While the potential of AI in satellite manufacturing is undeniable, companies are approaching its implementation with caution, prioritizing cybersecurity and data integrity.
Blue Canyon Technologies, a Raytheon Technologies subsidiary, is exploring how AI can contribute to manufacturing without compromising cybersecurity. “When you’re trying to teach an AI machine, where does your data go,” Chris Winslett, Blue Canyon Technologies general manager, asked at the Satellite Innovation conference. “There’s also a concern about pulling in data from external applications. Where do they come from?”
Winslett emphasizes the value of AI in streamlining the engineering design process. “You want to be able to use AI to help you turn a ton of data into information,” he said, adding that it frees up engineers to make informed decisions rather than manually analyzing data.
Kongsberg NanoAvionics shares similar concerns about data provenance. “How can you trust what you’re getting? What’s the source?” asked Karolis Senvaitis, engineering operations director. “If you’re aggregating results, are you getting the results that you want?”
Senvaitis believes that AI can be valuable for collecting and analyzing vast datasets, but its direct integration into manufacturing and testing processes is premature until these questions are addressed.
Machina Labs, a Los Angeles startup specializing in robotic technology for metal tooling, tackles data provenance differently. By generating its own data through its robotic systems, Machina Labs minimizes external data risks.
“A lot of our processes incorporate design engineers and process-development engineers, who essentially interpret this plethora of data that is generated by our forming robots,” explained John Borrego, Machina Labs vice president of production. “Using load sensors and positional sensors and highly accurate scanning software and devices, we’re able to determine if a part is going to be meeting requirements or not.”
Data from these sensors is securely stored in the cloud. “We’re just scratching the surface, because now we have concrete data that can be used and leveraged to optimize processes and reduce any kind of quality defects for future parts,” Borrego added.