboration between team members on the platform is also necessary for a large-scale company. Integration of Artificial Intelligence and technologies like Machine Learning and Deep Learning is also a very challenging task to accomplish.
Digital twin technology is also often associating itself with complementary technologies like Virtual Reality (VR) and Augmented Reality (AR). The use of VR and AR in a digital twin platform will upgrade the realism and accuracy of the product experience. With realistic simulations and modeling in VR and AR’s capability to enhance a product experience, the 4.0 industry will incorporate these technologies at the forefront with digital twin technology, increasing the challenges of creating digital twins. Alongside this, integrating the digital twin with the actual physical manufacturing process is also a huge challenge.
Although companies will have to adopt this new industrial revolution 4.0 with digital twin-driven smart manufacturing, the overall process will not be that complex. The hard part is the convergence of different technologies to enable a platform for generating this digital twin and integrating it with the actual physical process in product development or manufacturing. However, since the digital twin simulation accurately represents the actual physical product, the product/manufacturing team will have almost no difficulty incorporating this digital twin tech in their physical process.
Therefore, companies like FS Studio help product developers and manufacturers to focus only on product development and design rather than the process of adoption of the digital twin. While different industries are transitioning towards Industry 4.0 technologies, various platforms and solutions establish themselves as leaders in cutting-edge technologies like the digital twin model with AR VR to eliminate the complexities present while the transition happens. It will help the companies and organizations focus on their primary and core goals instead of shifting their resources and concentrate on their growth to the next industrial revolution.
Realization of challenges for the convergence of technologies like IoT, design, and generation of 2D or 3D models & simulation and analysis of existing data remains. With this, the incorporation of Artificial Intelligence, Machine Learning, and data analysis also pose challenges regarding automation, self-monitoring, and real-time optimization. Subsequently, corporations and manufacturers moving towards Industry 4.0 must place digital twin technology at its core.
It will help companies and organizations transition smoothly towards the industry 4.0 revolution, which incorporates product development and digital transformation. With the power of rapid design and development, new production and R&D innovation will take over the industry, reducing the challenges of creating digital twins in the transition to industry 4.0. Subsequently, with digital twin technology, industries across the spectrum will be growing exponentially in their move towards the next industrial revolution.
Simulation in the digital twin can help the aerospace, manufacturing, and robotics industries in many ways.
How many times have you bought a new product only to find out it's defective? It is a huge problem that has affected many people in the manufacturing industry, and with simulation, we can help! A digital twin can be used for testing products before they are released. Let me show you how this works.
Imagine we have an airplane manufactured by company ABC Corp, and they want to make sure that their new plane design will work well without any defects. To know if their plane is safe enough, they need a simulator that displays the different flight conditions to test for safety. If the makers detect any problems during this simulation, they can fix them before production begins, so no one gets hurt.
This blog post explores the importance of simulation in digital twin technology for aerospace, manufacturing, and robotics. We will discuss why simulation is essential to these industries and how we can use it to improve efficiency.
Let's get started!
Simulation in digital twin for Aerospace:
Aerospace companies have become more competent. They are using digital twins to eliminate unplanned downtime for engines and other systems. Today, airlines can keep their aircraft in service longer due to digital twins' warnings.
A digital twin is the computer model of how an asset behaves in the aviation world. It accounts for variables like weather and performance to predict outcomes. The virtual model also provides actionable advice on what to do if things go wrong based on simulated scenarios. This strategy has been so effective at airlines that aircraft are flying more hours than ever before!
Digital twins are capable of recommending mission adjustments that will decrease wear on equipment, thus increasing longevity and success rate for a given operation.
Data analytics are a vital component of digital twins and can predict when an asset will fail. The sensors receive the data in real-time on specific failure points.
The models make predictions and help determine how long the running equipment has left before needing replacement or repair. It saves companies both money and valuable resources like human labor that would otherwise go towards maintenance efforts if they were done manually instead of digitally predicted.
Creating a digital twin is challenging without the necessary data. However, data about calibration details, the geometry of components, and mechanical assemblies could be enough for creating an effective model that will help improve quality assurance testing.
According to Aviation Today, "Boeing has been able to achieve up to a 40% improvement in quality of parts and systems it uses to manufacture its planes with the "digital twin." Essentially this means that before any aircraft component enters production, they are analyzed digitally using high-powered computers.
Imagine if you could test out how your new car will perform in any weather. Well, with digital twin replication that's possible! This virtual 3D model can go through a range of simulated environments like being underwater or enduring freezing temperatures - all before it ever leaves the assembly line.
Alongside these simulations are data fusion techniques that help gather information on an asset by combining different datasets such as images from sensors embedded into machines. Data fusion evolves alongside technological advances, keeping up-to-date with the piled-up data in volume, velocity, and variety. It can be crucial for businesses who want their products ready for anything life throws at them!
Data is the driving force in our industry. We produce an unimaginable amount of data every day, and it has to be processed by machines so that we can make sense out of it.
The flow from raw data to high-level understanding requires a complex fusion process at different levels: sensor-to-sensor, sensor-to model, and model--model fusion.
Designing a digital twin for one or more critical systems like airframe, propulsion & energy storage, life support, avionics, and thermal protection is recommended for success.
Digital Twin Simulation for Robotics:
For example, let's say you're building a machine that picks up parts from its bin. You want it to know where the function is and how big it is so your robot can grab them correctly without any mistakes or hiccups in production.
We need an algorithm trained by images of the items on top of our bins - which would then tell us what size each item was. We will also need a video feed captured by cameras positioned overtop these bins, giving us that cameras above images
A great example is bin-picking; people must manually place parts in many different configurations for a machine-learning algorithm to learn how it should pick up a part automatically.
