Frontiers of Mechanical Engineering >
Intelligent methods for the process parameter determination of plastic injection molding
Received date: 09 Jun 2017
Accepted date: 14 Sep 2017
Published date: 23 Jan 2018
Copyright
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.
Key words: injection molding; intelligent methods; process parameters; optimization
Huang GAO , Yun ZHANG , Xundao ZHOU , Dequn LI . Intelligent methods for the process parameter determination of plastic injection molding[J]. Frontiers of Mechanical Engineering, 2018 , 13(1) : 85 -95 . DOI: 10.1007/s11465-018-0491-0
1 |
Yang D, Danai K, Kazmer D. A knowledge-based tuning method for injection molding machines. Journal of Manufacturing Science and Engineering, 2000, 123(4): 682–691
|
2 |
Kolodner J L. An introduction to case-based reasoning. Artificial Intelligence Review, 1992, 6(1): 3–34
|
3 |
Mok S L, Kwong C K, Lau W S. Review of research in the determination of process parameters for plastic injection molding. Advances in Polymer Technology, 1999, 18(3): 225–236
|
4 |
Kwong C K, Smith G F, Lau W S. Application of case based reasoning injection moulding. Journal of Materials Processing Technology, 1997, 63(1–3): 463–467
|
5 |
Kwong C K, Smith G F. A computational system for process design of injection moulding: Combining blackboard-based expert system and case-based reasoning approach. International Journal of Advanced Manufacturing Technology, 1998, 14(4): 239–246
|
6 |
Mok S L, Kwong C K, Lau W S. An intelligent hybrid system for initial process parameter setting of injection moulding. International Journal of Production Research, 2000, 38(17): 4565–4576
|
7 |
Mok S L, Kwong C K. Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding. Journal of Intelligent Manufacturing, 2002, 13(3): 165–176 doi:10.1023/A:1015730705078
|
8 |
Shelesh-Nezhad K, Siores E. An intelligent system for plastic injection molding process design. Journal of Materials Processing Technology, 1997, 63(1–3): 458–462
|
9 |
Zhou H M, Zhao P, Feng W. An integrated intelligent system for injection molding process determination. Advances in Polymer Technology, 2007, 26(3): 191–205
|
10 |
Kim S G, Suh N P. Knowledge-based synthesis system for injection molding. Robotics and Computer-Integrated Manufacturing, 1987, 3(2): 181–186
|
11 |
Pandelidis I, Kao J F. DETECTOR: A knowledge-based system for injection molding diagnostics. Journal of Intelligent Manufacturing, 1990, 1(1): 49–58
|
12 |
Jan T C, O’Brien K. A user-friendly, interactive expert system for the injection moulding of engineering thermoplastics. International Journal of Advanced Manufacturing Technology, 1993, 8(1): 42–51
|
13 |
Kameoka S, Haramoto N, Sakai T. Development of an expert system for injection molding operations. Advances in Polymer Technology, 1993, 12(4): 403–418
|
14 |
He W, Zhang Y F, Lee K S,
|
15 |
He W, Zhang Y F, Lee K S,
|
16 |
Tan K, Yuen M. An expert system for injection molding defect correction. In: Proceedings of the 3rd International Conference, Computer Integrated Manufacturing. 1995, 11–14
|
17 |
Chen M Y, Tzeng H W, Chen Y C,
|
18 |
Li D Q, Zhou H M, Zhao P,
|
19 |
Dhaliwal J S, Benbasat I. The use and effects of knowledge-based system explanations: Theoretical foundations and a framework for empirical evaluation. Information Systems Research, 1996, 7(3): 342–362
|
20 |
Inaba Y, Sakakibara S, Taira T,
|
21 |
Liao S H. Expert system methodologies and applications—A decade review from 1995 to 2004. Expert Systems with Applications, 2005, 28(1): 93–103
|
22 |
Pardo S A. Fractional factorial designs. In: Pardo S A, ed. Empirical Modeling and Data Analysis for Engineers and Applied Scientists. Cham: Springer, 2016, 59–93
|
23 |
Liu T F, Zhang C L, Yang G S,
|
24 |
Roy R K. A Primer on Taguchi Method. New York: Van Nostrand Reinhold, 1990
|
25 |
Helton J C, Davis F J. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety, 2003, 81(1): 23–69
|
26 |
Fang K T, Lin D K J, Winker P,
|
27 |
Chen W C, Nguyen M H, Chiu W H,
|
28 |
Zhao J, Cheng G. An innovative surrogate-based searching method for reducing warpage and cycle time in injection molding. Advances in Polymer Technology, 2015, 35(3): 288–297
