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.
Industries are rapidly advancing. With growing adaptation and accessibility of state-of-the-art technologies, various industries’ production innovation and R&D technology are becoming very advanced, albeit more complex. However, with technologies getting more complex, they are also getting easier to adapt. So laden with numerous possibilities and opportunities, industries are adopting digital technologies in their industrial application to reap these lucrative advantages as deep learning boosts robot picking flexibility.
The ultimate pursuit of automation in industries and production goes through the path of intelligent and smart robots. With more demanding industries, newer and better robots can perform various industrial applications more smoothly and efficiently. But as industries expand their reach into more fields/sectors, they need robots to achieve even more different tasks in different environments.
This broad spectrum of need for the usability of robots leads to robotic technology not being able to keep up with the demand. Hence, traditional methods and approaches to robotics must be let go to introduce new and better techniques to robotic technology. Within the advent of digital technology lies more possibilities for robotics that are even unseen before.
Digital technologies and platforms like Robotic Simulation Services, Offline Programming, Augmented Reality, Virtual Reality, and Artificial Intelligence take the world by storm. They are now in integration or development for almost every industry possible. The robotics industry also is not lagging in this aspect, with robotic manufacturers or various services providers already utilizing these technologies to propel robotics further. Deep learning is one of the technologies in use, with much anticipation and exciting possibilities, within the robotic industry.
Let's talk about Deep Learning
Deep learning is a type of Artificial Intelligence, or more so a kind of Machine Learning approach. In the broader AI paradigm, Machine Learning is a subset of AI that refers to an AI system that can learn with the help of data instead of developers having to code it. ML is an approach to AI that enables various algorithms to remember from data, i.e., training data consisting of input and output data, to infer a pattern or a “knowledge” in the input data about the output. With this knowledge, ML algorithms can effectively predict the outcomes with the analysis of input data.
Deep Learning is a similar approach. It's a family of algorithms in the machine learning paradigm based upon Artificial Neural Networks (ANNs). These ANNs in deep learning can perform representation learning. Representation learning is a method in which systems detect or infer a pattern or representation, i.e., features in the input data for feature detection or classification. Hence, computer science also defines it as feature learning since it detects features from raw data and uses them to perform some specific task.
Deep learning boosts robotic picking flexibility with its data by effectively imitating how intelligent creatures like humans gain knowledge and do certain things. In deep understanding, a system takes in input data and tries to infer a pattern or detect some specific feature in that data. This “learning” approach is known as deep learning. Furthermore, education can also be either supervised, unsupervised or semi-supervised.
These are various deep learning architectures that researchers combine up with various other computer techniques and technologies to enable different features and functions in robotics: deep neural networks, recurrent neural networks, convolutional neural networks. Deep reinforcement learning and deep belief networks are various architectures in deep learning—robotic technology pairs up these architectures with different hardware and technologies to build various robotic functions.
For instance, robotic researchers and developers use convolutional neural networks for computer vision with cameras and other sensors to give visual information like depth. Likewise, different architectures enable different computer application fields like speech recognition, natural language processing, image analysis, bioinformatics, etc. Moreover, these applications are often in use for various purposes within other industrial areas.
Why Deep Learning Boosts Robotic Picking Flexibility?
In robotics, one of the most complex things to perfect is its ability to pick things up. For human beings, picking items seems very easy. However, seemingly effortless things with biological creatures are not always similar to robotics and computer systems.
Thus, although it may seem that picking items up is easy, it is not the case. The complex interworking of different systems together to perform even a simple task is very hard for computers. For instance, to first pick things up, you need to know what you are picking.
This part is usually straightforward since, for example, you can tell a computer that the stuff it's gathering is in a specific location. But the hard part comes when it's doing the actual picking. For example, how is it even going to pick the object? Even in a single production environment, there are a variety of things with different shapes and sizes. In addition, objects have different textures, structures, and a specific suitable picking spot.
We can undoubtedly program a robot to utilize information about a particular object and a suitable method to pick the thing, but programming it to select it is challenging. Relatively, programming a robot to choose only a single type of object can be easy, but you would need other robots for different kinds of things/products. So this is certainly not an effective method to accomplish this.
