The AID4GREENEST project is advancing the digital transformation of green steel development through AI. In its first reporting period, it achieved major technical milestones, introducing new approaches to steel characterisation, modelling, and performance prediction. Key developments include AI tools linking steel chemistry, processing, and microstructure to enable faster, model-based alloy design with reduced environmental impact. Generative models produce realistic microstructures from processing parameters, advancing digital steel design.
A breakthrough is also underway in long-term performance prediction. The project is developing an accelerated creep testing method supported by simulations and Bayesian calibration, crucial for components like high-temperature turbine shafts. Simulations and experiments on meter-scale parts covering thermal tracking, dilatometry, and high-temperature testing support a mean-field model for microstructure evolution during forging and quenching, aiding in energy-efficient process optimisation.
A landing website for the project’s digital platform, microstructuredb.com was launched. It will manage large datasets from advanced characterisation and integrate AI tools, following FAIR data principles and aligning with standards. The platform aims to support data sharing, reproducibility, and model deployment in materials science.
AID4GREENEST promises to accelerate steel R&D, improve AI predictability, and drive EU decarbonisation and industrial competitiveness. To ensure lasting impact, ongoing research must expand tool applicability, with industrial-scale validation critical in forging and heat treatment. Commercial success will require exploitation plans, industry engagement, and platform sustainability.
The project is working closely with ESTEP and EMMC to ensure alignment with industry and standards, and is committed to regulatory and standardisation integration, positioning Europe as a leader in digital steel innovation.
