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Selecting the right alloy for mold manufacturing is essential for meeting industry demands for durability, thermal conductivity, and manufacturability.

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.

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.

The tooling digitalization during the whole supply chain is vital for maximizing productivity, improving operational efficiency, and minimizing downtime. But how and with what tools?

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. 

Reverse engineering (RE) in plastics processing has become an essential method for product reproduction, improvement, and optimization. It involves a thorough understanding of the design, manufacturing, assembly, and maintenance stages.

Molds must be engineered with precision to achieve optimal results, making mold life a key consideration. Several factors influence injection mold life, including material choice, operating conditions, design, and maintenance.

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.

For more than 70 years, hot runner systems have become a vital technology in plastic injection molding, improving production efficiency and enhancing product quality.

Multiple injection molding parameters interact to influence the final quality of the molded part. Certain parameters are recognized as having the most significant impact.