Companies are swiftly adopting digital technology, with the whole industry moving towards the Fourth Industrial Revolution (FIR) or Industry 4.0. The ultimate end goal of almost all industries is to be self-sustainable, with automation at its core eventually like the Skyscraper Window Washing Robots. Subsequently, the industry has to adapt and integrate robotic technology in its operational process to reach this goal.
Robotics technology is rapidly evolving in both accessibility and usability. With this evolution, the technology is getting better and more usable, but the robotics market is also more valuable. Consequently, the robotics industry is currently one of the biggest markets of the technology paradigm.
In hindsight, the market also reflects this favorable shift of the industry towards robotics technology. As a result, researchers forecast that the global robotic industry market was more than 27 Billion US Dollars in 2020, with the other estimation that it will cross 74 Billion US Dollars by 2026. This increase in the market value represents an annual increase of more than 17%.
Furthermore, these figures will only increase in rate and estimation in the future post-COVID-19 pandemic era where work-from-home and remote technology is experiencing an enormous boost in development, accessibility, and adoption.
Robotics especially shines in industries where the work is either reparative or dangerous. Industries see this in retrospect in which various industries and production use robotic technology to achieve automation in repetitive and potentially dangerous tasks. For example, in the cleaning industry, skyscraper window cleaning is a hazardous task. With skyscrapers towering very high, the workers usually have to climb onto a platform that is hanging potentially even a hundred floors above with the support of a couple of wires.
Cleaning windows properly is just one of the problems when you are dangling hundreds of floors above the ground. With such high risk, the window cleaning industry rides on workers with steel nerves and a good cleaning capability. Although this business is lucrative, the lack of such workers and even companies that perform such risky jobs. The industry is also declining due to the same reason. With over 40 Billion US Dollars worth of market revenue every year, the window cleaning industry faces a lack of young talents to replace the old and trusty workers. The fact that above 74% of workers with training for such jobs are over 40 years old reflects this problem. Consequently, although a lucrative business, it's a dangerous job facing a severe lack of replacement workers.
One of the primary solutions to this problem is to remove workers from the task altogether. Therefore, replacing the window cleaning workers with robots is one of the possible solutions to eliminate human risk and increase efficiency and potential. Hence, window cleaning robots are growing in popularity in this business.
But to know how this all works, we first have to know about robots.
What is a Robot?
A robot is a programmable machine that can automatically perform specific tasks or take particular actions without requiring human assistance.
People usually imagine robots as machines with humanoid shapes with high intelligence, at least that is the depiction of robots in media and science fiction. But unlike robots in popular media and science fiction cultures, robots come in different forms, sizes, and uses. Furthermore, a robot is any machine with some level of processing power and can perform specific tasks without needing human intervention.
Read more: How DeepMind Is Reinventing Robotics!
For instance, the disk-shaped machine/device that cleans the floor automatically while moving on its own and avoiding obstacles is a robot. Similarly, various toy robots and robotic kits are already available in the consumer market. Drones are also a type of robot that can fly autonomously, balance themselves and follow directions from human operators. Apart from the consumer market, robots are also widely in use in industrial settings. The most widespread use of robotic technology and robots is seen in industries and production sites.
A robot is not a singular device or a machine; instead, it combines various components, systems, and incoherence to perform multiple tasks. These components include sensors, processors, storage systems, power supplies, mechanical parts like wheels, arms, chains, cameras, actuators, rotors, motors, etc. These components, devices, and systems work together efficiently to behave like a singular unit and perform various tasks with collaboration and communication.
With the advancement of technology, various systems, including sensors, processing power, battery power, storage systems, motors, actuator systems, and digital systems, are getting more modern and efficient. With the constant evolution of these components, they are increasingly getting complex. However, increasing complexity also increases the ease of use, efficiency, and capability of these components. The whole robotic engineering paradigm reflects this increase with robots getting smarter, more capable, and more efficient in performing various tasks and jobs with increasing levels of autonomy.
The Case of Skyscraper Window Washing Robots
Skyscrapers are, as their name reflects, very tall and usually stand over 150 meters high. On the other hand, mega-skyscrapers are well over 600 meters in height with more than hundreds of floors. These skyscrapers also require a vast amount of maintenance, including cleaning their windows. But these skyscrapers are so tall that regular cleaners cannot clean them. So they need professional window cleaners.
