Combining simulation and AI technologies like Machine Learning & Deep Learning unveils outstanding new possibilities and opportunities. Moreover, the use of AI on traditional approaches to simulation may even bring forth a paradigm shift in the industry regarding how we perceive and develop the simulation.
Although simulation and Artificial Intelligence (AI) are two different technology paradigms, these technologies are related to each other in their primary forms. In computer engineering, simulation imitates an environment or a machine, while AI effectively simulates human intelligence.
While they may be related, simulation and AI were being used very differently with different mathematical and engineering approaches. However, in recent years, the development of AI-based simulations has experienced rapid growth in various industries.
For instance, now infamous, Cyberpunk 2077 used AI to simulate facial expressions and lip-syncing in the gaming industry. On the other hand, Microsoft Flight Simulator 2020 used AI to generate realistic terrains and air traffic.
Read more: Simulation in Digital Twin for Aerospace, Manufacturing, and Robotics
The power of AI to enable rapid simulation development with faster, more optimized, and less resource-hungry simulations even on a large scale would empower more applications of simulation technology in far wider industries and platforms.
However, to understand the benefits of using AI in simulations and its development, we need to understand the traditional simulation development approach and its use in this scenario at first.
Traditional Simulation vs. AI-based Simulations
The basic idea behind simulation development is to gather data related to the machine, environment or anything for different inputs and conditions. These data would then be collected, analyzed, and studied to understand how the machine/environment/anything simulated functions and behaves under different conditions and situations.
This understanding would then be used to build a basic mathematical model that can govern and imitate the actual object in different conditions, then used to construct a simulation model that can replicate or simulate the real thing.
However, when AI is used to build these simulation models, the AI has to be fed with data related to the object/environment's behavior and how these subjects (object/environment to be simulated) function under different conditions and settings. During this process, the AI model requires relevant data that can be considered a sample of the simulation subject and represents the subject properly.
Generally, Neural Networks (NNs) would be used as the AI model to be trained. After the training, this would simulate the subject and its behavior.
Both approaches, either traditional approaches or AI for simulation, have their advantages and disadvantages. One of the significant advantages of the conventional simulation method is that the mathematical model defined after studying the simulation subject can be reused and reconstructed easily.
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This allows other development teams to verify or reuse the same mathematical principles or models to generate the simulation. A traditional approach would also enable the developers to expand the simulation based on their understanding of the subject without explicit testing or proof test.
One of the significant disadvantages of this traditional approach remains to be its complex and resource-hungry process to generate the simulation. This is because everything has to be done by the simulation developers, who would also have to be experts in respective domains such that they need to understand the subject very closely.
Meanwhile, in AI-based simulation development, data is one of the essential components. The subject's information needs to be in abundant amounts and deterministic such that the data can represent the subject very closely.
This type of data may not be available readily when the data needs to be either collected or generated. But after the collection of accurate and abundant data, an AI-based/aided approach is very advantageous since there is no need to understand the subject by developers themselves.
Another significant advantage of the AI-based simulation holds within the power of AI to discover patterns or behavior in subjects not even considered or found by the developers. Apart from this, training an AI model usually takes a lot of time, but it may not be as resource hungry, complex, and costly as the traditional approach.
One of the significant disadvantages of the AI approach is that the model builder cannot be recognized or understood by developers in any way, so it cannot be usually reconstructed unless similar data or input is fed again to train the model.
Apart from this, due to the data required to qualify the model, expanding the model will generally be impossible without sufficient data.
Combining Simulations and AI
Using AI in simulation generation or development would enable data-powered development with rapid changeability and minimal human involvement. Although the simulation traits would be considered too complex for humans to develop, AI may easily reconstruct such characteristics if sufficient data is provided.
Due to this, AI can be used to simulate something too hard, complex, or time-consuming for humans in a short time without too much effort. Thus, not only would the development of simulations be faster, more productive, and easy, but AI would also enable the rapid iteration and tweaking of simulations that would be far less feasible, especially on a large scale.
We can open new doors by combining the power of AI and simulation for product design and development. Generally, without AI simulations, developers have to design a product/model that must be intensively tested before production, and changes are needed after the story. Then, the same process would have to be repeated.
This process is very resource-intensive. But through AI, design changes and validation can be easily tested through simulation, enabling rapid iteration and development.
