Imagine creating a super-strong 'skin' for industrial tools that doesn't rust or wear down, without using toxic or rare materials. Instead of guessing and testing thousands of recipes in a lab, this project uses AI to predict the perfect mix of metals instantly. It's like having a digital chef that guarantees a recipe for a durable coating before you even turn on the oven.
Developing new industrial coatings currently relies on slow, expensive trial-and-error methods and often depends on toxic or rare materials that are subject to supply chain risks.
An AI/ML-powered computational modelling tool and a high-throughput testing system for designing sustainable high-entropy alloy coatings.
If you are an engine component manufacturer dealing with premature part failure due to heat and friction — this project developed AI-driven coating designs that increase wear resistance by 100% and oxidation resistance by 30-60%. This ensures parts last longer and perform better under extreme stress.
If you are a shipbuilding firm dealing with saltwater corrosion — this project developed green, toxic-free coatings that increase corrosion resistance by 20-40%. This reduces the frequency of expensive repainting and maintenance cycles.
If you are a cutting tool producer dealing with high material waste during R&D — this project developed a computational design tool that reduces the development cycle-time by 400-500%. This allows you to bring new hard-coating products to market significantly faster.
The project aims to reduce the overall product manufacturing cost by 20% through the use of new tooling developed during the research.
Yes, the project specifically targets the PVD (Physical Vapor Deposition) sector, which is a global market projected to reach $40.97 billion by 2028.
Based on available project data, the project is in the 'SIGNED' status and is currently executing its work packages; specific licensing terms are not yet detailed.
It develops 'green' coatings free of toxic substances and reduces the use of critical raw materials (CRMs) by 30-40%.
The project period runs from 2024-01-01 to 2027-12-31, indicating that final results will be available by the end of 2027.
The consortium is highly industry-oriented, with 50% of its 12 partners coming from the industrial sector, 7 of which are SMEs. This balance between 4 universities and 2 research centers ensures that the AI modelling is grounded in practical manufacturing needs across 7 different European countries.
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