{"id":5750,"date":"2024-10-23T04:10:36","date_gmt":"2024-10-23T04:10:36","guid":{"rendered":"https:\/\/tech.newat9.com\/index.php\/2024\/10\/23\/applying-ai-to-satellite-manufacturing\/"},"modified":"2024-10-23T04:10:36","modified_gmt":"2024-10-23T04:10:36","slug":"applying-ai-to-satellite-manufacturing","status":"publish","type":"post","link":"https:\/\/tech.newat9.com\/index.php\/2024\/10\/23\/applying-ai-to-satellite-manufacturing\/","title":{"rendered":"Applying AI to satellite manufacturing"},"content":{"rendered":"


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MOUNTAIN VIEW, Calif. \u2013 Companies are proceeding cautiously in applying artificial intelligence to satellite manufacturing.<\/p>\n

Blue Canyon Technologies, for example, wants to better understand how AI can contribute to manufacturing without jeopardizing cybersecurity. <\/p>\n

\u201cWhen you\u2019re trying to teach an AI machine, where does your data go,\u201d Chris Winslett, Blue Canyon Technologies general manager, asked at the Satellite Innovation conference here. \u201cThere\u2019s also a concern about pulling in data from external applications. Where do they come from?\u201d<\/p>\n

Still, AI can assist in the engineering design process for Blue Canyon, a Raytheon Technologies subsidiary.<\/p>\n

\u201cYou want to be able to use AI to help you turn a ton of data into information,\u201d Winslett said. Then people can spend their time making decisions, as opposed to going over spreadsheets, he added.<\/p>\n

\"NanoAvionics
NanoAvionics will base its satellite on its MP42 microsatellite bus, like the one pictured here. Credit:<\/span> Konsberg NanoAvionics<\/span><\/figcaption><\/figure>\n

Kongsberg NanoAvionics<\/h4>\n

Karolis Senvaitis, Kongsberg NanoAvionics engineering operations director, shares Winslett\u2019s concerns about AI models.<\/p>\n

\u201cHow can you trust what you\u2019re getting? What\u2019s the source?\u201d Senvaitis asked. \u201cIf you\u2019re aggregating results, are you getting the results that you want?\u201d<\/p>\n

Until those questions are answered clearly, \u201cI would hardly see this being integrated directly into manufacturing or testing,\u201d Senvaitis said.<\/p>\n

He agreed, though, that AI is useful for collecting and analyzing large datasets.<\/p>\n

\"\"
Illustration of Blue Canyon Technologies\u2019 Saturn-class satellite buses. Credit: BCT<\/figcaption><\/figure>\n<\/p>\n

Machina Labs<\/h4>\n

Data provenance is less of a problem for Machina Labs, a Los Angeles startup developing\u00a0robotic technology for manufacturing metal tooling<\/a>. Rather than pulling in data from myriad sources or suppliers, Machina Labs generates its own data.<\/p>\n

\u201cA 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,\u201d said John Borrego, Machina Labs vice president of production. \u201cUsing load sensors and positional sensors and highly accurate scanning software and devices, we\u2019re able to determine if a part is going to be meeting requirements or not.\u201d<\/p>\n

Data from the sensors and devices are stored in a secure cloud.<\/p>\n

\u201cWe\u2019re 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,\u201d Borrego said.<\/p>\n

Related<\/em><\/h3>\n<\/p><\/div>\n


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