Professional window cleaners usually stand atop a platform hanging beside skyscrapers and are controllable by a crane. This crane can take them downwards or upwards and sideways across the building. Although the media onto which the workers stand to have railings for safety, it still hangs above hundreds of floors above where one small mistake or mishap can end horribly. Besides these factors, the weather is also a significant factor that can increase the risk of window washing, especially if it is a windy season. So it is a pretty risky job from every corner possible.
Moreover, besides the danger of hanging beside such tall buildings, window cleaning is also a challenging job, with few workers even willing to climb onto the platform that's dangling hundreds of meters above the ground. This same case is why this business is so lucrative in the first place. Unfortunately, this is also becoming why new recruitments are getting complicated. With over 74% of the trained professional window washers being over 40 decades old, the replacement rate with young blood is thin.
Even if one does not fear heights, the job has a significant risk of losing their life, making it unattractive to many. The risk factor is very unfavorable with humans on the scale.
How Robots Make Skyscraper Window Washing More Safe?
Right off the bat, when robots are the ones cleaning the skyscraper windows, we can eliminate the risks of having humans on platforms besides the skyscrapers. When robots are replacing almost every human labor, it is essential to look into this factor where the risk of losing life is more than human labor. It will significantly reduce the risks along with having massive leverage if something does go wrong. Thus, we can remove the heavyweight of having potential dangers for humans.
With robots, maintenance along with cleaning is straightforward to perform. With the advancement in remote technology or even autonomous technology, window cleaning robots can leverage this by being controllable by humans or even independent at their tasks. With robots, workers can permanently bolt or fix the robots onto the lift mechanisms, significantly reducing the time consumption that would otherwise be used in checking harnesses or straps for human workers. It will reduce the turnaround times between jobs and save time significantly.
Not only will window cleaning become safer, but it will also become more efficient and fast while consuming fewer resources. Another significant advantage of using window cleaning robots is the economic benefit. These robots can work at almost any condition without stopping and even multiple robots for faster turnaround times between different jobs. As a result, it will undoubtedly bring more returns from the investment.
It will increase the work capacity of window cleaning companies and make the whole gig more economical for consumers. It will also be fascinating for the skyscraper owner to see the robot cleaning the window rather than being guilty of risking human life. Meaning it will attract more consumers and even less time between the cleaning cycles. It will increase the market value and revenue of the whole industry altogether.
FS Studio, therefore, provides robotic services like Offline Robotic Programming or even robot training and software development that can cater to the window cleaning business. FS Studio’s collective experience and knowledge from decades of research and development with solutions like Robotic Simulation Services alongside emerging technologies like AR and VR.
With expertise in Artificial Intelligence and technologies like Machine Learning (ML) and Big Data, FS Studio provides intuitive solutions for product development and innovative R&D technologies. FS Studio offers cutting-edge solutions for present problems and issues. It also empowers its clients with solid solutions that will also help them solve and tackle future challenges.
Skyscraper window washing robots are a massive step for window washing companies. They are safer, efficient, and cost-effective on top of enabling new opportunities and possibilities not only in end jobs but also on business fronts. With the Industry 4.0 approach, industries are transforming themselves towards digital technology to strive for automation. This goal relies heavily on robotic technology that enables intuitive solutions like skyscraper window washing robots.
Teaching robots is a time-consuming and laborious task, especially when you’re manually teaching robots. Particularly with robots of niche applications, use cases, and robots with complex movements or robots within specific environments like industries and production. Robotic technology is continuously evolving, and so is its complexity. However, robotic tech is also becoming easier to use, more accessible, and more adaptable with increasing complexity. Conversely, teaching robots through traditional approaches like Teach Pendants is getting more and more challenging and complex.
The Robotics industry is complex because of the sheer complexity of the technology and the cost of developing, building, and deploying a robot. Robot research and development and deploying robots are challenging tasks because of the sensitive nature of testing in robotics. Testing a robot is an expensive task. Consuming massive resources and time, testing robots along with training them is a very resource-intensive task.
However, due to the advancement of technology and the Fourth Industrial Revolution (FIR) inching closer and closer, industries are rushing towards digital technology and automation, which, in some scenarios like industries and production only possible with robots. Consequently, the importance of robotics in the production industry is increasing day by day. As a result, manufacturers and production sites are getting more eager to adopt their production line with robots with digital technology at its core. And manually teaching robots would only slow the production down and eventually leave you behind in the competition.
