I’m a huge fan of the Icelandic firm Euler. The company lets you take the images that your LPBF machine already generates and analyses them. Euler can predict errors before they occur, let you find the root causes of problems, and reduce the need for window peeking. And you don’t have to integrate with people or hang all sorts of equipment on your printer. You can test Euler today on your data and quickly find out if it works or not for you. I love that kind of ease of use and think that this is a powerful way to quickly and painlessly engage with customers.

Given the team’s deep computer vision and machine learning expertise, the analysis could be more powerful than others’. But, while it is difficult to benchmark any quality assurance (QA) and fault detection tools, it’s easy to test out Euler in a few minutes. So if you are going to get any QA or fault analysis tool, benchmarking it with Euler is something you’ll always do (even if you’ve secretly already decided that you want the other tool) because that’s painless. And some of those people will find out that actually, Euler could be better for them than the alternatives. Testing Euler is a question of a short meeting, drag-and-drop, and this could make the company a real success in additive manufacturing.

Euler’s AI highlights warpage and other defects directly on build images in real time.

The €2 million round was led by Frumtak Ventures and Kvanted. The money will be used for marketing, patents, and improving the product. Previously, the company has gotten €2 million in non-dilutive funding in the form of grants. Spun out of the Technical University of Denmark (DTU), the company will launch its product at Formnext 2025. It already has paid customers, but will open up to a wider group at the AM event. Customers include Alloyed, high-tech manufacturer KMWE, the DTI, and the Korea Institute of Industrial Technology (KITECH).

As shown in Euler’s demo interface, the system generates detailed heatmaps of each build layer, using image data already produced by the machine.

The product is capable of real-time monitoring, real-time alerts, and automatically detecting defects. The DTU, which ran SLM machines using Euler, found a “77% reduction in time spent on failed builds, translating to potential savings of $115k in operating costs and more than 20% increase in revenue through improved overall equipment effectiveness.” These kinds of overall equipment effectiveness (OEE) gains will be crucial as companies transition from prototyping to actual manufacturing at scale. They point to the software essentially paying for itself quickly while saving you a lot of additional time in monitoring and correcting mistakes.

Critical defect detection email alert.

Autodesk Director of Additive Manufacturing, Alexander Oster — whose group has collaborated with Euler and featured the company’s process-monitoring tools within Autodesk’s Fusion platform — stated, “In my 25 years of being in this industry, I have very rarely seen a team and product which so profoundly leapfrogs the state of the art in the way Euler does.”

Euler CEO Dr Eyþór Rúnar Eiríksson added, “Additive manufacturing has yet to live up to its hype, despite its disruptive potential. Challenges around cost, scalability, and quality assurance remain. Euler is already helping manufacturers overcome these issues, and this investment will enable us to continue our growth and expand exponentially, solidifying additive manufacturing as a reliable production process.”

While Kvanted Partner Eerik Paasikivi said,

“Additive manufacturing is entering a new era of industrial adoption, and Euler’s platform is at the forefront of this. Their technology is bringing the reliability and intelligence needed to finally make large-scale 3D printing viable.”

Euler is badly needed in an industry that is still too accident-prone and artisanal by half. The tool’s simple implementation and the fact that you can feed it historical data easily mean that we can analyze more errors across more time intelligently. Visually scrolling through builds quickly and finding or getting alerted will be very helpful as well. But I’m still the most enthusiastic about this product’s ease of implementation. If you’re running a few LPBF machines, there is no excuse not to try it.

Images courtesy of Euler

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Euler Raises €2M for AI Fault Detection in LPBF

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