Metal 3D printing is a well-known method, but it’s often too hard to use on a large scale because it’s too complicated, expensive, or inaccurate. With $14 million from Nvidia and Boeing, Freeform wants to change that by creating a new method for adding metal to metal prints that, as the company says, changes the game. There is also an AI aspect to this.
Erik Palitsch, who is CEO, and TJ Ronacher, who is president, were both co-founders and worked at SpaceX as chief architects and lead analysts on the Merlin engines and other projects. They saw the promise of 3D printing metal parts while they were there, but they also saw the problems with the method.
“We saw that metal printing could be useful. It could change almost every industry that makes metal things.” âBut adoption has been slow, and at best there has been some success,â Palitsch said. “Why isn’t it possible to use on a large scale?” Mainly because of three things: poor quality that changes all the time; speedâcommercial printers are very slow; and costâthese printers are very expensive.
They came to the conclusion that they could break the whole thing open if they could make the process work so that they sold printing services instead of printers. To start Freeform, they teamed up with Tasso Lappas, who used to be the CTO of Velo3D.
Companies were mostly making a mistake when they tried to run their metal-printing businesses like standard factories do with CNC machines. That way, you can sell the machine and its software and make it work with any forms and methods you choose. But Palitsch said that metal addition is not the same.
According to him, these devices today work in a “open loop,” which means they play back a file. “They should have known better than that because using a laser to melt metal powder is a very complicated process that can be changed in a lot of different ways.”
It’s not a good idea to sell people a machine and then tell them “learn how to use it, good luck.”
“But there’s a lot you can do if you don’t want to build and package a printer into a box,” Palitsch said. “You can start from scratch and build an automated factory.”
Their plan is to offer printing as a service using a closed-loop system in a special machine that watches the print every microsecond and changes different settings to get the kind of print that would be expected at SpaceX.
The company is proud of many technological breakthroughs, but the feedback loop and the AI that runs it are the two that matter the most right now.
“Our system has high-speed computer vision feedback that works at the microsecond level, and all that data is being processed on the latest FPGAs and GPUs.” Palitsch said, “We had to put together this whole stack ourselves using parts that have only been out for a few years.”
The quality problems are lessened by the closed-loop system with real-time monitoring, which still lets complicated geometries be printed quickly. And because they only do printing, their business plan is easy to understand.
But for that part of the system to work, the second big step forward in technology was a machine-learning model that could learn quickly and accurately enough to do the tracking.
“Erik and TJ lived this and came to the same conclusions,” Lappas said. “This industry needed a level of computing power and sensors that had never been used before.”
“No one had datasets that worked over long periods of time that we needed in order to fully understand how to control the process.” So, we began making a cutting-edge telemetry system, a platform that would gather controlled, curated datasets that would almost name themselves.
They were able to build a model with this data to get more data for a better model, and so on.
But Then They Had To Deal With The Need For Speed
Some things we do are the same as generative models, but other things are different. “The latency, on the other hand, is very different,” Lappas said. “We need to do our inference in microseconds so that these processes can be closed.” Because neither the data nor the computing could be bought off the shelf, they had to make the GPU/FPGA “AI on steroids” combination from scratch.
Freeform is “building the biggest metal additive dataset in the world,â which is why Palitsch said that companies like Boeing are coming to them. “Nobody else has this basic, core ability to collect and process data like we do.”
When you add that to the basic benefits of printing-based manufacturing, like how flexible and quick plants are, it makes a pretty strong business case.
Nvidia and Boeing’s AE Ventures put in a total of $14 million, but they wouldn’t say how that money was split. Because each business invested, they got something in return. Nvidia gave them access to H100s and other computer hardware, and Boeing will help them get approved as a supplier and probably buy a lot of parts. Freeform will also become a part of Nvidia’s Inception startup programme.
As Palitsch put it, their clients are in “the whole nine” industries: aircraft, automotive, industrial, and energy. They wouldn’t say which ones on the record, but they did say they make some things, like exhaust parts for Formula 1 cars and parts for rocket engines. They want to hire up to 55 people over the next year and use the money to grow and make their next generation of printers, which will be much faster.
He said that it took some time for their plan to go from theory to practice, but that their orderly and technical approach is also what made them successful.
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Palitch said, “It was a slow change.” “But now that I think about it… We built from scratch the fastest laser melting platform in the world, along with its gear and software. It took six of us. “People told us we couldn’t do it, but we did it.”
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