Hyperganic launches its AI-based algorithmic design software to the public
Engineering software developer Hyperganic has announced the public launch of Hyperganic core 3the latest iteration of the company’s AI-based algorithmic design software.
Backed by $7.8 million in funding last year, the software platform allows users to design 3D printable parts using algorithmic models, providing an alternative to the traditional component design process. Hyperganic Core is expected to dramatically streamline engineering workflows in industries such as aerospace, allowing complex geometries to be computationally finalized in minutes while ensuring optimized performance.
As of June 15, Hyperganic Core 3 is officially open to a “limited number” of public applicants for the very first time. Free download, smooth deployment will happen in batches of hundreds of users at a time.
Lin Kayser, CEO of Hyperganic, said, “Algorithmic engineering translates ideas into designs in minutes, with the engineer defining the rules and the computer generating the results.”
The Hyperganic Core story
In development for seven years, the backbone of Hyperganic Core is a special purpose geometry core based on 3D voxels. Much of the platform is written directly in assembler, which is the low-level programming language just a cut above binary machine code (0s and 1s). As such, it is close to the hardware level and optimized for a wide variety of processor architectures. Kayser says it even works well on his fanless MacBook Air.
The initial release was Hyperganic Core 1. The (then small) team worked closely with various 3D printer manufacturers to ensure the platform was compatible with powder bed, resin and extruding.
Hyperganic Core 2 was the first time the Kayser team integrated an external coding interface with Visual Studio and C#. The language was chosen because it is simple yet modern and suitable for larger scale software development. This is what allowed the company to grow sevenfold in the last 18 months, after hiring a whole department of software engineers.
Since Core 2, the company has also created comprehensive online tutorials that people can use to learn about algorithmic engineering. Hundreds of early adopters have since been invited to the platform, many of them coding their own AI design algorithms for fields such as aerospace and consumer goods.
Last month, Hyperganic partnered with leading 3D printer manufacturer EOS to integrate Core with EOS laser powder bed fusion 3D printers. The companies have already used the software to design a complete aerospike rocket engine, which was fabricated on an EOS machine.
Democratize generative design with Core 3
With Core 3 now available to the public, anyone willing to do so can learn how to design 3D printable parts using algorithmic engineering. Kayser states that for non-commercial and open-source users, Hyperganic Core will always be free to use. Customers also have the ability to monetize the objects they design, as well as the custom algorithms used to design them.
Kayser writes: “You can share and open up your code – and I hope many of you do, because we engineers should be focusing on solving new problems instead of sweating over things that have been done in the past.”
Despite its hot launch, Hyperganic Core isn’t the only generative design software on the market. The biggest fish in the pond is nTopology, which recently introduced its third-generation lattice technology for 3D printing. Designed to be quick and easy to use, the new lattice design tools aim to provide users with greater control over complex lattice structures for DfAM applications.
Last year, engineering software developer Autodesk announced a huge 80% price reduction for its own algorithmic design software, Fusion 360 Generative Design Extension. Launched commercially in 2018 for an annual subscription of $8,000 per year, the extension powered by AI has always been relatively inaccessible to anyone other than large engineering and manufacturing companies.
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The featured image shows a close-up of a 3D printed rocket engine. Photo via EOS.