Neural net model for optimizing injection molding cycle time and part quality

Overview

This innovative solution offers a neural net model designed to optimize the injection molding cycle time while ensuring high-quality parts. By analyzing various factors such as machine technology, molding process parameters, mold design, and material rheology, the model predicts cycle time and maintains equilibrium with part quality. This approach addresses the critical balance between efficiency and quality in manufacturing processes, particularly when using varying percentages of PET/PCR materials.

Technical specifications

Key features:

• Considers machine technology, including hydraulic versus electric machines
• Evaluates molding process parameters like cooling time, injection velocity, and material temperature
• Examines mold design, including hot runner vs cold runner and cavitation
• Analyzes material rheology, especially changes due to varying PCR percentages
• Utilizes a multi-layer neural net model for accurate predictions
• Incorporates 60% of collected data for model validation to ensure reliability and accuracy

Technology readiness level

The technology is at a readiness level of 9, indicating it is fully developed and validated, ready for commercial application and deployment in the industry.