Furthermore, products and objects may behave differently in different environments, creating complexities in ways deep learning boosts picking flexibilities. For instance, a product with a smooth surface can be slippery to grab or hold onto in a humid environment. Moreover, picking other objects in different backgrounds requires the robot developer to program the robot for various environments and various things. Along with this, considering the wide range of products, this problem quickly becomes substantially huge.
One of the enormous complexities we are not even exploring yet remains motor skills. Programming a robot to perform specific motor skills and functions is one of the vastest complexities of the robot development paradigm. Even to grant them specific motor functions is very hard. That's why it's a huge deal, even if a robot can perform simple tasks like holding a cup, walking, etc. However, now you can certainly deal with these problems through various means.
For instance, a robot that needs to move can have wheels. A robot that does not have to move but grab onto things can have arms on a fixed body. But these solutions are also tough to implement. Add this to the use case, such as a moving robot that has to move on an uneven surface or a wrong road or even locations where there are no roads, i.e., hills, rocky places, etc. Then this problem becomes substantially more challenging. Similarly, for industrial robots, picking different products and objects is also a complex problem due to different environments and types of things it has to deal with in a particular manner.
Apart from these problems, one primary concern is how deep learning boosts robotic picking flexibility, computer vision. A robot needs to see the object it's picking up. Recognizing a thing insight is a significant feat of computer vision that is currently possible with a massive range of solutions available. But simply recognizing an object is enough to interact with the thing. The robot has to know what object it's looking at and determine how it will pick it up. It again involves problems regarding the size, shape, texture, and structure of the object or product.
In hindsight of all these problems, an industrial robot capable of gripping and interacting with different types of objects or products with other characteristics and properties in different conditions or environments is tough to build. Consequently, it is one of the biggest problems in the industrial robotic plane. It is where deep learning comes into play.
We can use various deep learning techniques to teach a system to recognize and interact with an object. Using deep learning methods, we can use data from multiple production sites, companies, and industries of interaction and manipulation of various things and products for training the system. This data can effectively help a deep learning model to “learn” how to pick different objects in different environments in various particular ways.
The initial data can come from systems already proficient in picking and dealing with objects, which would help in how deep learning boosts robotic picking flexibility. For instance, there is data with humans picking up things. These specialized robots pick only a specific object or interact with them, or even human operators that operate machines to pick up different objects. After data collection of these types, a robot with a deep learning system can go through a training process to effectively learn how to replicate the task or perform it more efficiently.
With this, data collection is complete from a specific specialized robot and for different machines. Moreover, developers and researchers can share and augment such data for training there be used robots for broader use cases and even interact and manipulate objects they are yet to interact with. The possibilities are endless as deep learning boosts robot picking flexibility. As a result, developers can build with a wide range of picking flexibility that can help an industry drive itself towards the end goal of automation. It is why companies like FS Studio provide various services regarding robots and AI tools like deep learning. With decades of collective experience and knowledge with a wide range of expertise, FS Studio provides deep learning services for various robots and other innovative services like Robot Simulation Services, Offline Programming Solutions, and the integration of innovative technologies like AR and VR in different systems.
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.
Robot programming software is a software solution that helps program or code a robot for its use or operation. Offline Robot Programming Software is also the same.
With the advancement of technology, Industry 4.0 is inching swiftly closer towards us faster than ever. Industry 4.0, also known as the Fourth Industrial Revolution, is the age of digitization where every industry has digital technology at its core. Consequently, digital technology is continuously evolving. Today, it almost seems inevitable for industries to adopt digital technology instead of relying on the traditional approach to industry, manufacturing, and product innovation.
Robotic technology is also continuously evolving, with robots today more capable than ever in various fields and fronts, even unseen in the last decade. Moreover, with the complexity and sophistication of the robots increasing, they are constantly getting more and more complex to program, code, and even develop.
However, with increasing complexity in technology, it is also getting more and more adaptable, usable, accessible, and easy to use. It’s because newer bleeding-edge technological solutions help keep these complex problems and technology operable and functional with great ease of use and access. One of the similar problems regarding the increasing complexity is currently running alongside the robotic industry.
The Robotics industry is far more complex, risky, and resource-hungry than most technological undertakings out there. Due to the growing industry use cases for robot and their ability to fulfill these use cases. The nature of robotic technology is that numerous parts and systems converge together to form a single system unit that can perform various tasks and operations using these parts and systems. Due to this nature, alongside the already complex building blocks of robots, i.e., the components and different systems, integrating these building blocks to work in an efficient cohesion with each other is a huge undertaking.