The Complexity in Robotics
With robotics comes its complexity. A robot is not a single entity but an integration of several different parts, components, and systems working together. These parts, components, and systems are usually various mechanical parts, motors, actuators, hydraulics, sensors, processing systems, networking interfaces, and many more. These components are very hard to build and even complex to perfect. Furthermore, integrating these parts to work together simultaneously with efficient cohesion to achieve a system that can perform specific tasks is complex on another level.
The integration may well be complete and the robot ready. But another major hurdle comes in the form of programming/coding the robot. Programming a simple robot with a particular function may be easy, but the robots that have to perform complex tasks while performing complex movements with precision are strenuous. This difficulty only scales up for industrial robots that have to accomplish tasks with accuracy and repeatability and perform various activities and functions within the production environment.
Why Manually Teaching Robots Will Hold You Back?
Programming a complex robot also requires a complex teaching process. The traditional approach to programming and coding robots is to use teaching pendants. Teaching pendants are a device that helps robot operators to control and program an industrial robot remotely. For example, these devices can code or teach a robot to follow a specific path or perform certain actions in a particular manner. With teaching pendants, robot operators or developers have to teach these robots manually.
Manual robot teaching may be easier on robots with low movement paths, simple actions, or singular axes. But industrial robots are a whole another story. They need to be constantly working in a usually adaptive and harsh environment of production. Such robots are complex and also very sensitive. Hence training the robots with teaching pendants is a difficult task. It is a very time-consuming task with the requirement of the teaching personnel to be present at all times. Furthermore, the robots have to be in teaching mode during all this time which means they cannot perform other tasks. Add this to the fact operators have to take them out of production during this long teaching process. All this makes manual teaching very cumbersome.
The downtime while teaching the robots is a massive issue to production. Moreover, this downtime is not only a one-time thing. Since industries have to be at the top of their game to thrive, they need to evolve and adapt over time. New changes and upgrades are necessary. Maintenance and repair works are inevitable. And even the failure of robots is not a common thing. All this requires teaching pendants, which is again very slow and a tedious approach to programming robots. It will add more delays, difficulties, costs and consume more resources. And this is a massive bottleneck for production.
Instead of wasting time in this slow and cumbersome manual approach, using new and better solutions with automation at its core is the way to go.
Learn About Offline Robot Programming
Offline Robot Programming is an “offline” approach to robot programming. Offline Programming (OLP) is a software solution to manually robot teaching by replacing the teaching pendants with simulation software. This “offline” solution teaches the robots virtually through software remotely. Thus, OLP takes leading away from the manual approach and takes out the requirement to remove the robots from production.
Although Offline Robot Programming is not a new technology, its evolution in recent years puts it in the spotlight in robot programming and the whole paradigm of robotics. It’s because of the advantages and benefits of using offline robot programming. Offline robot programming replaces the teach pendants with a more elegant solution. Furthermore, OLP allows for industries to train robots and their programming/coding through software updates. Robotic Programming Platforms also offer different software solutions to generate these instructions.
It means there is no need for the actual physical robot to be present in any generation phase or testing the training program/code. Instead, all this happens within the simulation technology inside the robotic programming platform itself. The evolution of simulation technology is so far ahead that it can now accurately simulate almost any object or environment with all the characteristics and behaviors of the original real-world object or environment.
Simulation technologies today can simulate every robot’s functionalities, features, and operations. Various behavior, states, and phenomena of robots and their components can simulate without manually teaching robots. Simulations can accurately simulate the mechanical elements of different parts with different materials and their operation in different environments and conditions. Along with this, fluid dynamics for air and water is also possible to simulate. Collisions, movements, etc., are also potential. It is due to the ability of simulations to accurately simulate and imitate the real-life physics of materials and the environment.
In addition to this, simulations can also imitate electronic components and processes. For example, it can accurately simulate the processing of CPUs and progressing units or even network interfaces and data exchange. Along with this, simulations can even test technologies like Artificial Intelligence (AI) with Machine Learning (ML) and deep learning. All these possibilities allow simulations to simulate all behavior, state, and properties of a robot along with its features and functionalities effectively.
Robotic simulation software solutions are already available, and different industries and companies are already leveraging their benefits. These simulations make innovative technologies like OLP possible to exist and thrive, creating manually teaching robots irrelevant. With offline robot programming, companies need not go back to the old approach of using teaching pendants. Such an old approach is very time-consuming while also requiring enormous resources, workforce, and investment. In contrast, OLP provides companies with elegant future-proof solutions that are effective and efficient.
