A Review on Injection Molding: Conformal Cooling Channels, Modelling, Surrogate Models and Multi-Objective...

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.