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AI-assisted polymer design screens millions of candidates to identify chemically recyclable packaging polymers that still meet barrier and thermal targets.

AI control for recycled plastics stabilizes injection molding despite resin variability, reducing defects and improving operator confidence with explainable models.

Rigid polyurethane composites with industrial waste fillers: mechanical strength, thermal conductivity, and machine-learning guided optimization.

Mechatronics technology (MT) and improved machine learning (ML) algorithms improve rubber and plastic manufacturing temperature control.

Machine learning (ML) is revolutionizing quality control in manufacturing, enabling faster, smarter, and more efficient processes. By addressing the limitations of traditional methods, ML enhances defect detection, waste reduction, and overall reliability.

Tooling digitalization can be the solution for today’s manufacturing systems, where the tool failures can cause as much as 20% downtime, leading to significant productivity and profit losses. 

Fatigue failure prediction methods of elastomers help estimate product service life and play a critical role in preventing catastrophic failures.

A digital twin includes real-time data and continuous feedback from the physical world. Simulation is typically a static or hypothetical model used for scenario-based analysis.

BBTWINS project addresses challenges in the EU agri-food sector. It unites tech companies, biochemistry specialists, and innovative feedstock producers to create a digital platform based on ‘digital twins’ technology.

The need to lower the steep costs of traditional manufacturing, involving expensive tooling and molds, makes advancing composite manufacturing a key research focus.