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Updated: Jun 14, 2025

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars
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Multi-Objectives Optimization of Plastic Injection Molding Process Parameters Based on Numerical DNN-GA-MCS Strategy.

Feng Guo1, Dosuck Han1, Naksoo Kim1

  • 1Department of Mechanical Engineering, Sogang University, Seoul 04107, Republic of Korea.

Polymers
|August 29, 2024
PubMed
Summary

An intelligent optimization strategy using deep neural networks, genetic algorithms, and Monte Carlo simulations enhances the structural performance of carbon fiber-reinforced polymer parts. This method precisely identifies optimal plastic injection molding parameters for improved material properties and reduced weight.

Keywords:
carbon fiber-reinforced polymers (CFRPs)multi-objective optimizationmultiple structural performanceplastic injection molding (PIM)surrogate model methodologies

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Area of Science:

  • Materials Science and Engineering
  • Computational Mechanics
  • Polymer Engineering

Background:

  • Optimizing structural performance in carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) is complex due to intricate relationships between process parameters and material properties.
  • Existing methods often struggle to precisely determine optimal settings for multi-objective performance enhancements in PIM products.

Purpose of the Study:

  • To develop and validate an intelligent optimization technique for enhancing the multiple structural performance metrics of PA6-20CF CFRP PIM products.
  • To establish intrinsic relationships between processing methods and material properties by identifying complex process parameters.
  • To numerically investigate the PIM structural performance of an automotive front engine hood panel.

Main Methods:

  • Integration of a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS) into a DNN-GA-MCS strategy.
  • Utilized mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time as design variables.
  • Employed Z-score normalization for evaluating the comprehensive objective function in multi-objective optimization of molding process parameters.

Main Results:

  • The DNN-GA-MCS strategy accurately and efficiently selected optimal process parameters for the PIM front hood panel.
  • Achieved improvements of 8.63%, 6.61%, and 9.75% in key objectives compared to the training set mean.
  • Reduced weight by 16.67% compared to a full AA 5083 hood panel, with significant gains in lateral (92.54%), longitudinal (93.75%), and torsional (106.85%) strain energy.

Conclusions:

  • The proposed DNN-GA-MCS methodology demonstrates considerable potential for optimizing the structural performance of CFRP PIM products.
  • The strategy provides stability and precision for accurate results in complex PIM numerical targets.
  • Validated enhancement effects on global and local multi-objectives for molded polymer-metal hybrid (PMH) components.