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This review explores advanced computational methods for optimizing plastic injection molding. It highlights techniques like surrogate modeling and multi-objective optimization to improve efficiency, quality, and sustainability in manufacturing.

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Area of Science:

  • Manufacturing Engineering
  • Computational Science

Background:

  • Plastic injection molding is a key global manufacturing process, facing challenges in optimizing its complex phases.
  • Sophisticated computational techniques are crucial for enhancing efficiency, quality, and sustainability.

Purpose of the Study:

  • To review and analyze optimization methodologies in plastic injection molding.
  • To focus on integrating advanced modeling, surrogate models, and multi-objective optimization.

Main Methods:

  • Analysis of key injection molding phases (plasticizing, filling, packing, cooling, ejection).
  • Examination of computational tools (Moldex3D, Autodesk Moldflow) and optimization algorithms (evolutionary algorithms, simulated annealing).
  • Exploration of surrogate models (Kriging, RSM, ANNs) to reduce computational costs.

Main Results:

  • Cooling phase optimization is critical, impacting 50-80% of cycle time; conformal cooling channels (CCCs) show promise.
  • Surrogate models effectively address computational challenges in complex simulations.
  • Integration of advanced techniques enhances process efficiency, product quality, and sustainability.

Conclusions:

  • Advanced modeling, surrogate models, and multi-objective optimization are vital for injection molding.
  • Future research should focus on adaptive AI and machine learning for real-time mold optimization.
  • This review provides a guide for practical implementation and theoretical advancement in the field.