Artificial Intelligence

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.

Artificial Intelligence (AI) offers significant opportunities for small and medium-sized enterprises (SMEs). However, many SMEs face challenges, including limited resources, that slow AI adoption. This includes not only the plastics industry, but is a bottle neck across all fields.

Industry 6.0 represents the next evolution in manufacturing, driven by artificial intelligence (AI) and autonomous decision-making robots, known as infobots. The progression of injection molding technology and its integration with advanced robotics offer greater efficiency and sustainability.

For tooling digitalization, we already spoke about the basics and application milestones here the final chapter closing loop checking the role of knowledge management across the supply chain.

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.

Researchers in Finland developed a new smart label for intelligent packaging and conducted a life cycle assessment (LCA) to evaluate its environmental impacts.

Virtual reality is revolutionizing the plastics industry, turning complex challenges into streamlined solutions from design to maintenance.

Artificial intelligence (AI) transforms the plastics industry through predictive maintenance, which uses data-driven insights to foresee equipment failures.