Lockheed Martin to Integrate Machine Learning with Metal 3D Printing

Lockheed Martin and the Office of Naval Research are exploring how to apply artificial intelligence (AI) to train robots to independently oversee and optimize 3D printing of complex parts.

The two-year, $5.8 million contract will study and customize multi-axis robots for laser deposition. The team led by Lockheed Martin's Advanced Technology Center will develop software models and sensor modifications for the robots to build better components.

"We will research ways machines can observe, learn and make decisions by themselves to make better parts that are more consistent, which is crucial as 3-D printed parts become more and more common," says Brian Griffith, Lockheed Martin's project manager. "Machines should monitor and make adjustments on their own during printing to ensure that they create the right material properties during production."

Researchers will apply machine learning techniques to additive manufacturing so variables can be monitored and controlled by the robot during fabrication.

"When you can trust a robotic system to make a quality part, that opens the door to who can build usable parts and where you build them," says Zach Loftus, Lockheed Martin Fellow for additive manufacturing. "Think about sustainment and how a maintainer can print a replacement part at sea, or a mechanic print a replacement part for a truck deep in the desert. This takes 3D printing to the next, big step of deployment."

According to Lockheed Martin, technicians currently spend many hours per build testing quality after fabrication, but that's not the only waste in developing a complex part. It's common practice to build each part compensating for the weakest section for a part and allowing more margin and mass in the rest of the structure. Lockheed Martin's research will help machines make decisions about how to optimize structures based on previously verified analysis.

Lockheed Martin and its team will vet common types of microstructures used in an additive build. The team will measure the performance attributes of the machine parameters as well as these microstructures and align them to material properties, enabling machines to make decisions about how to print a part with the desired performance. Work will begin with Ti-6AI-4V, and integratie the related research with seven industry, national lab and university partners.