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!
IoT and Digital Twins can reduce the costs in the manufacturing industry, minimizing unexpected downtime. These emerging technologies also help to perform complex simulations, offer deep insights and suggest equipment improvement. IoT technology also safeguards the interest of the manufacturer adding speed and flexibility in every situation.
What is a Digital Twin in simple words? Or, for that matter, IoT?
Digital Twin is the virtual twin or copy of the actual product. The digital twin connects the physical and digital worlds.
On the other hand, IoT, or the Internet of Things, is a physical "things" network. IoT platform is the medium for connecting and exchanging data between material objects. Generally speaking, we embed the sensors and software into physical objects over the internet.
As we live during the industrial revolution 4.0 (Industrie 4.0), manufacturers and industries embrace emerging tech to automate traditional systems. They are using emerging technology like AI, Robotics simulation, Biometrics to speed up the industry systems.
Emerging tech like IoT and Digital Twins are also reducing costs in astonishing ways.
IoT & Digital Twins Case Studies:
The evolution of IoT has made data transfer hassle-free by connecting sensors to the cloud and other "things." Apart from this, IoT is also serving as an effective tool for predictive maintenance.
Conversely, Digital twins leverage IoT to aid organizations in monitoring assets or processes virtually. Unfortunately, these are assets that are hard to check due to their distant location or a hazardous environment.
IoT and Digital Twins have unimaginable use in reality. For instance, power grids breakdowns create hindrance in every life, causing delays in businesses and services.
We can now tackle unwanted interruptions in power distribution thanks to IoT predictive maintenance.
Finland's electrical substation is the exemplary model of the predictive maintenance case study. In 2018, the electrical sub-station used Haltian's Thingsee wireless sensors for the first time. But, unfortunately, these sensors require manual checks from the human side.
The electrical sub-station used the sensors to collect temperature components, including humidity, air pressure, and distance.
This IoT-based predictive maintenance helped to increase efficiency in the electrical sub-stations while eliminating equipment failures. In addition, predictive maintenance helped to detect flopping assets and understand the factors leading to abnormal operations and disrupting schedule maintenance activities.
Finland's electrical sub-station isn't the only example of successful IoT in industrial applications.
Ericsson Panda manufacturing plant in China is another IoT case study we need to discuss.
The Ericsson Panda plant in Nanjing used Cellular IoT and connected 1000 devices to form a gigantic branch. In addition, the system had embedded IoT modules to send and receive data in real-time.
The IoT modules are said to transmit about 100 bytes of data per 8 hours from recent usage. Later, Ericsson Panda used the data in a cloud solution for analysis. The IoT solution costs just $20 per unit, will cut 50% maintenance work, saving USD 10,000 annually, and achieving breakeven for Ericsson Panda in 2 years.
The Ericsson Panda manufacturing plant is the first cellular IoT –based smart factory, and its immense success has contributed to the expansion of IoT worldwide.
Today, IoT technology has become a key element in the global supply chain already.
Since the beginnings of the industrial revolution, companies were eager to measure the temperature of the transported goods using the low-cost solution. IoT-based predictive maintenance and analysis applications in refrigeration systems help to understand when the system may fail. Therefore, it helped to avoid wastage of valuable agricultural goods and medicines and save money and time.
Similarly, companies have managed to keep the maintenance costs of factory equipment under control by 40%. IoT -based predictive maintenance has also helped to reduce equipment downtime by 50%. It reduced equipment capital investment by 3% to 5%. It saved the overall capital investment by 3% to 5% by extending the life of machinery.
Digital Twins can save money by predicting future failures. So, companies can repair defects at their earliest at a much lesser cost. It also recommends best strategies to improve the product development cycle, maximizing profitability. In this way, companies using Digital Twin can maintain a good relationship with their consumers.
As we can see, emerging technologies can help industries in the most remarkable ways. However, the expansion of Digital Twin and IoT isn't just limited to electrical sub-stations, supply chains, or manufacturing plants.