This method is an example of supervised learning. When training a supervised learning algorithm, the training data will consist of inputted images paired with their correct outputs like bounding rectangles and labels describing what objects are in each image (e.g., "box," "can," etc.).
There's a lot to consider when you're teaching robots how to complete tasks. In addition to training them on what the job looks like, it also takes repetition before being trusted with delicate and potentially dangerous materials.
The robot must have had multiple rounds of practice for every task for its skill sets not only get better but continue improving overtime without any hiccups or errors that could lead to injury accidents down the line
A robust automation solution can take weeks and even months to converge, depending on the task. For example, a complex system will require more time than one which has few components. Additionally, some of your parts might be unavailable or still in production already - this could limit you from accessing them for training purposes.
"Digital Twin" is making significant leaps forward in industrial robotics, assisting manufacturers by not only setting up systems but also validating them for robust reliability using machine learning and integrated vision techniques. As a result, it can shorten the time taken significantly from months or years down to days.
In a virtual environment, the avatar replaces the real robot. So instead of spending all day in front of video screens and keyboards, it's now easy to do everything from your couch: launch a simulation on your computer and let the machine work for you!
In addition, the costs go down by about 90% because there are no lab fees or equipment setup charges.
Next, you bring your robotics into the physical world from the virtual.
The machine learning algorithm helps to learn what everyday objects and scenes look like when viewed by this device so that its actions are more in line with our expectations for how we would behave if given these inputs.
You can teach an old robot new tricks using AI-based facial recognition software!
Digital Twin: The Future of Manufacturing:
Digital twins are the future of manufacturing. With a digital twin, you can test and simulate before any mistakes happen with physical prototypes—saving time and money from costly errors that could have occurred through experimentation on materials or manufacturing processes.
In addition, manufacturers will never again risk releasing a defective product to market because they know what works beforehand thanks to their virtual representation by way of a "digital twin."
It is getting to market faster than their competitors is a challenge for companies. However, it can be possible with a digital twin as it cuts long steps shorter and reduces changes in production.
The product life cycle happens in the virtual environment where we can make all improvements much easier and quicker- perfecting efficiency and development time.
Imagine you have created this beautiful virtual prototype that has all the potential features. But, instead of wasting time test
One of the best features of digital twin technology is that it can help you predict problems before they happen. So, for example, every time one broke down, its virtual copy would start to analyze data from sensors to pinpoint any potential troubles.
It can solve many potential issues without any intervention from an operator by using predictive maintenance software that collects various sources of data through sensor readings to identify likely future complications with machinery. As a result, if you replace worn-out parts sooner rather than later, your manufacturing process will run more smoothly!
Simulation in the digital twin is reducing costs for industries.
For example, ASME reported, a 2020 study says that up to 89% of all IoT platforms will have a digital twin in 2025, while nearly 36% of executives across industries understand the benefits, with half planning for implementation within just five years from now.
If you're not already familiar with the concept of digital twins, then it's time to get up-to-date. A digital twin is a virtual representation that mirrors an existing physical system in real-time.
In other words, if your company has a manufacturing plant and wants to find ways to be more productive by reducing costs or improving product quality, implementing a digital twin may help!
Oil and gas industry operators benefit from digital twin through a quick response when something goes wrong. With digital twin technology, operators can use data they collect from sensors on their equipment to create accurate models that replicate how these machines operate in real-time.
It means that if an anomaly occurs, there's no need for expensive trial-and-error or lengthy troubleshooting procedures - open up your model, and you'll know what went wrong. In addition to saving money by avoiding costly repairs, this will save time which means more production time!
This article describes how digital twins are helping organizations make sense of large volumes of diverse data sources—whether internally generated or provided by third parties—and use them effectively for making.
Emerging Technologies and Digital Transformation:
The new face of the oil and gas industry is quickly becoming digitized. Emerging technologies allow for production to become a cycle, automated, efficient, and streamlined - but this also means that you get to deal with operational intelligence.
Digital transformation will affect every stage of a company's lifecycle- from upstream operations to midstream labor-management down into downstream sales efforts. Even services in oil fields can be managed more efficiently digitally through Emerging Technologies. It will challenge operators to transform substantial data sets acquired in various processes into actionable intelligence.
Oil and gas industry operators benefit from digital twin through advanced analytics in their plant operations to improve the performance of assets, reduce unplanned downtime, and extend equipment life. In addition to these things, it also allows for a greater return on investment by identifying complex problems.
Digital transformation provides opportunities for improved return on investment by identifying quick fixes upstream, midstream, and downstream processes.
In addition, with the digital twin, a machine's maintenance and operational intelligence are never compromised.
With predictive analytics for maintenance and prescriptive analytics for operations intelligence, your business will always have the edge over any of its competitors by being able to fix problems before they even happen! In addition, the augmented reality provides tools that improve both productivity time and the effectiveness of the repair.
Digital Twin Mirrors Manufacturing Big Data:
The oil & gas industry is a massive business that generates an incredible amount of data. The oil & industry data will typically have quality reports, process control history, operational deviations and variations, product blends and formulas, etc., related to the production process.
The Bureau of Labor Statistics found that this sector had more stored data than any other business or industrial sector in a recent survey among US manufacturers.
The data generated by today's connected world comes in a wide variety of formats and needs to be aggregated, analyzed, and converted into actionable information.
