MIT researchers 3D print sensors for satellites

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MIT announced that a team of researchers had created the “first fully digitally fabricated plasma sensors” for spacecraft in orbit. The sensors, also known as Retarding Potential Analyzers (RPA), are used by satellites to determine the chemical composition and ion energy distribution of the atmosphere.

The 3D-printed and laser-cut hardware performed just as well as the state-of-the-art solid-state plasma sensors fabricated in a clean room. Cleanroom-made sensors are expensive and take weeks of complex fabrication, while 3D-printed sensors can be produced for tens of dollars in days.

The low cost and rapid production of the sensors make them ideal for CubeSats. They are inexpensive, low-power, and lightweight satellites that are often used for communication and environmental monitoring in Earth’s upper atmosphere.

A glass-ceramic was used in a manufacturing process developed for 3D printing with plastics. This means the researchers were able to create intricately shaped sensors capable of withstanding the large temperature swings a spacecraft would encounter in lower Earth orbit.

“Additive manufacturing can make a big difference to the future of space hardware. Some people think that when you 3D print something, you have to concede less performance. But we have shown that this is not always the case. Sometimes there’s nothing to trade,” said Luis Fernando Velásquez-Garcia, senior scientist at MIT’s Microsystems Technology Laboratories (MTL) and lead author of the paper introducing the new plasma sensors.

An RPA was first used on a space mission in 1959. The sensors detect energy in ions, or charged particles, floating around in plasma, which is a superheated mixture of molecules found in Earth’s upper atmosphere.

With the sensors aboard an orbiting spacecraft like a CubeSat, the instruments measure energy and perform chemical analyzes that can help scientists predict the weather or monitor climate change.

The key to the success of an RPA is the structure of the case which aligns with the series of electrically charged meshes. The structure must be electrically insulating while resisting sudden temperature variations. A printable glass-ceramic material that exhibits these properties, known as Vitrolite, has been used.

Vitrolite was launched in the early 1900s and was often used in colored tiles, and became common in art deco buildings. The material is able to withstand temperatures up to 800 degrees Celsius without breaking down. The polymers used in solid-state RPAs begin to melt at 400 degrees Celsius.

Typically with the 3D printing process for ceramics, the laser will leave the material coarse and weak due to the heat. The MIT researchers used vat polymerization, a process in which a 3D structure is built one layer at a time by repeatedly submerging it in a vat of liquid material, in this case Vitrolite.

UV light is used to harden the material after each layer is added, before it is re-immersed in the tank. Each layer is only 100 microns thick (about the diameter of a human hair), allowing the creation of smooth, poreless and intricate ceramic shapes.

Additive manufacturing technology makes it possible to design objects in a very complex way. The precision allowed the researchers to create laser-cut meshes with unique shapes so that the holes lined up perfectly when placed inside the RPA case.

The high precision could enable 3D-printed sensors for applications in fusion energy research or supersonic flight. According to the researchers, the process of rapid prototyping could even further spur innovation in the design of satellites and spacecraft.

Velásquez-Garcia added: “If you want to innovate, you have to be able to fail and take the risk. Additive manufacturing is a very different way of making space hardware and if it fails it doesn’t matter because I can create a new version very quickly and inexpensively and really iterate on the design. It is an ideal sandbox for researchers.

MIT researchers often involve additive manufacturing in their work. Recently, a team from the institute created a machine learning system capable of adjusting the 3D printing process to correct errors in real time.


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