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Simulation and Experimental Study on the Precision Molding of Irregular Vehicle Glass Components.

Zhijun Chen1, Shunchang Hu2, Shengfei Zhang2

  • 1School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Micromachines
|October 28, 2023
PubMed
Summary

This study developed a numerical model to optimize glass molding processes for large automotive components. The optimized process reduces stress, improves dimensional accuracy, and balances energy consumption for higher yields.

Keywords:
energy consumptionlarge irregular glass componentsmoldingoptimization

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Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Computational Mechanics

Background:

  • Large, irregular glass components for vehicles suffer from low yield rates.
  • High stress and dimensional deviations during glass molding are primary causes.
  • Existing methods lack optimization for balancing quality and energy efficiency.

Purpose of the Study:

  • To establish a numerical model for large glass component molding.
  • To identify dominant factors influencing molding quality (stress, deviation, energy).
  • To achieve a synergistic balance between quality characteristics and energy consumption.

Main Methods:

  • Development of a numerical model for glass molding simulation.
  • Analysis of molding temperature and pressure effects on quality metrics.
  • Application of the NSGA-II optimization algorithm.

Main Results:

  • Molding temperature significantly impacts energy consumption and residual stress.
  • Molding pressure is the key factor for dimensional deviation.
  • Optimized parameters include specific heating/cooling rates, holding time, molding temperature, and pressure.

Conclusions:

  • The numerical model accurately predicts outcomes, with experimental verification showing <20% error.
  • The optimized scheme provides a viable solution for precision molding of large, irregular glass components.
  • Findings offer guidance for improving manufacturing processes and yield rates.