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Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

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Front. Mech. Eng.    2018, Vol. 13 Issue (1) : 85-95    https://doi.org/10.1007/s11465-018-0491-0
REVIEW ARTICLE
Intelligent methods for the process parameter determination of plastic injection molding
Huang GAO, Yun ZHANG, Xundao ZHOU, Dequn LI()
State Key Laboratory of Material Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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Abstract

Injection molding is one of the most widely used material processing methods in producing plastic products with complex geometries and high precision. The determination of process parameters is important in obtaining qualified products and maintaining product quality. This article reviews the recent studies and developments of the intelligent methods applied in the process parameter determination of injection molding. These intelligent methods are classified into three categories: Case-based reasoning methods, expert system-based methods, and data fitting and optimization methods. A framework of process parameter determination is proposed after comprehensive discussions. Finally, the conclusions and future research topics are discussed.
Keywords injection molding      intelligent methods      process parameters      optimization     
Corresponding Author(s): Dequn LI   
Just Accepted Date: 08 November 2017   Online First Date: 26 December 2017    Issue Date: 23 January 2018
 Cite this article:   
Huang GAO,Yun ZHANG,Xundao ZHOU, et al. Intelligent methods for the process parameter determination of plastic injection molding[J]. Front. Mech. Eng., 2018, 13(1): 85-95.
 URL:  
https://academic.hep.com.cn/fme/EN/10.1007/s11465-018-0491-0
https://academic.hep.com.cn/fme/EN/Y2018/V13/I1/85
Fig.1  Flowchart of the initial process parameters setting based on CBR
Methods References Applications
KBR Kim and Suh [10] Weld line
Pandelidis and Kao [11] Bubbles, cracking, shrinkage
Jan and O’Brien [12] Pit marks, surface ripples, flashing
Kameoka et al. [13] Short shot, sink mark, warpage
Yang et al. [1] Short shot, flash, dimension
RBR Shelesh-Nezhad and Siores [8] Burn streaks, weld line, jetting
FR He et al. [14,15] Short shot, sink mark, flash, flow mark, warpage
Tan and Yuen [16] Flash, short shot, black streak
Zhou et al. [9] Short shot, flash
Chen et al. [17] Weld line
Li et al. [18] Short shot, flash
Tab.1  Expert system-based methods and their applications in injection molding
Fig.2  Framework of a typical FR system
Fig.3  The main framework for the data fitting and optimization methods
Method classification References
Space filling Taguchi method [27–59]
LHD [60–68]
UD [69,70]
Classical CCD [71–73]
Others Full factorial experimental design [74,75]
Tab.2  Survey of experimental design methods for injection molding
Fitting model Applications
RSM [27,30,35,36,40,72] Warpage, shrinkage
Kriging [28,64,67,76] Warpage, cycle time, deflection, and max injection pressure
PR [46,71] Sink mark, waviness, weight
ANN [32,33,37,38,61,77–86] Warpage, shrinkage, runner volume, weight, cycle time, strength
SVR [87,88] Weight, cycle time, max injection pressure, shrinkage
Tab.3  Survey of surrogate models used in injection molding
Approaches Advantages Disadvantages
PR Can be easily constructed, has clear rules on parameter sensitivity, allows quick convergence of noisy functions Instabilities that may arise for high-order polynomials; difficulty in obtaining sufficient sample data for high-order polynomials; cannot interpolate new sample points and be restricted by the selected function type
Kriging Does not need to construct a specific mathematics model, is extremely flexible in capturing nonlinear behavior, is accurate for nonlinear problems under small sample with moderate scale of variables More complex compared with RSM
RSM Requires less manual intervention, does not need the trial-and-error method to achieve a suitable model because of the benefit of data driven Falls easily into the local minimum value
ANN Has learning capability, is best for repeated application, has a highly nonlinear mapping capability, can approximate any function High computational expense, lack of a complete and mature theoretical system and high dependence on experience, overfitting; it is a “black box” method and cannot obtain explicit and meaningful models for further analysis
SVR Has solid theoretical foundation, is suitable for small sample data, has good generalization ability Not suitable for large sample data
Tab.4  Advantages and disadvantages of several surrogate models
Fig.4  The framework of the hybrid intelligent system
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