Neural networks are predicting how metal will deform under pressure to pilot robots through the tricky process of fabricating aircraft.
What’s new: Machina Labs crafts metal using AI-guided robotic arms, Bloomberg reported. The company recently inked contracts with the United States Air Force, the U.S. National Aeronautics and Space Administration, and Hermeus, which makes hypersonic airplanes.
How it works: The system combines robot arms, sensors, and machine learning models to form, trim, finish, and polish metal sheets according to a computer-aided design. Working in pairs, robot arms on either side of a sheet apply pressure to sculpt deformations up to four feet deep. The system works on aluminum, steel, and titanium in varying thicknesses and sizes upward of 4 feet by 12 feet. A basic two-arm setup costs $2.5 million.
- Unspecified neural networks plan an arm’s path, determine how much force to apply, and predict how the metal will respond to pressure and how it might spring back.
- Laser scans compare the robots’ progress to the design specification in real time. A neural network adjusts the arm’s motion to compensate for differences.
- Based on the scans, the system creates a digital twin that’s used to check quality. Random forest and Bayesian models detect defects and forecast a maintenance schedule.
Behind the news: Most sheet-metal manufacturing is performed manually by skilled workers. Some parts can be mass-produced, but manual labor is still required to build molds. Both processes are slow, laborious, and expensive — a problem exacerbated by a shortage of craftspeople.
Why it matters: Large machines like airplanes and automobiles are expensive to manufacture. Robots guided by deep learning models can bring costs down by fabricating parts quickly and precisely and by recognizing defects before they leave the factory.
We’re thinking: This application of deep learning is riveting.