The digital twin is a virtual representation of your production plant that can provide personnel with operational intelligence. This process starts by combining Big Data, statistical sciences, rules-based logic, and artificial intelligence into one easy-to-use package called predictive analytics.
Advanced machine learning allows the company to discover complex problems shaping up in their manufacturing processes and then determine ways to resolve them before they become costly.
The move from predictive analytic models will eventually lead manufacturers out on top because it utilizes big data effectively without adding too much cost or complexity along the way.
Digital Twin and Machine OEMs:
The relative benefits of the digital twin will depend on many factors, not limited to complexity and quality. As assets increase in sophistication, demand for a digital representation is bound to overgrow, too - with one difference: ubiquity across its lifecycle. The genuine virtual version will contain information about design as well as manufacturing and service life.
There has been some debate over who should be overseeing them: those with knowledge or experts in data science? Without answers, we won't know how best to utilize their potential capabilities
The oil and gas equipment OEMs (Original equipment manufacturers) are traditionally the best informed about information, such as engineering analysis data. However, end-users of these assets require this operational performance data to be successful in their jobs.
For a digital twin to work effectively, the manufacturer should share the information or offer an online service-based business to monitor and optimize digital and physical asset performances.
It includes servicing, optimizing operations with real-time data analytics, improving safety in complex environments like offshore drilling rigs, or carrying out hazardous tasks like handling chemicals at a refinery.
Implementing this type of initiative could be done through partnerships between IIoT software vendors that develop solutions to support these new approaches. In addition, there are emerging opportunities within large organizations that have been adopting advanced techniques across their business units.
Manufacturers of long-lifecycle products such as gas turbines and pumps are coming to understand that after-sale service is a significant differentiator for them. Implementing digital twin services will improve efficiency in the field, which can be very helpful when considering how many people it takes on average to change out oil filters at most factories worldwide.
By connecting remote sensors with real-time data analytics, companies have new opportunities not only have they have never seen before but also ones that were previously unaffordable due to cost considerations or complex engineering problems involved.
Manufacturers who implement this intelligent technology into their manufacturing process stand poised to provide better customer satisfaction rates and reduced downtime through continuous monitoring, thereby increasing profitability by improving quality control metrics.
Oil and Gas Industry Operators Benefit from Digital Twin & Asset Performance Management:
With digital transformation, oil & gas companies are redefining their business models and operations, but these changes would not be possible without effective asset performance management (APM).
APM can help oil & gas firms to increase maintenance efficiency and effectiveness.
It helps to avoid costly unplanned downtime while minimizing the need for scheduled downtime. It also improves safety by cutting down on risks of accidents.
With this strategic approach to managing assets in place, the company's regulatory compliance costs will also decrease as well as minimizing the risk of non-compliance which is always a top concern when it comes to environmental protection regulations
Data is a valuable resource, but it cannot be easy to manage due to the sheer abundance and variety of sources. Modern APM can alleviate this by collecting all information into one system for ease-of-use and quicker analysis periods so that valuable insights are never lost again!
Imagine life without oil & gas. It would be much less convenient, not to mention plain dangerous. That's why you should invest in the industry today!
Operators collect data and analyze it. The approach enables companies to develop new techniques with better efficiency, safety, yield rates, etc., leading us towards a brighter future for all involved parties in your investments.
The technology around collecting and analyzing data has enabled many improvements for those invested in this sector. This work can lead industries into their "brightest" futures through increased production flexibility or more efficient operations...and it only gets easier when people are willing to dedicate themselves fully toward these goals.
APM is a new way to monitor and manage oil production from unconventional sources. APM integrates into the larger automation environment, enabling companies to take advantage of shale oil and gas opportunities, ultra-deepwater, or subsea applications.
Accurate and timely data is the lifeblood of a company's success. In today's business world, oil companies have to constantly adapt their operations to improve efficiency and safety standards for employees operating on site.
It becomes difficult to comply with regulations across different sectors without an efficient way of collecting accurate information about all aspects, from production levels and equipment status up through downstream applications like environmental impact reports or health & safety assessments.
Midstream operators can now benefit from improved visibility into what goes wrong when things go wrong to act quickly. It is possible because integrated APM solutions aggregate real-time operational event intelligence at every level - including plants, refineries, pipelines, and transportation networks.
Fossil fuels have powered the world ever since the Industrial revolution. However, Digital technologies like artificial intelligence (AI) and Blockchain are making the process of extracting energy more accessible, cheaper, more efficient, less risky - and cleaner!
Digital twin technology is a new, innovative innovation that has the power to change the way we work. For example, we can use this new technology to create digital replicas of our environments and assets – also known as virtual simulations – and have them interact in real-time with their physical counterparts.
It means you could simulate making any significant changes or decisions which would otherwise be costly!
Digital twins are changing today's way we operate by providing information about our environment and previously unavailable assets.
Oil and gas industry operators benefit from the digital twin significantly. The benefits include increased safety, improved production rates, lower maintenance costs, and reduced downtime. With these advantages in mind, it's no wonder why more companies are jumping on board the digital twin train!
Digital Twin decarbonizes the energy systems by bringing all technologies together. The virtual twin can model energy flows and changes of variables in real-time.
Initially, designers and engineers used it to build prototypes of new products, but it proved to have more practical uses. Moreover, integrating the Internet of Things and AI has strengthened it to carry out multiple tasks.
Digital Twin can model a raft of internally connected systems through big data analytics. So, Digital twin can aid the reduction of CO2 and other greenhouses gases released into the earth's atmosphere.
Digital Twin can map out all the aspects of the energy systems to help with decarbonization. Starting from electrical production to distribution losses and localized demand, it would lay out the whole system in the virtual environment.
