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Process parameters optimization in plastic injection molding using metamodel-based optimization: a comprehensive review

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Abstract

This paper comprehensively reviews the process parameters optimization in plastic injection molding (PIM) using the metamodel-based optimization. The PIM is a typical manufacturing technology to produce lightweight and high gloss appearance plastic products. To produce the plastic product, the process parameters such as melt temperature, injection time, packing pressure, packing time, cooling temperature, and cooling time are conventionally adjusted through the trial-and-error method. Computer-aided engineering (CAE) is one of the alternatives in the PIM but the numerical simulation is computationally so expensive that the metamodel-based optimization is widely used to determine the optimal process parameters. First, typical metamodels including the sampling strategy are briefly reviewed. Then, the papers on the process parameters optimization for high product quality such as warpage, shrinkage, and weldline reduction are reviewed. Rapid heat cycle molding (RHCM) that actively controls the mold temperature is a novel PIM technology, but the process parameters optimization in RHCM is rarely discussed in the literature. Then, the RHCM is also reviewed in this paper. Not only the process parameters optimization but also the cooling channel plays an important role for high product quality and high productivity. Due to the recent advancement of metal 3D printer, it is possible to produce conformal cooling channel. It is possible to enhance the product quality and productivity by using the conformal cooling channel. Several papers on the process parameters using the conformal cooling channel are also reviewed.

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This research is partially supported by Grants-in-Aided for Scientific Research from Japan Society for the Promotion of Science (JSPS).

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    Satoshi Kitayama

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Kitayama, S. Process parameters optimization in plastic injection molding using metamodel-based optimization: a comprehensive review. Int J Adv Manuf Technol 121, 7117–7145 (2022). https://doi.org/10.1007/s00170-022-09858-x

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