For easing the difficulty of integrating different parts and systems, many state-of-the-art industries and companies are starting to use robotic computer simulations. Simulations are a great innovation of digital technology that can help develop robotics through research, design, development, and production. Moreover, even after production, robotic software can now help robot operations, maintenance, and programming with different robot programming software solutions.
What is Offline Robot Programming Software?
Offline Robot Programming software is an “offline” approach to programming or coding a robot. This “offline” approach takes the usual method of programming a robot, i.e., teaching pendants away while doing the “teaching” part through the software remotely. However, this remote programming of the robot takes away the need of taking the physical robot out of production; instead can program and code robots virtually through software.
Teach pendants the most common interface to program an industrial robot. The device helps control an industrial robot remotely and teaches them to move or act in a certain way. For example, these devices can program or code the robots to follow a specific path or perform a certain action in a particular manner. These devices also allow the operator to control and work with these robots without being physically present or in tether connection with the robot. It means robot programmers or operators get to control the robots and “teach” them remotely. Technicians usually use these devices for testing or programming, or coding of industrial robots. Hence, teaching pendants are a crucial part of industrial robotics.
Offline Robot Programming Software replaces this teaching pendant with a more elegant and efficient solution with the power of software and simulation technology. Due to the control over robots with software since robotic OLP or Robotic Offline Programming allows for uploading programs and codes through software updates. Furthermore, software developers and robot operators can generate these programs and codes through robotic simulation software in a PC rather than using the robot physically. OLP is, therefore, a more elegant, efficient, and more modern way to program industrial robots.
Why is Offline Robot Programming Software Important?
Even though these pendants are helpful and crucial to industrial robotic operations, they remain one of the bottlenecks of industrial robotics. Right off the bat, these devices are very slow and time-consuming. It's also very resource-consuming and requires personal at all times to operate. Furthermore, pendants also require the presence of the actual robot. They need the robot to be physically active in the teaching mode rather than doing other work during the teaching process, which is usually very long.
Hence, during teaching, the robot cannot be in production or be doing other functional tasks teaching process is very lengthy and tediously for the more complex robots with various joints or movement points and axes. The robot programmer has to program multiple joints and parts to code the robot manually, which is very time-consuming. The programmer will also have to take out the production robot during and until the teaching process. It will surely hamper the production line, and hence downtimes become longer.
In any industrial setup, the production line is the most vital part of it. So much so that the whole manufacturing or production plant usually is based around it. Holding such importance, the optimization of production lines is a very crucial task in any industry. Downtimes, irregularities, or faults in the production lines and components around it means it directly hampers the sector. Moreover, machines like robots, especially the ones with automation, are very crucial in production lines. Hence, production lines must not stop nor deter it due to the robots.
However, with robotic OLP, industries can remove and eliminate all these disadvantages and bottlenecks from production. Instead of teaching these industrial robots online, offline programming eliminates the downtime for programming these robots completely. With this power in their hands, production lines can now completely get rid of time for programming. Instead, industries can use all these times in the actual production and get better returns.
With OLP, automation comes one step closer in production setups. Offline Robot Programming software enables rapid prototyping to test programs and codes before uploading them to the robots through simulation software. Furthermore, simulations are now very technologically smart such that they can simulate all robot parts, mechanics, systems, and movements. With such capability in hand, robot programming and even robot development and the building will become very easy. Due to this, testing, training, and evaluating robots virtually become very easy through OLP. Furthermore, it allows for error detection and verification of programs and robot capability to perform tasks and operations even before they are physically present.
Apart from this, Offline Robot Programming software also increases the productivity of production lines and robot operators and developers. Furthermore, OLP also provides greater profitability and has a better Return On Investment (ROI). Moreover, with OLP, one can test and prove new and better project or concept ideas in their quotation phase without investing in physical resources.
OLP allows for not only training and testing but also helps in maintenance and repairs too. OLP can help to track down potential faults and errors even before they occur or after they occur. It further makes the production efficiency and without any downtimes possibly in future too.
Not only is OLP advantageous and beneficial for regular industrial robots, but it's also essential and can be a boon for some industries that involve high risk. For example, industries like aviation, nuclear, automotive are very high-risk industries. Testing robots in these industries is a sensitive matter. Hence OLP is a requirement in these industries to train and test robots efficiently. Furthermore, without, OLP it is likely not even possible and feasible for industries like the space industry able to undertake projects and accomplish them.