IoT and Digital Twin have expanded to other utility industries like healthcare, rail transportation, and oil and gas.
Oil and gas industries are adopting Digital Twins faster to minimize the costs of assets and productions. These industries have costly investments and handle them very carefully. Thus, it's no surprise that they aggressively adopt digital twins for modeling operations such as oil rigs, pipelines, and processing facilities.
Oil and gas companies have digitalized their systems to cut off weeks of unplanned downtime while reducing production costs. In addition, these industries have adopted predictive maintenance and IoT analytics to review historical data to detect failures in major components located at their offshore oil platform.
Digital Twins have transformed the transport industry as well. Today, the transport industry applies high-value rolling stock, such as locomotives, to maximize fuel efficiency and optimize maintenance.
The transport industry is willing to achieve the highest fuel efficiency possible to save hundreds of dollars to buy fuel. The rail transportation industry had reported saving about 10% on maintenance costs when they switched to condition-based preventive maintenance of rolling stock.
The digital twin is making remarkable contributions in the healthcare industry as well.
Q-Bios can be a great example to discuss. Q-Bios is the first clinical digital twin platform that harnessed the ability of digital twins to replicate anything indifferently.
Q Bios Gemini Digital Twin platform has built Mark-I, a computational biophysics model to scan the whole body. The company reported that Mark- I will examine the human body in 15 minutes and doesn't require radiation or breathe of the actual person.
Q Bios Gemini has claimed that Mark- I can work 10X better than the traditional MRI scanners for many critical diagnoses. In addition, Mark-I, the computational model, can eliminate bias or hallucination risk from AI and machine learning.
Another most significant advantage of the Mark-I is that it shields the patients from exposure to radiation, protecting them from running into the risks of developing cancer cells in the future.
Q Bios Gemini has received over $80 million from Andreessen Horowitz and Kaiser Foundation Hospitals to develop and expand its breakthrough whole-body scanning technology. In the future, the full-body scanning tech from Q Bios Gemini will provide data-driven and affordable care for all.
Medical and software companies are collaborating on digital twinning projects to create exact replicates of human body organs like the heart and the brain. The aim is to minimize risks in critical surgeries and aid organ donations.
Sim&Cure, a medical technology company, has built a digital twin called Sim&Size. This digital twin simulation will make brain surgery safer for Aneurysms patients as they will need less invasive surgery using catheters to install implants.
In another instance, Dassault Systèmes SE, a French software company, developed a Digital Twin heart using MRI images and ECG measurements. This digital twin model of the heart replicates the structure and some functions of the human heart. Now, heart surgeons can feed the patient data into the Digital Twin heart to determine whether the surgery will be successful.
Dassault Systèmes SE has launched the Living Heart Project in collaboration with academic and industrial members like Medtronic, Philips, and Boston. All the Living Heart Project members are working together to build safer and effective cardiac devices for patients.
All the major industries are gaining massive value from IoT and Digital Twins. In other words, they are saving and making money simultaneously.
According to the predictions of McKinsey & Co, IoT technology would reach $11.1 trillion in economic impact by 2025. In addition, Cisco reported that data derived from IoT devices would surpass 800 Zettabytes by the end of 2021. There's no doubt that industries using IoT devices are experiencing explosive growth.
These industries are witnessing such massive growth because they managed to cut off shocking downtimes with Industry 4.0 technologies and build the ability to predict future failures and make necessary repairs using a digital twin.
Sadly, many companies have no idea about unplanned downtime's costs, root causes, and consequences. According to Service Max, 82% of companies reported that they had experienced unplanned downtime for three consecutive years. In addition, these companies experienced an average outage duration of 4hours every day with a median cost of $2 million.
Service Max also concluded that 70% of companies have no idea when their production machines will need maintenance or upgrades.
So, we can say that companies adopting IoT and digital twins are increasingly performing better than those avoiding emerging technologies. It happens because IoT and digital twins improved situational awareness and aided industry leaders in making faster business decisions.