|
29 |
Wang Y, Kim J, Song J I. Optimization of plastic injection molding process parameters for manufacturing a brake booster valve body. Materials & Design, 2014, 56: 313–317
|
30 |
Jou Y T, Lin W T, Lee W C,
|
31 |
Wen T, Chen X, Yang C,
|
32 |
Chen W C, Liou P H, Chou S C. An integrated parameter optimization system for MIMO plastic injection molding using soft computing. International Journal of Advanced Manufacturing Technology, 2014, 73(9–12): 1465–1474
|
33 |
Chen W C, Kurniawan D. Process parameters optimization for multiple quality characteristics in plastic injection molding using Taguchi method, BPNN, GA, and hybrid PSO-GA. International Journal of Precision Engineering and Manufacturing, 2014, 15(8): 1583–1593
|
34 |
Azaman M D, Sapuan S M, Sulaiman S,
|
35 |
Chen W C, Wang L Y, Huang C C,
|
36 |
Tzeng C J, Yang Y K, Lin Y H,
|
37 |
Lu N Y, Gong G, Yang Y,
|
38 |
Yin F, Mao H, Hua L. A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters. Materials & Design, 2011, 32(6): 3457–3464
|
39 |
Mehat N M, Kamaruddin S. Multi-response optimization of injection moulding processing parameters using the Taguchi method. Polymer-Plastics Technology and Engineering, 2011, 50(15): 1519–1526
|
40 |
Chen W L, Huang C Y, Hung C W. Optimization of plastic injection molding process by dual response surface method with non-linear programming. Engineering Computations, 2010, 27(8): 951–966
|
41 |
Ting P H, Hsu C I, Hwang J R. Optimization of the wear properties of polyoxymethylene components under injection process conditions. Polymer-Plastics Technology and Engineering, 2010, 49(9): 892–899
|
42 |
Altan M. Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Materials & Design, 2010, 31(1): 599–604
|
43 |
Chen W C, Fu G L, Tai P H,
|
44 |
Chen C P, Chuang M T, Hsiao Y H,
|
45 |
Chen W C, Wang M W, Chen C T,
|
46 |
Deng W J, Chen C T, Sun C H,
|
47 |
Cheng W S, Chen C S, Chen S C,
|
48 |
Tsai K M, Hsieh C Y, Lo W C. A study of the effects of process parameters for injection molding on surface quality of optical lenses. Journal of Materials Processing Technology, 2009, 209(7): 3469–3477
|
49 |
Yang Y K, Shie J R, Liao H T,
|
50 |
Huang M S, Lin T Y. An innovative regression model-based searching method for setting the robust injection molding parameters. Journal of Materials Processing Technology, 2008, 198(1–3): 436–444
|
51 |
Shie J R. Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network. International Journal of Advanced Manufacturing Technology, 2008, 36(11–12): 1091–1103
|
52 |
Chen W C, Fu G L, Tai P H,
|
53 |
Wang L, Li Q, Shen C,
|
54 |
Song M C, Liu Z, Wang M J,
|
55 |
Oktem H, Erzurumlu T, Uzman I. Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part. Materials & Design, 2007, 28(4): 1271–1278
|
56 |
Kuo C F, Su T L. Optimization of injection molding processing parameters for LCD light-guide plates. Journal of Materials Engineering and Performance, 2007, 16(5): 539–548
|
57 |
Kemal Karasu M, Cakmakci M, Cakiroglu M B,
|
58 |
Liu F, Zeng S Q, Zhou H M,
|
59 |
AlKaabneh F A, Barghash M, Mishael I. A combined analytical hierarchical process (AHP) and Taguchi experimental design (TED) for plastic injection molding process settings. International Journal of Advanced Manufacturing Technology, 2012, 66(5): 679–694
|
60 |
Kitayama S, Natsume S. Multi-objective optimization of volume shrinkage and clamping force for plastic injection molding via sequential approximate optimization. Simulation Modelling Practice and Theory, 2014, 48: 35–44
|
61 |
Shi H Z, Xie S M, Wang X C. A warpage optimization method for injection molding using artificial neural network with parametric sampling evaluation strategy. International Journal of Advanced Manufacturing Technology, 2013, 65(1–4): 343–353
|
62 |
Xia W, Luo B, Liao X. An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding. International Journal of Advanced Manufacturing Technology, 2011, 56(9–12): 929–942
|
63 |
Shi H Z, Gao Y H, Wang X C. Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method. International Journal of Advanced Manufacturing Technology, 2010, 48(9–12): 955–962
|
64 |
Chen W, Zhou X, Wang H,
|
65 |
