3d models - Robotic Simulation Services

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.

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.

The landscape of Robotics technology is evolving, pushing industries forward for a 360-degree approach to robotics. More so than before, today, robotic technology is progressing at a swift speed alongside its integration with technologies like Artificial Intelligence (AI), Simulation technology, Augmented Reality (AR), and Virtual Reality (VR). Robotics was always at the center of a future where industries are digital with automation at its core. However, industries that fully integrate AI and digital technology to enable automation with robots are still far away.

In the current world, car production and manufacturing is probably the industry with the highest level of robotic usage. One of the most prevalent uses of robotics and automation even in this industry is the Tesla manufacturing facility. Even though this is the case, Elon Musk, the CEO of Tesla, admits that robots are tough to automate and efficiently run without advancing digital technologies like AI and more innovative technologies like the Offline Robot Programming Software Platform or Robotic Simulation Services.

However, with the advent of Industry 4.0, the next industrial revolution, we will see some industries take a 360-degree approach to robotics through digital technology. Robotics technology is a crucial part of this transformation. Hence, enterprises will have to change their traditional policy to robotics with a new innovative and modern digital strategy to keep up with the changing industry and competitors.

With that said, industrial robotics is complex, in fact, very hard. With industries and production, the site the robots will have to work in is susceptible to all kinds of risks. These risks are not only limited to humans but also to the industry itself. Production environments generally contain various types of materials and substances that can create many unforeseen circumstances and problems. For example, rusts or corrosion of machine parts or robots, leaks, noise pollution, etc., are issues that the production will have to deal with almost regularly. Pair this with unforeseen problems in machines since they run all the time; industrial environments are very tough for robots to survive, which is why the 360-degree approach to robots is so important.

Not just the risks and problems for the robots, but the aftermaths of these problems and faults are more expensive to a production site. For instance, when a robot fails, or an installation of a new robot occurs, the actual production environment will probably suffer from its downtime. And industries do certainly not like downtimes. Downtimes lead to the stopping of whole production facilities and bar the production, resulting in the loss. Furthermore, this loss becomes more substantial if the materials or products that are not complete can go wrong. It will add the loss of materials and incomplete products to lower numbers of outgoing products from the factories.

Robotics in industries possesses more importance when it comes to error detection. Since production sites and factories can be dangerous and harmful for humans since they have to approach the machines to detect errors, it can be hazardous and even fatal in some cases. Hence, the emergence of drones and locomotive robots is rising in this department. However, industries are still taking the old approaches to use robotics and digital technology.

Industries generally shape robots around the production and use cases in the production sites rather than the inverse. Although typically, enterprises approach robotics as only a medium to replace human resources either in potentially dangerous places or tasks that may not be possible for humans to perform, the 360-degree approach to robotics in the future would only develop the technology further. Instead of this, industries and production facilities should shape themselves around robotics. Of course, it does not mean changing the particular industries’ end goal towards robotics and its implementation. Instead, it means to shape the industry so that it embraces robotics and involves it in the actual process and communication of the production sites.

Usually, robots in industries are linear, i.e., they are put in place of a human to speed up a process/task with a set of inputs fed to them by the developers or operators. They only do or set out to do specific functions inside the production line.

For instance, we can use a robot to put a product inside a box, put product stickers in packages, and seal the box. However, these robots only perform one task, i.e., a robot for placing products in a box cannot close it or put product stickers on it. Therefore, it limits the opportunities and possibilities that robotics can unlock. For instance, with the integration of technologies like AI, robots can become more dynamic and a part of the actual production process rather than the production line.

With AI and technologies like simulation, innovations like Offline Robot Programming Software Platforms are possible. With this, robots become more helpful; they can even participate in production processes to make them brighter and effective. Moreover, With the possibilities of self real-time optimization and self-diagnosis possible, robots will become able to report errors or possible errors in the future and solve those problems faster than humans ever can. And the time essential for robots to process what went wrong and determine if a possible solution is tiny.

In comparison, humans must first come across the errors, either after the error has already happened or detect it beforehand. Then such errors have to go through actual experts and need proper analysis. Only after this, a solution can come up which can fix the problem. But, unfortunately, the developers or the debug team may misinterpret the answer due to insufficient data or enough time. Even during this time, though, the situation can escalate, sometimes even forcing a downtime in the production. But the upcoming 360-degree approach to robotics would change it all.

With the integration of robotics from the start, alongside the significant goals of the particular industry, the actual use cases of robotics with more comprehensive and newer possibilities can emerge. It will let the industries access the actual use case they want from robots and the robotic technology more appropriately instead of focusing on what robots can do afterward, limiting the robotic possibilities. Only after integrating robotics with the actual goal or vision can an industry properly access what they need from robotics and other complementary technologies.

Every industry has a different need. Along with this need, various production systems and methods emerge. Hence, every industry or company may need something different from robotic technology. Even without using the latest or bleeding-edge technology, a company may fulfill its actual needs, i.e., every company need not use them. Hence, every industry needs to use and approach robotics differently to achieve their needs.

For instance, in a data-driven industry, the static robots that cannot communicate or process does not make sense. Since it's a data-driven industry, utilizing such technology in their robots will provide them with numerous benefits.

In an industry where robots and humans have to work together, human-robot collaboration makes much sense for the upcoming 360-degree approach to robotics. For instance, to perform a task like inspection of a faulty machine, robots can collect data from the air or the ground, while humans can analyze them and provide their insight. It becomes even more efficient with technologies like digital twins, AR, or VR.

3D models with digital twins can be much more efficient if industries integrate them with robotics. Automation becomes much closer while remote operations can thrive. With simulation technology, the training and testing of robots will become a digital endeavor rather than an inefficient, risky and expensive physical approach. Digital technology for robotics can enable rapid prototyping, higher form of product innovation, more advanced Research and Development (R&D), all the while remaining inexpensive, safe, efficient, and fast.

The 360-degree approach to robotics would also impact how we teach the robots as well. Technologies like offline robot programming (OLP) will enable robotics to evolve more rapidly. Offline robot programming replaces the traditional approach to teaching robots with Teach Pendants. Teaching pendants can be very slow, inefficient, and resource-consuming on top of being a significant cause of downtimes when it comes to teaching a robot. Pendants require robots to be out of production and in teaching mode the whole time during their programming. It increases downtime during the installation of robots and brings downtimes if the production house wants to upgrade the programming or coding.

But OLP replaces all that with a software model of teaching. The generation, testing, and verification of the teaching programs are possible through software simulations through OLP. OLP effectively eliminates the need to take out robots during its teaching process, allowing production to continue and robots to work even when training. OLP even opens a path for rapid maintenance, repair, and continuous upgrading of robots, all due to its teaching possible through software updates. Along with this, adopting simulation technology is another major win in terms of robot research and development. Simulations with AI can enable whole new ways of robot development, testing, and deployment. Pair this with technologies like Machine Learning, deep learning, and digital twins, AR and VR. Robots will then indeed be able to thrive. Companies like FS Studio that thrive in product innovation and advanced R&D technology can provide the industry with a much-needed push to propel themselves towards Industry 4.0. With over a decade’s collective knowledge and experience, FS Studio delivers a plethora of solutions for robotic technology and helps companies take a 360-degree approach to robotics.

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.