The use of Digital Twin is expanding as we are developing the technology to be more effective with time. For example, it has proven effective in energy management, electrically powered public transport infrastructure management, and sector coupling.
It is not possible to derive the best outcomes from the Digital Twin without knowing the factors. Therefore, one needs to know these factors below to decarbonize the energy systems without failure with Digital Twin.
Get Digital Twin Experts on Board:
When you are up for decarbonization of energy systems, you'll need technical know-how. So we are expecting that your in-house talents already possess the understanding of electrical generation, supply, and distribution.
Next, you will need a more profound working knowledge of Digital Twin to use this for the digital decarbonization of energy systems.
You must have a clear understanding of building digital twins aligned with each phase of the decarbonization process. Additionally, you need to know how to deal with data modeling and intelligent data technology to make digital twin work for you.
It's better to outsource the Digital Twin project to experts if you lack the in-house skills needed to make this project successful. Companies have to deal with the unique sets of challenges of building and deploying a digital twin.
Digital Twin is a framework for building a bridge between the physical and digital worlds. Therefore, you must satisfy a range of requirements to develop and deploy virtual twins successfully, including:
The biggest challenge of deploying a digital twin is that it shares the same characteristics as the physical entity. The Digital Twin (DT) decarbonizes the energy systems better if the team can deal with the challenges below:
Make use of your best Datasets:
Preparing the data set should be the next step in your list before building the digital twin.
We're living in the big data age where high-quality data sets are crucial to success. Feed high-quality data into the Digital Twin and you can expect fantastic outcomes. Feed trash into the twin system and the result will be the other way round.
To sum it up, it means that you need to clean up, gather and structure data sets to set up the twin system. Also, cross-check the Data's relevancy in real-world settings to ensure you will be feeding high-quality sensible data into the twin system.
When you are swamped with data and need to analyze different sets for different purposes, it adds complexity.
For instance, analyzing meteorological data like temperature, dew point, wind direction, and speed next to electrical consumption in buildings from photovoltaic, solar thermal, and hydropower plants at locations worldwide can become messy.
In brief, you'll need to combine patience and years of professional experience to locate, collect and prepare the best data sets.
Digital Twin Decarbonizes the Energy Systems Better if You Avoid Preconceptions About Model or Configuration:
Start building and deploying Digital Twin with an open mind. Don't keep any preconceived notions about which specific model or design will work for your program.
Keeping an open mind will give you the flexibility to draw the best model and configuration from a variety of ideas and solutions.
Your digital twin will end up with average quality outcomes if you have a preconceived technological pathway and follow it.
Examine all the possible solutions to decarbonization before going with one specific approach. In this way, you can make the best out of the digital modeling and AI computing calculations and stats.
Adopt Digital Twin for the whole system:
Before starting your digital twin program, you must figure out the pathway to optimize the entire energy system.
You'll need to conduct an end-to-end analysis of the initial situation and the entire technological framework conditions. Digital Twin decarbonizes the energy systems better if you consider the parameters below of each site individually for decarbonization of the energy system:
Prepare the decarbonization strategy before jumping into the program:
You must set clear goals before starting the decarbonization program with the digital twin. Digital Twin decarbonizes the energy systems better if these factors are implemented:
Don't look for Instant success:
It's not possible to achieve success within a short period with digital decarbonization as it's a high multi-dimensional program.
The digital twinning of the energy system will need care because it's a complex model. So, building the model may take a few weeks to months, depending on the size of the venture. Also, onboarding the right people for the fieldwork and synchronizing all the steps together cannot happen overnight.
How Digital Decarbonization can Change the World?
Digital Twin decarbonizes the energy systems in a different class when you apply a tactical approach. Reducing carbon emissions in the atmosphere will help climate change and keep our earth greener. Both the private and public sectors will feel the impact of using digital twins in decarbonization.
Adopting decarbonization applies mainly to the utility sector. However, other high energy demanding sectors such as the chemical industries should think about it as well.
We cannot deny that buildings are responsible for about 40 percent of all current carbon emissions. So, digital decarbonization of energy systems can make a massive difference to the climate of tomorrow.
However, the question may arise: Is decarbonization possible?
Digital decarbonization would be hard to implement but not impossible to achieve. We can reduce indirect carbon emissions through electrification and clean energy only. It is hard to control direct carbon emissions. So, we cannot deny that heavy industries would still be responsible for at least 20% of GHG emissions.
Digitalization of industries has already begun. So, companies that won't embrace modern trends like digital decarbonization processes will be left out. Decarbonization and digitalization are getting popular. As a result, industries have to switch from their traditional business models to stay relevant.
Every company must adopt the digital decarbonization process. At present, businesses are in a situation where they need to decide on a sustainability strategy that will work from the economic perspective. To do that, they must transform into a lower-carbon business model company.
Suddenly, this change of direction cannot take place overnight. Therefore, cooperation between industries is required to implement a lower-carbon business model. The enterprises will need to review climate strategy, set measurable goals, and clearly define their action plans. They will also need to assess their performance from time to time and optimize as necessary.
Adopting digital decarbonization can benefit industries in many ways. For example, businesses can run cost-efficient systems designed for specific localities when Digital Twin decarbonizes the energy systems.
Digital decarbonization can aid district heating systems and energy storage management systems of the community. In addition, it can help to increase the capacity of electric vehicle charging stations while ensuring saving potentials in environments. Moreover, digital decarbonization can reduce electricity waste and daily operational costs.
