Software

Plastics supply chains are moving from fragmented visibility to predictive control through digital twins, AI, and traceability tools.

Smart controls and modified plasticizing systems help stabilize recyclate injection molding despite variations in viscosity and feedstock.

Reducing melt overheating, back pressure, and poor mold cooling can shorten cooling time and improve injection molding efficiency.

Engineers use machine learning algorithms to map polymer degradation, replacing physical trials with precise predictive models for industrial extrusion pipelines.

Designing high-performance polymeric coatings requires balancing multiple formulation variables that interact in complex and often unpredictable ways.

Virtual injection molding training using digital twins improves skills, cuts costs, and reduces material waste.

Advancements in biotechnology highlight how engineered microbial catalysts can recycle plastic waste in vivo.

A newly developed, open-access tool helps plastic recyclers pinpoint appropriate market applications.

AI-aided polyurethane simulation reduces modeling time from days to seconds. Digital material twins optimize tool design and predict foaming behavior.

Artificial Intelligence (AI) may help bridge the gap between scientific research and policy in the plastics industry.