The development and adoption of AI for simulation are far more required in industries like Augmented Reality (AR) and VR (Virtual Reality), where the sheer complexity of building high scale models, environments, and graphics through the traditional methods would be infeasible compared to using AI to develop and deploy simulations with its data-driven approach of development. The opportunities in AR and VR could be far more explored and matured through the AI to generate and develop simulations.
Alongside this, simulation of subjects like fluids (air and water) is brutal to construct with only a traditional approach, the result of which would still not be good and very close to reality. But with the help of AI, such simulations would be closer to reality and more refined.
One of the significant advantages of AI-based simulation compared to the traditional approach is that the conventional system would be significantly resourced heavy since it usually calculates each simulation particle.
However, AI-based simulation would enable such complex simulations easily since AI can perform these calculations/predictions much faster and less resource hungry. Alongside this, generative simulations like the generation of models, terrains in games, and product designs would also be possible with AI.
For instance, take the game Microsoft Flight Simulator 2020 as an example. This game allows gamers to experience realistic flights worldwide without lagging in the quality of models, terrains, and environment.
By traditional approach, this would mean that the game developers would have to model and build all terrains used in 3D along with matching landscapes and backgrounds to give the simulation a realistic feeling.
This would have cost the game developers a massive amount of time, resources, and a considerable number of experts to deal with complex problems lying ahead in such an enormous project. Realistically, such a project would not be feasible or even practically be possible to complete.
But through the use of AI, the developers used massive amounts of data that are already available and combined them with vast amounts of computation through the power of the cloud to train an AI model that could build realistic 3D models of terrains, environments, along with grasses, trees, and water-based upon the real world.
The results produced were pretty spectacular and received substantial critical acclaim from game developers and gamers alike.
Conclusion
By combining simulations and AI, we can unfold new opportunities and endless possibilities in different industries.
Along with technologies like Machine Learning and Deep Learning, AI-enabled simulations will be propelled by the data-driven backend. Conquering the disadvantages of the traditional approach to simulation, AI-based simulations will be able to push the boundaries of what simulations can do.
Even the most complex simulations, which would be next to impossible when developed with traditional methods, will be attainable by combining simulations and AI.
Moreover, with AI enabling rapid development of more optimized and improved quality, the industry may experience a revolution empowering next-level simulations with realism and details never seen before.
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.
Read more: How Are Industries Creating New Opportunities By Combining Simulations and AI
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
Read more: How Digital Twins Can Help In Saving The Environment
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!
Conclusion:
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!
Simulation experts in industry 4.0 must have a passion for digital twin technology today for industry research. Digital Twin simulation is entering mainstream use as more industries are adopting this technology. According to Gartner's IoT implementation 2019 survey, 75% of organizations already use Digital Twin or plan to in a year. Notably, all the companies willing to adopt Digital Twin are implementing Internet-of-Things.
Digital Twinning is not a new technology. In 2002, Michael Grieves of the Florida Institute of Technology introduced the concept of Digital Twin publicly. In 2010, NASA showcased the first practical implementation of Digital Twinning to improve the physical model simulation of spacecraft.
We can see Digital Twin has been around for two decades now. Yet, many businesses are confused about the value of Digital Twin Technology. In addition, many companies don't know the use of Digital Twin in the modern energy, chemical, and process manufacturing industries.
Many companies still don't know that Digital Twin can enjoin the disconnected processes cutting out manual efforts that can be time-consuming.
For instance, interns or low-level employees will still follow the outdated method to gather engineering information. They would walk around one department to another to collect data required for engineering research.
Many engineers use CAD and PLM software and other sources to collect data to make informed design decisions. A few engineers are lucky enough to use enterprise search engines to pull information from various departments from hundreds of documents, folders, presentations, etc.
Read more: Simulation in Digital Twin for Aerospace, Manufacturing, and Robotics
Now that we are entering the age of automation, companies must adapt to cultural change and access the right technology. They need to integrate technologies like Digital Twin to help teams gain information without any hassle. They also need to save employees from the pain of surfing through numerous record systems.
There are other reasons to consider Digital Twin to accelerate business innovation. Here we've discussed 3 of them below:
1) The Rapid build-up and expansion of data:
The business environment of the energy and chemical industries is already volatile. On top of that, the decision-making cycle is in an array across these industries due to piled-up data sources.
System Digital Twins made for entire plants, or factory systems can rescue energy and chemical industries. A massive amount of operational data can be collected, organized, and analyzed from various devices and products.