Businesses that adopt digital decarbonization will gain the rewards of CSR too. First, they will achieve better brand recognition as environmentally friendly companies. It, in turn, will strengthen their brand image among the public.
Therefore, implementing Digital Decarbonization is a win-win situation for businesses, our environment, and society.
IoT and Digital Twins can reduce the costs in the manufacturing industry, minimizing unexpected downtime. These emerging technologies also help to perform complex simulations, offer deep insights and suggest equipment improvement. IoT technology also safeguards the interest of the manufacturer adding speed and flexibility in every situation.
What is a Digital Twin in simple words? Or, for that matter, IoT?
Digital Twin is the virtual twin or copy of the actual product. The digital twin connects the physical and digital worlds.
On the other hand, IoT, or the Internet of Things, is a physical "things" network. IoT platform is the medium for connecting and exchanging data between material objects. Generally speaking, we embed the sensors and software into physical objects over the internet.
As we live during the industrial revolution 4.0 (Industrie 4.0), manufacturers and industries embrace emerging tech to automate traditional systems. They are using emerging technology like AI, Robotics simulation, Biometrics to speed up the industry systems.
Emerging tech like IoT and Digital Twins are also reducing costs in astonishing ways.
IoT & Digital Twins Case Studies:
The evolution of IoT has made data transfer hassle-free by connecting sensors to the cloud and other "things." Apart from this, IoT is also serving as an effective tool for predictive maintenance.
Conversely, Digital twins leverage IoT to aid organizations in monitoring assets or processes virtually. Unfortunately, these are assets that are hard to check due to their distant location or a hazardous environment.
IoT and Digital Twins have unimaginable use in reality. For instance, power grids breakdowns create hindrance in every life, causing delays in businesses and services.
We can now tackle unwanted interruptions in power distribution thanks to IoT predictive maintenance.
Finland's electrical substation is the exemplary model of the predictive maintenance case study. In 2018, the electrical sub-station used Haltian's Thingsee wireless sensors for the first time. But, unfortunately, these sensors require manual checks from the human side.
The electrical sub-station used the sensors to collect temperature components, including humidity, air pressure, and distance.
This IoT-based predictive maintenance helped to increase efficiency in the electrical sub-stations while eliminating equipment failures. In addition, predictive maintenance helped to detect flopping assets and understand the factors leading to abnormal operations and disrupting schedule maintenance activities.
Finland's electrical sub-station isn't the only example of successful IoT in industrial applications.
Ericsson Panda manufacturing plant in China is another IoT case study we need to discuss.
The Ericsson Panda plant in Nanjing used Cellular IoT and connected 1000 devices to form a gigantic branch. In addition, the system had embedded IoT modules to send and receive data in real-time.
The IoT modules are said to transmit about 100 bytes of data per 8 hours from recent usage. Later, Ericsson Panda used the data in a cloud solution for analysis. The IoT solution costs just $20 per unit, will cut 50% maintenance work, saving USD 10,000 annually, and achieving breakeven for Ericsson Panda in 2 years.
The Ericsson Panda manufacturing plant is the first cellular IoT –based smart factory, and its immense success has contributed to the expansion of IoT worldwide.
Today, IoT technology has become a key element in the global supply chain already.
Since the beginnings of the industrial revolution, companies were eager to measure the temperature of the transported goods using the low-cost solution. IoT-based predictive maintenance and analysis applications in refrigeration systems help to understand when the system may fail. Therefore, it helped to avoid wastage of valuable agricultural goods and medicines and save money and time.
Similarly, companies have managed to keep the maintenance costs of factory equipment under control by 40%. IoT -based predictive maintenance has also helped to reduce equipment downtime by 50%. It reduced equipment capital investment by 3% to 5%. It saved the overall capital investment by 3% to 5% by extending the life of machinery.
Digital Twins can save money by predicting future failures. So, companies can repair defects at their earliest at a much lesser cost. It also recommends best strategies to improve the product development cycle, maximizing profitability. In this way, companies using Digital Twin can maintain a good relationship with their consumers.
As we can see, emerging technologies can help industries in the most remarkable ways. However, the expansion of Digital Twin and IoT isn't just limited to electrical sub-stations, supply chains, or manufacturing plants.
IoT and Digital Twin have expanded to other utility industries like healthcare, rail transportation, and oil and gas.
Oil and gas industries are adopting Digital Twins faster to minimize the costs of assets and productions. These industries have costly investments and handle them very carefully. Thus, it's no surprise that they aggressively adopt digital twins for modeling operations such as oil rigs, pipelines, and processing facilities.
Oil and gas companies have digitalized their systems to cut off weeks of unplanned downtime while reducing production costs. In addition, these industries have adopted predictive maintenance and IoT analytics to review historical data to detect failures in major components located at their offshore oil platform.
Digital Twins have transformed the transport industry as well. Today, the transport industry applies high-value rolling stock, such as locomotives, to maximize fuel efficiency and optimize maintenance.
The transport industry is willing to achieve the highest fuel efficiency possible to save hundreds of dollars to buy fuel. The rail transportation industry had reported saving about 10% on maintenance costs when they switched to condition-based preventive maintenance of rolling stock.
The digital twin is making remarkable contributions in the healthcare industry as well.
Q-Bios can be a great example to discuss. Q-Bios is the first clinical digital twin platform that harnessed the ability of digital twins to replicate anything indifferently.
Q Bios Gemini Digital Twin platform has built Mark-I, a computational biophysics model to scan the whole body. The company reported that Mark- I will examine the human body in 15 minutes and doesn't require radiation or breathe of the actual person.