Offline Robot Programming software is generally seen as a technology with high complexity and requiring very skillful personals. But that is not the case. Various companies like FS Studio provide solutions when it comes to offline robot programming. Companies like these can help industries get started with OLP and thrive on enabling substantial new possibilities and opportunities. With decades of experience and expertise in fields like Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Simulation technology, FS Studio, can provide companies with proper and efficient OLP solutions to propel their industries with more efficient, safe and effective production lines. With the advent of Industry 4.0 upon us, companies and industries now must look for better alternatives and modern approaches to the industry. Digitization of industries is the future where digital technology will be at the core of all industries with efficient and smart solutions. OLP with simulation technology enables rapid prototyping, testing, development, and superior research and development (R&D) along with faster and efficient programming or coding of industrial robots. Furthermore, industries can collaborate with different OLP providers to determine the best solution for their particular industry and production and help them integrate their existing robots and production for a more smooth transition towards Offline Robot Programming Software.
Computer Simulation of Human Robots Collaboration in the industries is closer than we think. The current industry is moving towards the Fourth Industrial Revolution (FIR). FIR or Industry 4.0 is the digital transformation of the existing industries to enable new ways of manufacturing & production with automation at its core. The digital world will effectively meet the real world at this stage, integrating them on a level never seen before. Human Robots collaboration is one of the significant parts of this integration. With transformative technologies like computer simulations, AR, VR, and digital twins, cooperation among humans and robots is an absolute path that the next generation of technology will take.
Computer simulation is a very crucial tool for industries like robotic research and engineering. With the increasing adoption of computer simulation in various industries, simulations are rapidly becoming a vital part of product innovation and R&D technology. It is especially true for the robotic industry since collaboration between humans and robots is an essential part of the human robot paradigm.
Where Does Computer Simulation Come into Play?
Some factors influence the possibility for robots and humans to work together and collaborate efficiently. One of the top priorities or factors that affect this collaboration is human safety. During the operation, development, or testing of this concept of computer simulation of human robots collaboration, human safety is a top priority and should never be compromised. For this, various safeguards or failsafe mechanisms, power limiting restrictions, tools to monitor for possible errors, and proper fallback plans can be helpful.
Alongside this, robots that are in use must be aware of their surroundings and environment. At the very least, the use case of the robot must reflect its awareness and capabilities. Furthermore, robots also must control and change their actions as per real-time feedback and happenings in their surroundings. Thus, it presents the robot research and development industry with another challenge of autonomy and the ability of robots to perceive their surroundings or environments efficiently.
Conversely, bidirectional communication among robots and humans may open the door to fulfilling all the requirements necessary for a safe and effective human robot collaboration. But achieving such a feat is also not possible without proper testing and massive investments of time, resources, and money.
Computer Simulations can solve all these problems and complexities with efficient and elegant solutions. Computer simulation technology provides a modeling system to visualize any complex system, even 3D digital space. For example, a robot consists of joints, motors, arms, actuators, sensors, links, controllers, and other mechanical and electronic components like a battery, processing unit, and networking interfaces. All these components and elements can be costly when they reach the level of sophistication a robot requires. Alongside this, integrating these components into a complete robotic system in which these components work together efficiently as a whole system is also a very complex and expensive task to accomplish. Nevertheless, this is where computer simulations come into play.
The advancement in computer simulation technology now allows for the simulation of all these components and elements in a fully functional robot. Alongside this, computer simulation software can also simulate various environments and conditions under which a robot may operate. Much like a natural environment, a simulation environment allows for multiple experiments, tests, and evaluation of a robot, except it, is without all the costs and risks present when testing the robot in the real world. Computer simulations also enable monitoring and assessing robots with a very high level of sophistication in virtually any environment or condition possible.
Why is Computer Simulation of Human Robots Collaboration Important?
The human robot collaboration is essential for the factories of the future and all the possibilities that follow. In a space where robots and humans can work together efficiently to complete different tasks, endless opportunities emerge. For example, robots allow us to perform precarious and dangerous jobs that require massive strength or skill, along with repetitive or requiring extra precision. Meanwhile, some jobs require human intervention due to either being too expensive or complex to automate and jobs that require critical thinking and human intelligence. Thus, it constructively allows industries to utilize the best of both worlds efficiently.
