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AI-guided design and optimization of recycled polymer blends for high-performance and sustainable composites

CORDIS · observation · Publication date unknown

gn with systematic variation of processing parameters (blend composition, compounding time, temperatures, screw speed, cooling rate, etc.), combined with morphological characterization. 2. Generative machine learning to model and reproduce microstructures conditioned on manufacturing parameters. 3. A multi-fidelity model based on finite element analyses to predict yield stress, ultimate strength, and toughness while quantifying epistemic and aleatoric uncertainties. 4. Bayesian active learning and multi-objective optimization to identify optimal manufacturing configurations. The framework will be validated through an experimental testing campaign. Expected outcomes include a fundamental understanding of process–microstructure–property relationships in recycled PP/PE blends, efficient predictive tools, and manufacturing guidelines. The project will directly support the circular economy by enabling recycled polymers to be used in high-demand sectors (e.g., transportation, construction), strengthening Europe’s competitiveness while reducing environmental impacts. Recycled polymer compos

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recordType

award

status

SIGNED

region

EU

value

395841

unit

EUR