Q Bios Gemini has claimed that Mark- I can work 10X better than the traditional MRI scanners for many critical diagnoses. In addition, Mark-I, the computational model, can eliminate bias or hallucination risk from AI and machine learning.
Another most significant advantage of the Mark-I is that it shields the patients from exposure to radiation, protecting them from running into the risks of developing cancer cells in the future.
Q Bios Gemini has received over $80 million from Andreessen Horowitz and Kaiser Foundation Hospitals to develop and expand its breakthrough whole-body scanning technology. In the future, the full-body scanning tech from Q Bios Gemini will provide data-driven and affordable care for all.
Medical and software companies are collaborating on digital twinning projects to create exact replicates of human body organs like the heart and the brain. The aim is to minimize risks in critical surgeries and aid organ donations.
Sim&Cure, a medical technology company, has built a digital twin called Sim&Size. This digital twin simulation will make brain surgery safer for Aneurysms patients as they will need less invasive surgery using catheters to install implants.
In another instance, Dassault Systèmes SE, a French software company, developed a Digital Twin heart using MRI images and ECG measurements. This digital twin model of the heart replicates the structure and some functions of the human heart. Now, heart surgeons can feed the patient data into the Digital Twin heart to determine whether the surgery will be successful.
Dassault Systèmes SE has launched the Living Heart Project in collaboration with academic and industrial members like Medtronic, Philips, and Boston. All the Living Heart Project members are working together to build safer and effective cardiac devices for patients.
All the major industries are gaining massive value from IoT and Digital Twins. In other words, they are saving and making money simultaneously.
According to the predictions of McKinsey & Co, IoT technology would reach $11.1 trillion in economic impact by 2025. In addition, Cisco reported that data derived from IoT devices would surpass 800 Zettabytes by the end of 2021. There's no doubt that industries using IoT devices are experiencing explosive growth.
These industries are witnessing such massive growth because they managed to cut off shocking downtimes with Industry 4.0 technologies and build the ability to predict future failures and make necessary repairs using a digital twin.
Sadly, many companies have no idea about unplanned downtime's costs, root causes, and consequences. According to Service Max, 82% of companies reported that they had experienced unplanned downtime for three consecutive years. In addition, these companies experienced an average outage duration of 4hours every day with a median cost of $2 million.
Service Max also concluded that 70% of companies have no idea when their production machines will need maintenance or upgrades.
So, we can say that companies adopting IoT and digital twins are increasingly performing better than those avoiding emerging technologies. It happens because IoT and digital twins improved situational awareness and aided industry leaders in making faster business decisions.
Simulation experts in industry 4.0 must have a passion for digital twin technology today for industry research. Digital Twin simulation is entering mainstream use as more industries are adopting this technology. According to Gartner's IoT implementation 2019 survey, 75% of organizations already use Digital Twin or plan to in a year. Notably, all the companies willing to adopt Digital Twin are implementing Internet-of-Things.
Digital Twinning is not a new technology. In 2002, Michael Grieves of the Florida Institute of Technology introduced the concept of Digital Twin publicly. In 2010, NASA showcased the first practical implementation of Digital Twinning to improve the physical model simulation of spacecraft.
We can see Digital Twin has been around for two decades now. Yet, many businesses are confused about the value of Digital Twin Technology. In addition, many companies don't know the use of Digital Twin in the modern energy, chemical, and process manufacturing industries.
Many companies still don't know that Digital Twin can enjoin the disconnected processes cutting out manual efforts that can be time-consuming.
For instance, interns or low-level employees will still follow the outdated method to gather engineering information. They would walk around one department to another to collect data required for engineering research.
Many engineers use CAD and PLM software and other sources to collect data to make informed design decisions. A few engineers are lucky enough to use enterprise search engines to pull information from various departments from hundreds of documents, folders, presentations, etc.
Now that we are entering the age of automation, companies must adapt to cultural change and access the right technology. They need to integrate technologies like Digital Twin to help teams gain information without any hassle. They also need to save employees from the pain of surfing through numerous record systems.
There are other reasons to consider Digital Twin to accelerate business innovation. Here we've discussed 3 of them below:
1) The Rapid build-up and expansion of data:
The business environment of the energy and chemical industries is already volatile. On top of that, the decision-making cycle is in an array across these industries due to piled-up data sources.
System Digital Twins made for entire plants, or factory systems can rescue energy and chemical industries. A massive amount of operational data can be collected, organized, and analyzed from various devices and products.
Human decisions are not rational, even if we make sound judgments after weighing evidence and assessing probabilities. It happens because the human brain tends to simplify information processing. So, cognitive biases, including memory and attention biases, influence human decision-making.
System digital twins can eliminate human bias for critical decision makings. System digital twins can also provide a single logical view of the actual situation based on evidence, probabilities, and analytics.
It is also essential that you know your needs before adopting Digital Twin Technology. Therefore, you must ask these three questions to ensure your success with Digital Twin:
2) What type of analytics should Industry players seek?
The factory systems and manufacturing plants involve complex processes today. So, measuring KPIs isn't easy now.
The digital twin can resolve this problem by providing deeper analytics from factory systems and plants, taking multi-dimensional factors and non-linear trade-offs into account.
The digital twin can build an accurate understanding of the future based on historical and present performances data. The digital twin can recommend the best strategies that can maximize profitability for the industries. Next, the experts will need to assess each recommendation and its impact to make the best decision for the businesses.
Therefore, industries can use the digital twin technology as a supporting tool to aid decisions enabling improved safety, reliability, and profitability.
3) Digital Twin Model Utility across the entire lifecycle of the asset:
Manufacturers use digital twins differently at each stage of the product development cycle.
