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

被引:103
作者
Zhao, Nan-yang [1 ]
Lian, Jiao-yuan [2 ]
Wang, Peng-fei [2 ]
Xu, Zhong-bin [1 ,2 ,3 ]
机构
[1] Zhejiang Univ, Coll Energy Engn, Hangzhou 310027, Peoples R China
[2] Zhejiang Univ City Coll, Sch Engn, Hangzhou 310015, Peoples R China
[3] Zhejiang Univ, Ningbo Res Inst, Inst Robot, Ningbo 315100, Peoples R China
基金
中国国家自然科学基金;
关键词
Injection molding; Process parameters; Optimization methods; Warpage; Shrinkage; MULTI RESPONSE OPTIMIZATION; TAGUCHI METHOD; MULTIOBJECTIVE OPTIMIZATION; SEARCHING METHOD; REGRESSION-MODEL; NEURAL-NETWORK; DESIGN; PARTS; SYSTEM; HYBRID;
D O I
10.1007/s00170-022-08859-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:85 / 101
页数:17
相关论文
共 122 条
[1]   Shrinkage prediction of injection molded high density polyethylene parts with taguchi/artificial neural network hybrid experimental design [J].
Abdul, Rafa ;
Guo, Gangjian ;
Chen, Joseph C. ;
Yoo, John Jung-Woon .
INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM, 2020, 14 (02) :345-357
[2]  
Iniesta AA, 2013, REV FAC ING-UNIV ANT, P43
[3]  
Amin S. Y. M., 2013, J TEKNOL SCI ENG, V63, P51, DOI [10.11113/jt.v63.1454, DOI 10.11113/JT.V63.1454]
[4]   Experimental study of warpage and shrinkage in injection molding of HDPE/rPET/wood composites with multiobjective optimization [J].
Azad, Reza ;
Shahrajabian, Hamzeh .
MATERIALS AND MANUFACTURING PROCESSES, 2019, 34 (03) :274-282
[5]   Optimization and Numerical Simulation Analysis for Molded Thin-Walled Parts Fabricated Using Wood-Filled Polypropylene Composites via Plastic Injection Molding [J].
Azaman, M. D. ;
Sapuan, S. M. ;
Sulaiman, S. ;
Zainudin, E. S. ;
Khalina, A. .
POLYMER ENGINEERING AND SCIENCE, 2015, 55 (05) :1082-1095
[6]   Shrinkage and Warpage Detailed Analysis and Optimization for the Injection Molding Process Using Multistage Experimental Design [J].
Barghash, Mahmoud A. ;
Alkaabneh, Faisal Alkhannan .
QUALITY ENGINEERING, 2014, 26 (03) :319-334
[7]  
Bement T.R., 1989, Technometrics, V31, P253, DOI [DOI 10.1080/00401706.1989.10488519, 10.1080/00401706.1989, DOI 10.1080/00401706.1989]
[8]  
Byon Sungkwang, 2020, [Journal of the Korean Society of Manufacturing Process Engineers, 한국기계가공학회지], V19, P55, DOI 10.14775/ksmpe.2020.19.06.055
[9]   Shrinkage behavior and optimization of injection molded parts studied by the Taguchi method [J].
Chang, TC ;
Faison, E .
POLYMER ENGINEERING AND SCIENCE, 2001, 41 (05) :703-710
[10]   Optimization of clamping force for low-viscosity polymer injection molding [J].
Chen, Jian-Yu ;
Yang, Kai-Jie ;
Huang, Ming-Shyan .
POLYMER TESTING, 2020, 90