Formnext Challenges: what were the most promising innovations in AM?

The innovations of winners of the Formnext Startup Challenge will be presented during Formnext Connect, which kicked off yesterday (10th). Therefore, no better timing to have a closer look at the 5 winners of the challenge and what they tell us about the latest technical developments in the industry. As always, the competition was looking to nominate innovative startups, but also those that are actively contributing to sustainability, rewarded by the AM Ventures Impact Award. Sustainability being one of the pillars often considered key to the benefits of additive manufacturing, and one which more and more investors are measuring closely. Additionally, the purmundus challenge will also reveal its winners during Formnext Connect, with this year’s theme being “Geometry and Material in Harmony”. The challenge sought innovative solutions for the beneficial use of 3D and 4D printing in product design and manufacturing!

This year, five startups managed to win over the jury of the Formnext Startup Challenge with their innovations: Addiguru, based in the US, AM Flow, based in the Netherlands, Molyworks based in the US too, NetmatX from Switzerland, and TOffeeAM from the UK. Additionally, the CEO of AM Ventures explains this year’s focus on sustainability: “Making a contribution to sustainable development is no longer just an added bonus for startups; it’s crucial to their success, and it also provides them with major business opportunities. Plus, more and more investors are factoring sustainability into their decision-making, and it won’t be long before they all do.” This year, the winner of the AM Ventures Impact Award is Molyworks.

Addiguru: External real-time monitoring

Addiguru offers an easy-to-use and affordable real-time monitoring system for additive manufacturing. Its monitoring technology is compatible with any manufacturers and integrates well into both established and newly developed AM units that work with metal. It involves a camera that connects to an external computer and looks down onto the powder bed from above. The US startup’s software automatically recognizes the relevant images and sends the photos taken to a self-learning algorithm for analysis. This then detects anomalies and informs the user accordingly.