The Power of Machine Learning in Aviation Manufacturing Business
Look, there’s this whole thing about machine learning being some kind of magic trick that fixes everything. Not quite! But the truth is, for an industry like aviation that deals with tons of data, machine learning can be a real game-changer. It can make designing, building, and even maintaining aircraft way faster and more accurate. There is a team at Acubed, and their project ADAM is all about figuring out how to use machine learning as a cool tool, along with other digital stuff, to improve things across the board.
Machine learning is like a smart sidekick to Artificial Intelligence (AI). It lets computers learn from all the information they’re fed, figure out hidden patterns, and then make choices way faster than us humans ever could.
Think of it like this, umm… have you ever wondered, how Netflix suggests shows you might like, or how your email knows what’s spam? That’s machine learning working its magic behind the scenes!
Free your focus, empower your work
The aerospace industry is full of top-notch folks with years of experience under their belts. These veterans are like the rockstars of the field, and machine learning isn’t here to take their place. Instead, it’s like a superpowered sidekick that can help them out.
Think about it this way: a lot of aerospace stuff is still done by hand because safety is the number one priority. Machine learning won’t be flying any planes anytime soon (at least not yet!), but it can be a whiz at looking through mountains of data to:
- Catch problems before they even show up, meaning less time spent on constant checks.
- Make things run smoother so our experienced workers can focus on the most important tasks.
- Free up some time for these experts to share their knowledge and train the next generation.
Basically, machine learning is like giving the existing team a superpower! This way, safety stays on top, but things also get faster and more efficient — that’s a win-win for everyone in aerospace manufacturing! and this is how machine learning can fit into aerospace manufacturing, even though they focus more on safety and experience for their customers and passengers.
Just to be clear, we’re not talking about replacing all the amazing people in aerospace with machines. The whole point is to use these machine learning models to get computers to handle the boring, repetitive tasks that take up a lot of time. That way, our brilliant human workers can focus on the stuff that really matters and use their skills in even more meaningful ways.
So, how are we actually putting this machine learning stuff to good use? Check out these three examples to see how it’s making a difference in design, building, and even after-sales service for airplanes!
Machine learning and design
Designing a new aircraft part might look good on paper, but building it is another story! This is where machine learning steps in to make things smoother.
The industry is collaborating with research students at universities to create a faster way of figuring out how easily a part can be made. This includes things like the tools needed, how long it’ll take, and how much it’ll cost.
Think of a seasoned mechanic who has seen countless car parts over the years. This mechanic can instantly understand how a new part works based on its shape and how it connects to other parts. We’re training a similar system, but instead of a mechanic, it’s a machine learning program. This program learns from a massive database of existing parts, analyzing their shapes and connections. Then, just like the mechanic, it can automatically figure out the best way to build a new part based on its design.
Designing aircraft parts is usually a messy business. Different teams using different tools in different places — it takes forever! But machine learning throws a party and gets everyone together in one smooth system. This lets design and manufacturing folks work closer, shoulder to shoulder, and optimize part designs way faster than ever before!
Machine learning in manufacturing and production
Building airplanes involves a lot of moving parts, spread out across different locations. Keeping track of everything can be a real headache! That’s why once airbus was working on a connected toolset for their A320 assembly line in Alabama. This fancy toolkit will be like a hawk, constantly scanning for any problems that might pop up during production, catching them before they cause major delays. Now, this isn’t some fancy magic trick, it’s just smart data analysis working its wonders!
Now, we are thinking about how these machine-learning models are created? There are typically three stages in creating machine learning models:
- Data collection
- Data engineering to build meaningful features
- Machine learning algorithms to deliver predictive insights
In the aircraft manufacturing setup, there’s a lot happening all the time. Getting precise data is tough, so the manufacturers start slow. But this could be really useful. They are always keeping a track of any unusual happenings during production and share them with the managers clearly and in simple terms. This helps assembly line managers spot issues fast and fix them, making things smoother and better. Eventually, we can use this data to teach machines and make our production even more efficient. Like, we can predict how long a particular job will take with more accuracy.
Machine learning in services
Designing an aircraft interior is a big job with lots of details and information to sort through, which can take weeks. But imagine if you could do it instantly! That’s what currently the team is working on with machine learning. The current solution can check if the design works technically and even create drawings, lists of parts, and cost estimates automatically, making everything much quicker.
This is all about a specific type of machine learning called deep learning. Regular machine learning deals with organized data like numbers or categories, but deep learning can tackle messy stuff like text, images, or videos. Unlike regular machine learning where humans pick out the important bits, deep learning figures it out on its own. Think of it as what makes your voice-controlled smart assistant or self-driving cars.
With deep learning, engineers are developing software that can quickly figure out what changes are required to move from one customer configuration to another. It can instantly tell us what’s possible and what’s not, streamlining the process.
What’s next?
People are spreading rumors across the world, will the robots take over the pilot’s job? While autonomous flight often dominates discussions about machine learning in the aviation industry, it’s clear that these technologies offer significant advantages to design, manufacturing, and assembly processes as well. I frequently find myself realizing the solution of time and cost efficiencies the industry can deliver. The awareness and understanding of AI, machine learning, and deep learning are growing, but there’s still progress to be made. Nevertheless, Do clap 👏🏻👏🏻 50 times and share the article if you like it. Thank you for giving your valuable time.