Human decisions are not rational, even if we make sound judgments after weighing evidence and assessing probabilities. It happens because the human brain tends to simplify information processing. So, cognitive biases, including memory and attention biases, influence human decision-making.
System digital twins can eliminate human bias for critical decision makings. System digital twins can also provide a single logical view of the actual situation based on evidence, probabilities, and analytics.
It is also essential that you know your needs before adopting Digital Twin Technology. Therefore, you must ask these three questions to ensure your success with Digital Twin:
2) What type of analytics should Industry players seek?
The factory systems and manufacturing plants involve complex processes today. So, measuring KPIs isn't easy now.
The digital twin can resolve this problem by providing deeper analytics from factory systems and plants, taking multi-dimensional factors and non-linear trade-offs into account.
The digital twin can build an accurate understanding of the future based on historical and present performances data. The digital twin can recommend the best strategies that can maximize profitability for the industries. Next, the experts will need to assess each recommendation and its impact to make the best decision for the businesses.
Therefore, industries can use the digital twin technology as a supporting tool to aid decisions enabling improved safety, reliability, and profitability.
3) Digital Twin Model Utility across the entire lifecycle of the asset:
Manufacturers use digital twins differently at each stage of the product development cycle.
Initially, manufacturers start working with Digital Twin Prototype or DTP. Then, manufacturers use DTP to design, analyze, and plan out the process to predict the future shape of the actual product.
In the next phase, manufacturers use Digital Twin Instance or DTI. DTI is the virtual twin of a physical asset. Developers will use DTI to run multiple tests and determine how the product will behave in different scenarios.
The DTI stays connected with the physical asset throughout its lifecycle. As a result, developers will add more operational data to improve it over time.
In the final phase, manufacturers will use Digital Twin Aggregate or DTA. Manufacturers use DTA to cross-examine the physical product, predictions, and learning based on the collected data from the previous phase.
People from engineering, operations, supply chain, shop floor even board room can look inside the assets and processes of the Digital Twin technology at every stage.
Companies integrate AI, machine learning, predictive analytics, etc., into the system with high hopes. They do it because they believe that digital transformation will cut out all the manual workload. However, when they realize that a lot of the work still depends on the human end, they get shocked.
Industries may have entered the automated age and have innovative IoT solutions at their disposal. However, automated systems cannot replace the human touch in many critical areas of business. For example, humans still need to implement and monitor automated systems in manufacturing plants.
Read more: How Are Industries Creating New Opportunities By Combining Simulations and AI
Automation cannot replace other tasks like enhancing product design, building strategies, and growth roadmap, decision making, communicating with stakeholders, applying creativity to solve problems, etc. These areas will continue to need human intervention.
Companies need to set clear expectations when moving forward with the digital transformation of the assets.
The purpose of digital transformation and digital twin is to make the technical aspects of the job easier. In addition, Digital Twin technology will provide you the intelligence to help you focus your hard work on beneficial outputs.
Companies building Digital Twin Technology today are the pioneers of shaping the agile and intelligent industries of tomorrow. So, they need to develop the right digital twin platforms to leverage the full potential of digital transformation to create an exemplary model that others can follow.
The first steps will always be challenging. You can expect objections and hurdles to come your way. However, all these troubles are manageable if you know the proper ways to manage them.
Here is a brief guideline to follow for successful digital twin adoption in business:
To sum it up, digital twin adoption has the scope to attract more stakeholders' buy-in. Companies can show them the data-driven rewards based on concrete analysis instead of flawed predictions. So, the stakeholders will always have the know-how of the direction they are heeding with you.
"The true benefit of a digital twin: it gives you business intelligence to make better decisions in the future. It doesn't eliminate or minimize the work you're doing now, but it fundamentally changes what you're going to do next." - Former chief executive of Cambridge City Council, Andrew Grant
As we deduce the statement of Andrew Grant, it's a life lesson that industries have learned the most brutal way around the world after the Covid-19 shock. Thus, many enterprises are seriously considering the concept of Digital Twin and thinking big to expand it.
Companies are now interested in optimizing business operations based on the real-time insights gained from manufacturing plants and product use. As a result, they are more focused on satisfying orders, resolving root causes that are hindering growth, and maximizing factories' performance based on solid predictions.
We have already entered the age of automation as the 4.0 industrial revolution has begun. Today, companies maybe just interested in predictive maintenance. However, the use of Digital Twin Technology will expand where it will be integrated not for products but into manufacturing processes and entire factory systems.
