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A new Thermal Enthalpy Transformation Method (TETM) accurately predicts polypropylene (PP) shrinkage by identifying in-cavity solidification. This method improves dimensional accuracy in injection molding by providing reliable process-relevant data.

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Area of Science:

  • Polymer Science and Engineering
  • Materials Science
  • Manufacturing Processes

Background:

  • Injection molding of semi-crystalline polymers like polypropylene (PP) faces challenges with part shrinkage due to volume changes during cooling and crystallization.
  • Accurate prediction of shrinkage is crucial for dimensional accuracy, but standard material tests often fail to capture in-cavity conditions (cooling rate, pressure history).
  • Existing P-V-T (Pressure-Volume-Temperature) models struggle with inconsistent solidification temperature data, compromising shrinkage prediction reliability.

Purpose of the Study:

  • To develop a modified P-V-T framework using the Tait equation for improved shrinkage prediction in PP injection molding.
  • To introduce and validate the Thermal Enthalpy Transformation Method (TETM) for determining process-relevant solidification parameters directly from in-cavity measurements.
  • To enhance the practical usability of P-V-T data for controlling shrinkage behavior under varying molding conditions.

Main Methods:

  • Developed a modified P-V-T framework incorporating the Tait equation and the novel Thermal Enthalpy Transformation Method (TETM).
  • Utilized in-cavity melt temperature monitoring with an infrared sensor to capture crystallization-induced enthalpy release.
  • Identified the effective solidification point and crystallization-completion temperature (target specific volume) from the temperature-time plateau detected by TETM.
  • Executed experiments with varying target specific volumes, melt temperatures, and mold temperatures, employing a two-stage packing strategy.

Main Results:

  • The TETM successfully identified a process-relevant solidification time and crystallization-completion temperature, overcoming limitations of standard material tests.
  • Target specific volumes determined by TETM showed a more consistent and near-linear relationship with measured shrinkage rates compared to conventional methods.
  • The modified P-V-T framework demonstrated improved robustness in solidification-time identification and practical usability for shrinkage control.
  • Experimental results validated the effectiveness of the TETM in enhancing the reliability of shrinkage prediction under dynamic molding conditions.

Conclusions:

  • The Thermal Enthalpy Transformation Method (TETM) provides a robust approach for identifying critical solidification parameters in PP injection molding.
  • The developed P-V-T framework significantly improves the accuracy and consistency of shrinkage prediction by utilizing in-cavity data.
  • This study enhances the practical application of P-V-T information for achieving precise dimensional control in injection-molded polymer parts.