Artificial intelligence is no longer the stuff of science fiction. It’s not even some distant future technology that may or may not come to fruition.
AI is here, and is a key strategy for leading aerospace companies:
So what exactly does AI have to offer you if you’re in the aerospace and defense industry? How can you use it to make your engineering processes more efficient and accurate?
This article will walk through the current state of AI and how it is being used by aerospace and defense companies to give you an idea.
Artificial intelligence (AI) and machine learning are complex elements of smart technology that enable machines to engage in problem solving. This enables machines to streamline and optimize processes typically done by humans, resulting in greater speed and accuracy.
Artificial intelligence in the aerospace industry is used across functions:
Given the labor shortages and safety challenges recently faced by the industry, improving business processes with AI and machine learning is a top priority for many leading AI defense and aerospace companies.
As we mentioned above, aerospace companies leverage AI & machine learning in a variety of ways. Here are a few examples of where companies are seeing better efficiency and safety through the use of these automated technologies.
AI in aerospace engineering is commonly used to track, schedule, and manage maintenance based on historical data and predictive analytics. This enables them to predict parts and schedules before they’re even needed.
Bell Flight, for example, leverages AI to reduce the mean time to resolution for problems and promote longer maintenance free operating periods.
By using recorded data as a base, AI can optimize fuel consumption during the most taxing parts of a flight. These systems are advanced enough to build custom profiles based on pilots, aircraft, locations, weather, and more.
Southwest Airlines is just one example of a company that’s investing in data analytics as a path to greater fuel efficiency
According to the American Consumer Satisfaction Index, nearly every airline declined in customer satisfaction in 2022. Not only can AI improve the aircraft maintenance and operation side, but also the customer-facing parts of the business.
These include chatbots to converse with customers, discover their pain points, and deliver solutions. By automating the more tedious conversations, AI can free up human workers to deliver the personal experiences that a machine simply can’t do.
In the COVID-19 pandemic, nearly all airlines used chatbots as requests began to soar. Now, they’re a critical component of the industry, especially as labor shortages continue.
AI is a key component of virtual reality (VR) and augmented reality (AR) platforms, which are growing in popularity as a training platform for pilots and engineers. For example, Aries is a startup providing VR-based training solutions. Fyr, another startup, has developed a head-mounted AR-based visualization system.
A number of aerospace manufacturers are leveraging AI to automate and improve factory operations and supply chains. This has the benefit of reducing delays, lowering costs, improving productivity, and generally improving customer experiences.
Although we haven’t even seen the full impact of applications of AI in aerospace and defense, there are plenty of benefits that are on the horizon. Here are some of the more important.
AI can make models of what people do and how they behave, and look at aircraft requirements to build them up in a more automated way. Almost like an automated checklist, which can circumvent human error and improve safety.
Autonomy is nothing new to the aerospace industry. Getting a plane to fly itself, takeoff, and land is fairly common and straightforward. The challenge is in automating air traffic control systems, which currently faces a number of challenges.
AI and machine learning are the first automation technologies that can handle the complexity of air traffic control systems without causing major safety concerns. This is a major first step toward human acceptance of a move toward more autonomy.
Given the record pilot shortages we’re facing right now - and which are barely starting to ease - AI can provide a solution to the problem. First, a smart enough AI can eliminate the need for a copilot by providing a layer of redundancy comparable to a human copilot.
Second, there are areas where people will become more comfortable with fully autonomous flight. Freight transportation, for example, is an area where most people won’t have an issue with full autonomy - the worst case scenario is a couple hundred destroyed boxes.
By reducing the need for pilots, AI can help to alleviate the pressures currently facing the industry. The pilots that remain in service, then, will be able to fill the gap by providing the human touch, something airline passengers definitely need.
Artificial intelligence in aerospace is also a major component in system modeling. Now, not every simulation requires direct human input in order to run. AI can predict certain types of codes and assist in building not only the hardware model, but also the software and the simulation environments.
This combination makes the development and testing process more efficient and speeds up time to delivery. It also leads to more accuracy, as machines often can spot mistakes more quickly and thoroughly than human engineers can.
Additionally, with such high margins, physical prototyping is not a viable option for testing new aircraft systems. Each fatal mistake costs hundreds of millions of dollars, not to mention the risk of a crash in a populated area.
AI & machine learning enable engineers to move the testing processes to a virtual environment, which helps to improve a model’s chance of success before a physical prototype exists.
The rise of recent platforms like ChatGPT have demonstrated the power of generative AI. For aerospace engineers, this has major implications, particularly around embedded software. Now, AI applications in aerospace include building out code in languages like C or C++. Aerospace engineering teams no longer have to rely on expensive, high-demand talent to build out their embedded software, saving both time and resources.
AI & machine learning are already mainstays of the aerospace sector and we’re seeing early signs from the defense industry. In fact, the Department of Defense, the Air Force and the US Navy are all considering AI for military applications and weapons systems starting with cloud initiatives such as the DOD Cloud One. The question is: are you fully leveraging the benefits to speed up your engineering processes?
If you want to design and develop new aircraft systems quickly, efficiently, and safely, then an AI-powered modeling platform is critical.