Initially, manufacturers start working with Digital Twin Prototype or DTP. Then, manufacturers use DTP to design, analyze, and plan out the process to predict the future shape of the actual product.
In the next phase, manufacturers use Digital Twin Instance or DTI. DTI is the virtual twin of a physical asset. Developers will use DTI to run multiple tests and determine how the product will behave in different scenarios.
The DTI stays connected with the physical asset throughout its lifecycle. As a result, developers will add more operational data to improve it over time.
In the final phase, manufacturers will use Digital Twin Aggregate or DTA. Manufacturers use DTA to cross-examine the physical product, predictions, and learning based on the collected data from the previous phase.
People from engineering, operations, supply chain, shop floor even board room can look inside the assets and processes of the Digital Twin technology at every stage.
Companies integrate AI, machine learning, predictive analytics, etc., into the system with high hopes. They do it because they believe that digital transformation will cut out all the manual workload. However, when they realize that a lot of the work still depends on the human end, they get shocked.
Industries may have entered the automated age and have innovative IoT solutions at their disposal. However, automated systems cannot replace the human touch in many critical areas of business. For example, humans still need to implement and monitor automated systems in manufacturing plants.
Automation cannot replace other tasks like enhancing product design, building strategies, and growth roadmap, decision making, communicating with stakeholders, applying creativity to solve problems, etc. These areas will continue to need human intervention.
Companies need to set clear expectations when moving forward with the digital transformation of the assets.
The purpose of digital transformation and digital twin is to make the technical aspects of the job easier. In addition, Digital Twin technology will provide you the intelligence to help you focus your hard work on beneficial outputs.
Companies building Digital Twin Technology today are the pioneers of shaping the agile and intelligent industries of tomorrow. So, they need to develop the right digital twin platforms to leverage the full potential of digital transformation to create an exemplary model that others can follow.
The first steps will always be challenging. You can expect objections and hurdles to come your way. However, all these troubles are manageable if you know the proper ways to manage them.
Here is a brief guideline to follow for successful digital twin adoption in business:
To sum it up, digital twin adoption has the scope to attract more stakeholders' buy-in. Companies can show them the data-driven rewards based on concrete analysis instead of flawed predictions. So, the stakeholders will always have the know-how of the direction they are heeding with you.
"The true benefit of a digital twin: it gives you business intelligence to make better decisions in the future. It doesn't eliminate or minimize the work you're doing now, but it fundamentally changes what you're going to do next." - Former chief executive of Cambridge City Council, Andrew Grant
As we deduce the statement of Andrew Grant, it's a life lesson that industries have learned the most brutal way around the world after the Covid-19 shock. Thus, many enterprises are seriously considering the concept of Digital Twin and thinking big to expand it.
Companies are now interested in optimizing business operations based on the real-time insights gained from manufacturing plants and product use. As a result, they are more focused on satisfying orders, resolving root causes that are hindering growth, and maximizing factories' performance based on solid predictions.
We have already entered the age of automation as the 4.0 industrial revolution has begun. Today, companies maybe just interested in predictive maintenance. However, the use of Digital Twin Technology will expand where it will be integrated not for products but into manufacturing processes and entire factory systems.
There is no better time to get into Digital Twins than right now.
The global digital twin market is growing every single year, mostly in North America.
This is why we've created a digital twin statistics infographic, to illustrate just how much digital twins are growing year by year:
How using simulation and digital twins lowered development costs by an order of magnitude
A recent report by Market Study Report, LLC projects that the digital twin market will surpass $20 billion by 2025. What explains the growth? The government of Singapore, for one example, is spending over $73 million to build a “data-rich digital twin and improve public services with reduced cost for its citizens.” Like running a city, launching a product comes with significant expenses and risks. It’s important to have a relatively fast and inexpensive way to figure out if your product is going to work well in the real world.
At RSS, many of our clients come to us because their research and development process takes too long and is too costly, with the risk often outweighing the results. When we create a digital twin of a client’s product, it’s a game changer. We can accelerate product development and reduce risk by an order of magnitude. Here’s how it works.
When companies are preparing a product, it’s not uncommon for the research and development timeline to last a year or longer. Hardware is hard; that’s why so many software start-ups struggle when they get into the hardware business. They are used to a pace of iteration and development that isn’t possible with traditional hardware development cycles.
Another problem companies face is that a long and expensive R&D timeline increases risk and reduces their ability to experiment and tweak. There’s always the possibility that after the company spends time and money on an iteration, that iteration may not work. And even if the prototype is close to perfect, they have to start the process over again.
At RSS, we combat these problems using digital twins. According to the IBM UK Technical Consultancy Group (TCG), a digital twin is defined as “a dynamic virtual representation of a physical object or system. . . . It uses real-world data, simulation or machine learning models, combined with data analysis, to enable understanding, learning, and reasoning.”Using a digital twin gets us much closer to a software-style development cycle. Companies can iterate on design, conduct experiments, test assumptions, interact with the product, and repeat that cycle until they get it right. And because it’s all happening digitally, the yearlong timeline is reduced to a matter of months or weeks.Using digital twins also allows companies to experiment in a low-risk environment. You don’t have to spend money on procurement, materials, and production, and you know much sooner if you need to make changes before moving forward. Traditionally, the hardware development and design process is separate from the manufacturing process, which increases the risk of spending too much on an undesired result. But with digital twins, the development and design stage essentially becomes part of the manufacturing process, so your time and cost are reduced, and your desired results come much sooner. Finally, digital twins are a boon to marketing. With the images and footage that digital twins provide, companies can release promotional materials earlier and start selling their products even before they have created a physical prototype.
