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Review
. 2022;120(1-2):85-101.
doi: 10.1007/s00170-022-08859-0. Epub 2022 Feb 10.

Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review

Nan-Yang Zhao  1 , Jiao-Yuan Lian  2 , Peng-Fei Wang  2 , Zhong-Bin Xu  1   2   3
Affiliations
Review

Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review

Nan-Yang Zhao et al. Int J Adv Manuf Technol. 2022.

Abstract

The quality control of plastic products is an essential aspect of the plastic injection molding (PIM) process. However, the warpage and shrinkage deformations continue to exist because the PIM process is easily interfered with by several related or independent process parameters. Thus, great efforts have been devoted to optimizing process parameters to minimize the warpage and shrinkage deformations of products during the last decades. In this review, we begin by introducing the manufacturing process in PIM and the cause of warpage and shrinkage deformations, followed by the mechanism about how process parameters, like mold temperature, melt temperature, injection rate, injection pressure, holding pressure, holding and cooling duration, affect those defects. Then, we summarize the recent progress of the design of experiments and four advanced methods (artificial neural networks, genetic algorithm, response surface methodology, and Kriging model) on optimizing process parameters to minimize the warpage and shrinkage deformations. In the end, future perspectives of quality control in injection molding machines are discussed.

Keywords: Injection molding; Optimization methods; Process parameters; Shrinkage; Warpage.

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Conflict of interest statement

Conflicts of interestThe authors declare that they have no conflicts of interest.Competing interestsThe authors declared that they have no conflicts of interest within the last 3 years of beginning the work.

Figures

Fig. 1
Fig. 1
(a) Injection molding flow chart (b) A simplified model of an injection modeling machine
Fig. 2
Fig. 2
(a) Acceptable product (b) Product with severe warpage (c) Product with severe volumetric shrinkage [86]
Fig. 3
Fig. 3
Tie bar sensors used in injection molding [54]
Fig. 4
Fig. 4
(a) Warpage in injection molding (b) Warpage versus temperature difference (c) Warpage versus wall thickness (d) Influence of processing parameters on total warpage ((A) mold temperature, (B) melt temperature, (C) packing pressure, (D) packing time and (E) cooling time) [72]
Fig. 5
Fig. 5
General DOE process [56]
Fig. 6
Fig. 6
Flowchart of BPNN / GA [57]
Fig. 7
Fig. 7
Configuration of the ANN model [60]
Fig. 8
Fig. 8
Complete process of the optimization algorithm based on the Kriging model [112]
See this image and copyright information in PMC

References

    1. Huang XL, Yang JR, Sun YX, Chen YW, Wang XM, Du SM, Hua ZK. Novel combined shield design for eye and face protection from COVID-19. Adv Manuf. 2021;9:130–135. doi: 10.1007/s40436-020-00333-y. - DOI - PMC - PubMed
    1. Singh SK, Khawale RP, Chen H, Zhang H, Rai R. Personal protective equipments (PPEs) for COVID-19: a product lifecycle perspective. Int J Prod Res. 2021;2021:1–22. doi: 10.1080/00207543.2021.19155-11. - DOI
    1. Vahabi H, Wu H, Saeb MR, Koo JH, Ramakrishna S (2021) Electrospinning for developing flame retardant polymer materials: current status and future perspectives. Polymer 2021:123466. 10.1016/j.polymer.2021.123466 - DOI
    1. Abdul R, Guo G, Chen JC, Yoo JJW. Shrinkage prediction of injection molded high density polyethylene parts with taguchi/artificial neural network hybrid experimental design. Int J Interactive Design Manuf (IJIDeM) 2020;14:345–357. doi: 10.1007/s12008-019-00593-4. - DOI
    1. Haidiezul A, Hazwan M, Lee WS, Najihah NF, Fadhli I (2020) Shrinkage optimisation on the 3D printed part using Full Factorial Design (FFD) optimisation approach. IOP Conference Series: Materials Science and Engineering 932:012109. 10.1088/1757-899X/932/1/012109 - DOI

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