Imagine designing a new steel alloy like testing a recipe in a kitchen; usually, you'd cook and taste a hundred versions to find the best one, which wastes a lot of ingredients. This project creates a digital 'smart chef' that predicts the result before you even turn on the stove. It uses AI to simulate how different ingredients and heat settings will affect the final metal, saving months of trial and error.
Steel development relies on slow, expensive 'trial and error' physical testing. This leads to high material waste, carbon emissions, and high component rejection rates.
Six AI-based tools for steel design and performance prediction, and an open, interoperable digital platform for materials data.
If you are a steel mill dealing with high component rejection rates and slow product cycles — this project developed AI tools that link chemistry and process to design low-CO2 steel. This can cut development time and cost by over 50%. It reduces the need for expensive physical testing.
If you are a manufacturer dealing with unpredictable material failure in forged parts — this project developed ML models that predict creep performance from accelerated tests. This allows for faster validation of part durability without waiting years for real-world wear data.
If you are an alloy developer dealing with fragmented data and slow R&D — this project developed an open, interoperable digital platform aligned with European standards. This enables faster screening of new materials using a shared data repository.
Based on project data, the tools aim to cut steel development time and cost by over 50%.
The project includes industrial validation with partners like OCAS and RFC, and aims to deliver industry-ready tools.
Based on available project data, the project is developing an open online platform for knowledge transfer, but specific licensing terms for the six AI tools are not detailed.
The digital platform is specifically aligned with European standards including EMMC, EMCC, and EMMO.
The project runs from September 2023 to August 2026, meaning tools are currently in development and validation phases.
The consortium is well-balanced for technology transfer, featuring a 36% industry ratio with 4 industrial partners and 2 SMEs. The collaboration spans 5 countries (BE, DE, ES, FI, PL), combining the academic rigor of 3 universities and 3 research centers with the practical validation capabilities of industrial players like OCAS and RFC.
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Contact us to explore licensing opportunities for the 6 AI steel-design tools.