At RSS, we helped one of our clients, a major toy vendor, accelerate the product design of a toy quadcopter drone. We created a simulation of the drone that was physically accurate in terms of weight, weight distribution, center of gravity, thrust per rotor, and other characteristics.
The simulation helped the client prove that, when manufactured, their drone would be stable in flight. The simulation also provided a platform on which to develop special higher-level features of the product, like facial recognition and tracking the user during flight.
Simulating the drone involved simulating an entire system. Not only did we create a digital motor, body, and rotors, but we also incorporated principles of physics, including torque, energy consumption, and mass, to see how all these elements would interact in the real world.
For example, when we simulated the rotors, we wanted to see how long it would take them to get up to speed and exactly how fast they would need to turn to fly well and perform maneuvers the client was expecting. We validated and tuned the speed of the rotors until the drone simulation could perform the maneuvers with speed and accuracy.
We also needed to see how much torque we could apply to the rotors based on their mass. We knew, of course, that the battery would eventually drain, so we needed to calculate how much battery charge it would take to spin the rotor. The client gave us their best guesses about how much power the motor would draw at different rotor spin rates, and we applied those numbers to see what would happen.
We were able to validate and tune the design, demonstrating how it affected—and was affected by—every aspect of the system. Unlike drones in a video game, this digital twin included a simulation of physics principles, rotors, sound, and wind, creating a true representation of how the drone would behave in real life. Additionally, the simulation made it possible to test proposed paint schemes, colors, and some special maneuvers the drone could perform. None of this would've been possible without the use of digital twins.
It was important that our client could test how the drone would interact with the user. With our simulation, the client could put on a headset and simply turn their head or walk around as though they were really using the drone. The headset we used leveraged the Oculus Avatar SDK to provide user presence and used spatial context to allow the Oculus touch controllers to behave differently depending on user intent.
For example, let’s say the user is trying to get the drone to accelerate very quickly, which causes the drone to tilt too far forward. The Oculus Avatar SDK would sense the user’s intent to move the drone quickly but would adjust the rotors to keep the drone at a safe angle. Ultimately, the drone simulation had a small lag behind the user’s controls, but the lag is realistic for a physical drone. Our clients were able to experience these controls and see how they felt for a user, all before they had to manufacture a physical prototype.
Our client needed their drone to be able to track the user’s face. For this, we tested ray casting in our 3D simulation. With ray casting, the drone senses if there is any object between the camera and a face. If the ray cast hits the face, the drone can “see” the face. If something is in the way, the face isn’t seen.
We also tested the drone’s follow mode, which is when the drone is constantly looking for a face and noticing if the face is obscured. In the real world, users would have to look to see if the drone is going to fly into something it shouldn’t. With our simulation, we were able to test how well the drone could analyze a video frame—without having to actually analyze a video frame. This feature could have taken months to test, but we were able to test and tune it within weeks for our client.
In our simulations, VR is everything. With our client’s toy drone, not only could we validate designs, but we could also tune the designs by adding weights to change the flight characteristics in VR. We could then change the physical drone design to have more or less weight in different areas to match. VR also allowed us to revalidate or fine-tune the design when we needed to incorporate late design changes in manufacturing, such as a change in battery size.
VR made it possible to test how well the drone would fly in different sized rooms with varying numbers of obstacles. The drone featured obstacle-avoidance algorithms which we could simulate and test. We used the results from virtual testing to guide decisions about how much to limit the drone’s speed, how far it should look ahead to avoid obstacles, and what action to take when an imminent collision was detected.
We simulated audio for each rotor independently to make the user feel like they were immersed in the environment. When users move the drone forward, the rotors move at slightly different paces, creating four different frequencies. And these frequencies sound different when the drone is in different places relative to the user and other objects. In our VR simulation, the user can experience all those subtle sound differences.
With any drone, UX is key. One important part of the user experience with our client’s drone was its ability to respond to a user’s specific hand motions. Interestingly, hand tracking is a solution that is in high demand, but not many companies are offering it (13.4 percent of companies surveyed by Reuters Events said they were interested in hand tracking; just 6.5 percent of companies offering hand tracking were actively reaching out to companies). With our client’s drone, we simulated “peekaboo” behavior. The drone tracks the user’s hands and face. If the user moves the controllers near their face, this triggers the drone to display a peekaboo animation.
Through VR, we were able to test and tune UX features quickly and inexpensively for our client.
In business, we always have to evaluate the pay off of our decisions: what’s the ROI? This is especially true when you need to decide whether to move forward with a hardware project that has complex behaviors or integrations. When you start integrating things like computer vision, IMU/motion data, and complex behaviors, you are looking at a time-intensive, high-risk project that is extremely costly.
Now imagine compressing that timeline to get to a prototype to a matter of a month or two and then iterating on that in a matter of days or weeks. Imagine being able to not only visualize your product but also interact with it, code against it, and iterate on it, all as easily you would any software development project. That’s the power of a good digital twin. We can take our clients’ CAD files and simulate the system, allowing the client to interact with the simulated system in a virtual environment.
Here’s a sneak peek into what we’re working on now: We are bringing the power of reinforcement learning to speed up the development of complex behaviors. We are teaching an autonomous robot to perform complex maneuvers that we can then transfer to the physical platform. We’re working on improving the pipeline from simulation to deployment so there’s even shorter development time on the actual hardware platform.
To see how RSS can help you accelerate product development and reduce risk, visit https://www.roboticsimulationservices.com/services/