OLP successfully reduces downtimes from production due to its ability to upload programming instructions in robots that they are working on without taking them out of the output. They can also enable new roads to generation and testing robot programs far from the manual testing method and age of robot codes or instructions. Simulations make it very easy to try these codes, while AI automation enables self-diagnosis and real-time optimization of production lines.
OLP is often seen as a technology that is very complex and requires high skills to utilize. There is a huge misconception that only the sides with deep pockets can afford to use OLP solutions, and there won’t be any demand for manually teaching robots anymore. But that is not the case. OLP solutions are pleasing on paper and easy to integrate and adapt even in existing production. Companies like FS Studio are working hard to bring out innovative solutions and state-of-the-art R&D technologies, including robotic OLPs, to make this transition of using OLP solutions smoother. With decades of experience and collective knowledge of various skillful people, FS Studio brings out solutions like Robotic Simulation Services for multiple companies and industries.
With the increasing pace of the industry’s move towards Industry 4.0, every industry is eagerly shifting towards digital technology while replacing old technologies like Teach Pendants with newer, more elegant, and efficient solutions like Offline Robot Programming platforms. Offline robot programming opens the road to newer possibilities and opportunities, enabling rapid prototyping, testing, training, and superior research and development, saving you from manually teaching your robots. In addition, it will help companies bring out efficient production and help them maximize their efficiency with a proven feat of achieving higher Return of Investment (ROI) in production lines and product innovation. Furthermore, this will help industries and companies innovate and remain at the top of their game to surpass and outperform their competitors.
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.
Building intelligent infrastructure with digital twins has helped several companies to collect, extract, and analyze data. Digital twin technology or virtual twin is overgrowing with increasing accessibility and adaptability. As Industry 4.0 comes closer, technologies surrounding digital twins are also maturing and continue to develop. With the incorporation of technologies like the Internet of Things (IoT), data analysis, and Artificial Intelligence (AI), digital twins enhance R&D innovation with intelligent services like automation, self-monitoring, and real-time optimization. It enables rapid design & development and smart solutions in production, sales, logistics, and overall supply chain.
With the ability to enhance current manufacturing & product development, industries worldwide are incorporating digital twin technology. We can already see this accelerating adoption of digital twins across the industry. Although the global twin market was currently at 5.4 billion US Dollars in 2020, much of its slump is due to the worldwide pandemic. In addition, several industries shut down due to lockdowns and social distancing being the new norm during 2020 because of COVID-19. Nevertheless, the digital twin market is slowly rising again, with a tremendous rise expected after 2021. As a result, the global digital twin market will likely reach 63 billion US Dollars by 2027 due to a high growth rate of 42.7% annually.
What is Digital Twin?
While the idea of building intelligent infrastructure with digital twins is not entirely a new concept, due to its current exponential rise and growth, digital twins are undoubtedly growing more and more prominent. Along with the advancement in IT and digital technology infrastructure, digital twins are also evolving rapidly. In general, the concept of digital twinning represents a physical object or environment in a digital form that possesses its accurate characteristics and behavior. While 3D models and simulations also can describe an object or environment, twins systems do more than that.
A digital twin generally represents a physical object or environment not just in a static manner but in a dynamic form. A digital twin represents every phase of the lifecycle of a physical object or environment. A digital twin represents a physical object or environment from its design phase to manufacturing and maintenance and changes due to re-resign, iteration, and refining the object.
Hence, a digital twin is less of a 3D model rather more like an information model. Unlike traditional 3D models, building intelligent infrastructure with digital twins needs a more dynamic and adaptive approach. They can evolve and change over time concerning changes and enhancement in information and data. Digital twins can communicate, update and even learn similarly to their physical counterparts through data exchange with Artificial Intelligence at its core.
Artificial Intelligence with technologies like Machine Learning and Deep Learning enables a digital twin to behave as accurately as possible in contrast with its physical counterpart. Due to this dynamic nature of digital twins, they are in use to explore solutions, detect and prevent problems even before they happen and essentially plan for the future. Armed with these intelligent and smart solutions, companies and organizations worldwide rapidly adopt these technologies in their operations and global supply chain.
Building Intelligent Infrastructure with Digital Twins
Digital twins have five levels of sophistication. Ranging from a level 1 twin that can describe and visualize the product to a level 5 twin model that can operate autonomously, different levels of digital twin require different levels of infrastructure. For instance, a level 1 twin does not require advanced Artificial Intelligence or Machine Learning systems, but a level 5 twin does need them. Level 2 digital twin is an informative twin that needs to incorporate additional operational and sensory data. Furthermore, level 3 is a predictive twin that can use these different data to infer and make predictions. On the other hand, the Level 4 digital twin is a comprehensive twin that can consider and simulate future scenarios to predict and learn from them.
