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.
Simulation in the digital twin can help the aerospace, manufacturing, and robotics industries in many ways.
How many times have you bought a new product only to find out it's defective? It is a huge problem that has affected many people in the manufacturing industry, and with simulation, we can help! A digital twin can be used for testing products before they are released. Let me show you how this works.
Imagine we have an airplane manufactured by company ABC Corp, and they want to make sure that their new plane design will work well without any defects. To know if their plane is safe enough, they need a simulator that displays the different flight conditions to test for safety. If the makers detect any problems during this simulation, they can fix them before production begins, so no one gets hurt.
This blog post explores the importance of simulation in digital twin technology for aerospace, manufacturing, and robotics. We will discuss why simulation is essential to these industries and how we can use it to improve efficiency.
Let's get started!
Simulation in digital twin for Aerospace:
Aerospace companies have become more competent. They are using digital twins to eliminate unplanned downtime for engines and other systems. Today, airlines can keep their aircraft in service longer due to digital twins' warnings.
A digital twin is the computer model of how an asset behaves in the aviation world. It accounts for variables like weather and performance to predict outcomes. The virtual model also provides actionable advice on what to do if things go wrong based on simulated scenarios. This strategy has been so effective at airlines that aircraft are flying more hours than ever before!
Digital twins are capable of recommending mission adjustments that will decrease wear on equipment, thus increasing longevity and success rate for a given operation.
Data analytics are a vital component of digital twins and can predict when an asset will fail. The sensors receive the data in real-time on specific failure points.
The models make predictions and help determine how long the running equipment has left before needing replacement or repair. It saves companies both money and valuable resources like human labor that would otherwise go towards maintenance efforts if they were done manually instead of digitally predicted.
Creating a digital twin is challenging without the necessary data. However, data about calibration details, the geometry of components, and mechanical assemblies could be enough for creating an effective model that will help improve quality assurance testing.
According to Aviation Today, "Boeing has been able to achieve up to a 40% improvement in quality of parts and systems it uses to manufacture its planes with the "digital twin." Essentially this means that before any aircraft component enters production, they are analyzed digitally using high-powered computers.
Imagine if you could test out how your new car will perform in any weather. Well, with digital twin replication that's possible! This virtual 3D model can go through a range of simulated environments like being underwater or enduring freezing temperatures - all before it ever leaves the assembly line.
Alongside these simulations are data fusion techniques that help gather information on an asset by combining different datasets such as images from sensors embedded into machines. Data fusion evolves alongside technological advances, keeping up-to-date with the piled-up data in volume, velocity, and variety. It can be crucial for businesses who want their products ready for anything life throws at them!
Data is the driving force in our industry. We produce an unimaginable amount of data every day, and it has to be processed by machines so that we can make sense out of it.
The flow from raw data to high-level understanding requires a complex fusion process at different levels: sensor-to-sensor, sensor-to model, and model--model fusion.
Designing a digital twin for one or more critical systems like airframe, propulsion & energy storage, life support, avionics, and thermal protection is recommended for success.
Digital Twin Simulation for Robotics:
For example, let's say you're building a machine that picks up parts from its bin. You want it to know where the function is and how big it is so your robot can grab them correctly without any mistakes or hiccups in production.
We need an algorithm trained by images of the items on top of our bins - which would then tell us what size each item was. We will also need a video feed captured by cameras positioned overtop these bins, giving us that cameras above images
A great example is bin-picking; people must manually place parts in many different configurations for a machine-learning algorithm to learn how it should pick up a part automatically.
This method is an example of supervised learning. When training a supervised learning algorithm, the training data will consist of inputted images paired with their correct outputs like bounding rectangles and labels describing what objects are in each image (e.g., "box," "can," etc.).
There's a lot to consider when you're teaching robots how to complete tasks. In addition to training them on what the job looks like, it also takes repetition before being trusted with delicate and potentially dangerous materials.
The robot must have had multiple rounds of practice for every task for its skill sets not only get better but continue improving overtime without any hiccups or errors that could lead to injury accidents down the line
A robust automation solution can take weeks and even months to converge, depending on the task. For example, a complex system will require more time than one which has few components. Additionally, some of your parts might be unavailable or still in production already - this could limit you from accessing them for training purposes.
"Digital Twin" is making significant leaps forward in industrial robotics, assisting manufacturers by not only setting up systems but also validating them for robust reliability using machine learning and integrated vision techniques. As a result, it can shorten the time taken significantly from months or years down to days.
In a virtual environment, the avatar replaces the real robot. So instead of spending all day in front of video screens and keyboards, it's now easy to do everything from your couch: launch a simulation on your computer and let the machine work for you!
In addition, the costs go down by about 90% because there are no lab fees or equipment setup charges.
Next, you bring your robotics into the physical world from the virtual.
The machine learning algorithm helps to learn what everyday objects and scenes look like when viewed by this device so that its actions are more in line with our expectations for how we would behave if given these inputs.
You can teach an old robot new tricks using AI-based facial recognition software!
Digital Twin: The Future of Manufacturing:
Digital twins are the future of manufacturing. With a digital twin, you can test and simulate before any mistakes happen with physical prototypes—saving time and money from costly errors that could have occurred through experimentation on materials or manufacturing processes.
In addition, manufacturers will never again risk releasing a defective product to market because they know what works beforehand thanks to their virtual representation by way of a "digital twin."
It is getting to market faster than their competitors is a challenge for companies. However, it can be possible with a digital twin as it cuts long steps shorter and reduces changes in production.
The product life cycle happens in the virtual environment where we can make all improvements much easier and quicker- perfecting efficiency and development time.
Imagine you have created this beautiful virtual prototype that has all the potential features. But, instead of wasting time test
One of the best features of digital twin technology is that it can help you predict problems before they happen. So, for example, every time one broke down, its virtual copy would start to analyze data from sensors to pinpoint any potential troubles.
It can solve many potential issues without any intervention from an operator by using predictive maintenance software that collects various sources of data through sensor readings to identify likely future complications with machinery. As a result, if you replace worn-out parts sooner rather than later, your manufacturing process will run more smoothly!
Simulation in the digital twin is reducing costs for industries.
For example, ASME reported, a 2020 study says that up to 89% of all IoT platforms will have a digital twin in 2025, while nearly 36% of executives across industries understand the benefits, with half planning for implementation within just five years from now.
If you're not already familiar with the concept of digital twins, then it's time to get up-to-date. A digital twin is a virtual representation that mirrors an existing physical system in real-time.
In other words, if your company has a manufacturing plant and wants to find ways to be more productive by reducing costs or improving product quality, implementing a digital twin may help!