For instance, risky jobs like mining, exploration of unknown borders and areas, repetitive assignments, lifting heavy loads, etc., have more practical industry use cases for robot in the field, but they also require human intervention. Similarly, jobs that require extra precision, like in surgery, may be more suited for robots. Still, due to a lack of intelligence and critical thinking, it is currently unable to do so. Likewise, human intervention is essential in search and rescue operations, but it also requires scanning large and potentially unsafe environments that are more suited for robots or drones. Alongside this, all factories and manufacturing industries cannot generally use robots due to either being too expensive to automate the job or too complex for robots to perform. Hence, human resources are used in various factories and manufacturing sites, albeit the factory and manufacturing sites are dangerous and unsafe.
These difficulties are easily removable if computer simulation of human robots collaboration becomes very efficient and easy to realize. Moreover, if such cooperation becomes possible to achieve, one can reap potential benefits from both worlds. For instance, robot developers in health care organizations can utilize the precision of a robot and the critical thinking of a surgeon to develop a surgical robot to perform complex surgeries on patients.
Consequently, a collaboration between humans and robots that enables an open environment where humans and robots can work together to complete works with integration of benefits from both worlds is a very lucrative goal to achieve. Computer simulation opens the door to such a goal. Due to the numerous advantages computer simulations possess, various industries develop human robot collaboration systems.
Generally, robot development in computer simulation software starts with designing and prototyping the robot. It requires a massive amount of resources, cost, time, and multidisciplinary skills in the real world. Then, each prototype comes to its testing, assessment, and redesign of the system according to the evaluations and results. It also requires equally if not more massive amounts of resources, cost, time, and skills in the real world. For a complete robot consisting of all its features and functionalities and compliance with all the factors discussed above, this process of prototyping, redesign, and testing has to be repeated numerous times until the evaluation and results are entirely within acceptable terms.
However, with the help of computer simulations, all these processes become redundant. When robot development with computer simulations occurs, developers/manufacturers get a digital platform to perform rapid prototyping with testing, modeling, redesigning, and programming all within the simulation. With the help of the computer simulation, developers can design a robot with all the parts and components right from the start to get a robot model. This model can go through various experiments, evaluations, and assessments to ensure formal requirements compliance. If not, developers can make changes or even redesign the robot entirely without much effort since it's in a digital form.
Not only this enables rapid prototyping and development, it ensures that developers do not exhaust all their time worrying about resources or costs but utilize that time for better ideas and models. It also opens the door for creative minds to flourish and experiment with various designs and configurations of robots. Furthermore, since the initial design process starts with a digital model, developers can tweak, organize and play with different formats. Finally, it will ensure that the design phase outputs the team's accurate designs with an efficient and agile developmental process.
Moreover, testing and evaluation of robots in different environments is also possible with error reporting and monitoring systems working together to gather essential data. It ensures that all unexpected problems or errors that the developers may encounter during the physical build of the robot are taken care of and solved. Testing with trajectory planning, verifying algorithm operation and efficiency, verifying the integrity of the design, and overall working of the robot can all be done in simulations. Testing various fluid mechanisms, aerodynamics, mechanical integrity, and kinetic forces with realistic physics engines is also possible.
One of the most vital computer simulation of human robots collaboration is human safety. Simulations enable testing for human safety and protection in numerous conditions and environments. We can quickly test and examine communications, control, and safety mechanics inside computer simulations without ever having to put a human at risk. With technologies like Augmented Reality (AR), Virtual Reality (VR), and intelligent AI systems, humans can test these robots with immersive experiences in realistic environments without taking risks.
It will rapidly evolve the development of human robot collaboration with the power of rapid prototyping, innovative product development systems, and efficient R&D technology. Furthermore, with Industry 4.0 gradually moving from embedded systems towards the digital transformation of the industries, simulations can open the door to new ways of development and enhance the much sought-perfect cyber-physical system (CPS).
With the advent of computer simulations, robot development and research is moving away from machines with no or low-level intelligence towards a more autonomous, adaptable, flexible, and re-configurable system that can work efficiently with humans. With computer simulations, human collaboration with intelligent robots will be possible across various industries where the whole collaborative system will be efficient, sustainable, effective, and safe. And our approach of creating the computer simulation of human robots collaboration will be completed.
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.