Cycle time optimization for rpet injection molding using multivariate modeling

Overview

Smart Mold introduces an innovative solution to enhance the efficiency and quality of recycled PET (rPET) injection molding processes. By focusing on the molecular weight distribution and composition, we aim to optimize cycle time and improve the morphology of molded parts. This approach leverages both physics-based and data-driven methodologies to establish correlations that predict and enhance production outcomes, leading to predictable product quality and reduced cycle times.

Technical specifications

Key Features:

• Material Characterization: Identification of critical material parameters that correlate with cycle time, focusing on variations from different post-consumer recycled (PCR) suppliers.
• Injection Molding Experiments: Conducting experiments with instrumented molds using a design of experiments approach to collect comprehensive data.
• Data Analysis: Utilizing historical and experimental data to analyze the influence of material, processing, machine, and preform design parameters on cycle time and crystallinity.
• Modeling Techniques: Application of multivariate modeling approaches, including regression and machine learning, to develop accurate cycle time prediction models. These models are validated with data from controlled experiments and historical records.

Technology readiness level

The current technology readiness level is 6, indicating that the solution has been validated in relevant environments and is ready for further testing and potential commercial application.