Building digital twin technology includes converging technologies like IoT, data analysis, design & development of the twin either in 2D or 3D, and incorporating AI and technologies like machine learning and deep learning. The digital twin infrastructure is not only in a digital form but also in physical form. This is because a digital twin simulation model resides in a digital format and connects the physical world alongside it. This connection is the representation of both digital models and physical models such that they represent and replicate each other. Every change in the digital or physical model must be synchronized, and both should also respond to each other’s differences.
The actual connection is made through digital models. We can link the physical world with the virtual world by twins modeling and simulating the physical world to map and represent it in digital form. On the other hand, we can connect the virtual world with the physical one by replicating any changes and updates made in the virtual world in the physical world itself. It will ensure that neither the digital form nor the physical form is not synchronized.
In digital twin technology, synchronization must be in real-time when building intelligent infrastructure with digital twins. Real-time synchronization and simulation of the product is the following infrastructure for digital twins. Whenever a product is in the developmental phase of production, the status of the digital twin must also reflect that. The changes occurring in the digital twin must also be replicated in the physical product. Therefore, the changes in materials, processes, environmental, and every other change must be synchronized across physical and digital forms.
Apart from this, the digital twin infrastructure also requires data analysis for deep learning and intelligent systems. Artificial Intelligence generally powers these intelligent systems along with Machine Learning and Deep Learning capabilities. This is necessary for smart analytics and prediction. ML and deep learning systems must be capable of analyzing substantial amounts of data. This data must be representing the actual physical product in real-world environments. Such data are generated and collected by sensors placed in the physical world and physical development.
The data collection is a crucial metric for a system to detect anomalies or errors through analysis in the digital twins platform. Usually, ML systems process these data types and perform pattern recognition to make predictions or suggestions. Thus, these systems enable self-monitoring, predictive maintenance and diagnosis, alert systems for possible future errors, and detection of abnormalities or inconsistencies in the product.
Due to this, the data must be accurate and representative of the actual physical product and environment with great precision. These types of data also are helpful for the corporations or organizations for their product analysis and study.
These infrastructures together enable all the digital twin advantages. The convergence of these technologies is a complex task. Nevertheless, the resultant solution offers an intelligent system that can track past system analytics to predict future solutions and real-time product optimization. Companies are rapidly advancing towards implementing digital twin technologies in their platforms and systems to leverage such benefits.
Building the Infrastructures
Building digital twin infrastructures is a very complicated and complex process. Since digital twins incorporate various technologies together, it is tough to integrate these technologies to work together flawlessly. Only with such integration can one enable proper digital twin technology and can leverage its benefits.
Since the technologies part of the model twins infrastructures are different, companies must be willing to take on R&D for every technology when building intelligent infrastructure with digital twins. Moreover, if not for flawless integration, the technologies must at least be working together, which is a challenging task. However, technology is rapidly growing, and so is its accessibility and ease of use. Hence, integrating these technologies is increasingly easier to enable the tech stack for digital twins.
With the power of the cloud, technology today is dependent mainly upon real-time computing. With the help of the cloud, companies can leverage virtually endless amounts of computing to enable various services, including digital twins. Furthermore, cloud computing allows companies to build intelligent systems that are ideal for integrating multiple infrastructures of the digital twin technology.
One of the most prevalent uses of cloud computing is Artificial Intelligence. Due to the nature of Machine Learning and deep learning, immense computing power is necessary to develop these systems. Cloud computing shines brightly in this field due to its vast pre-built infrastructure and network of computer systems. In cloud computing, these computer systems are connected through an extensive network of servers and processing systems. Cloud computing service providers serve this network of different systems as a single system with enormous computing power.
Alongside this, a system for efficient and accurate modeling of the physical world with high-performance systems for real-time optimization and synchronization is mainly necessary. Moreover, deep learning and data analytics with intelligent AI systems to enable smart solutions with automation at its core is also imperative. Furthermore, a unified system integrating all these technologies is crucial while building an infrastructure for digital twins.
Companies like FS Studio pioneer product innovation and transformative R&D technology through already established and proven digital infrastructure. Since deploying and building intelligent infrastructure with digital twins is very complex and challenging for companies and organizations, FS Studio provides innovative and smart solutions for these problems. Consequently, companies can focus on their primary product innovation rather than shifting their resources towards building a digital infrastructure.