Liao X, Long F. Gaussian process modeling of process optimization and parameter correlation for injection molding. Journal of Marine Science and Engineering, 2010, 4(10): 90–97
|
66 |
Li C, Wang F L, Chang Y Q,
|
67 |
Gao Y, Turng L S, Wang X. Adaptive geometry and process optimization for injection molding using the kriging surrogate model trained by numerical simulation. Advances in Polymer Technology, 2008, 27(1): 1–16
|
68 |
Zhou J, Turng L S. Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach. Polymer Engineering and Science, 2007, 47(5): 684–694
|
69 |
Sun B S, Chen Z, Gu B Q,
|
70 |
Deng Y M, Wang L Z. Applying a uniform design of experiment approach for reducing injection moulding warpage deflection. Key Engineering Materials, 2010, 443: 57–62
|
71 |
Guo W, Hua L, Mao H J. Minimization of sink mark depth in injection-molded thermoplastic through design of experiments and genetic algorithm. International Journal of Advanced Manufacturing Technology, 2014, 72(1–4): 365–375
|
72 |
Chen C C, Su P L, Chiou C B,
|
73 |
Chen C C, Su P L, Lin Y C. Analysis and modeling of effective parameters for dimension shrinkage variation of injection molded part with thin shell feature using response surface methodology. International Journal of Advanced Manufacturing Technology, 2009, 45(11–12): 1087–1095
|
74 |
Spina R. Optimisation of injection moulded parts by using ANN-PSO approach. Journal of Achievements in Materials and Manufacturing Engineering, 2006, 15(1–2): 146–152
|
75 |
Kurtaran H, Erzurumlu T. Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm. International Journal of Advanced Manufacturing Technology, 2006, 27(5–6): 468–472
|
76 |
Deng Y M, Zhang Y, Lam Y C. A hybrid of mode-pursuing sampling method and genetic algorithm for minimization of injection molding warpage. Materials & Design, 2010, 31(4): 2118–2123
|
77 |
Cheng J, Liu Z, Tan J. Multiobjective optimization of injection molding parameters based on soft computing and variable complexity method. International Journal of Advanced Manufacturing Technology, 2013, 66(5–8): 907–916
|
78 |
Iniesta A A, Alcaraz J L G, Borbon M I R. Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm. Revista Facultad De Ingenieria-Universidad De Antioquia, 2013, (67): 43–51
|
79 |
Wang R J, Feng X X, Xia Y J,
|
80 |
Taghizadeh S, Ozdemir A, Uluer O. Warpage prediction in plastic injection molded part using artificial neural network. Iranian Journal of Science and Technology. Transaction of Mechanical Engineering, 2013, 37(M2): 149–160
|
81 |
Wang H S, Wang Y N, Wang Y C. Cost estimation of plastic injection molding parts through integration of PSO and BP neural network. Expert Systems with Applications, 2013, 40(2): 418–428
|
82 |
Shi H, Wang X, Xie S. A warpage optimization method for injection molding using artificial neural network combined weighted expected improvement. International Polymer Processing, 2012, 27(3): 341–347
|
83 |
Patel G C M, Krishna P. Prediction and optimization of dimensional shrinkage variations in injection molded parts using forward and reverse mapping of artificial neural networks. Advanced Materials Research, 2012, 463–464: 674–678
|
84 |
Chen W J, Lin J R. Application and design of artificial neural network for multi-cavity injection molding process conditions. In: Jin D, Lin S, eds. Advances in Future Computer and Control Systems: Volume 2. Berlin: Springer, 2012, 33–38
|
85 |
Yin F, Mao H J, Hua L,
|
86 |
Sun X F, Zhu P F, Lu Y J,
|
87 |
Li X, Hu B, Du R. Predicting the parts weight in plastic injection molding using least squares support vector regression. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2008, 38(6): 827–833
|
88 |
Yang J L, Yin Z Q, Guan C L,
|
89 |
Wang G G, Shan S. Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design, 2007, 129(4): 370–380
|
90 |
Simpson T W, Poplinski J D, Koch N P,
|
91 |
Oberkampf W L, Trucano T G. Validation methodology in computational fluid dynamics. In: Proceedings of Fluids 2000 Conference and Exhibit, Fluid Dynamics and Co-Located Conferences. AIAA, 2000, 2549: 19–22
|
92 |
Goldberg D E. Genetic Algorithm in Search, Optimization, and Machine Learning. Reading: Addison-Wesley, 1989, 2104–2116
|
93 |
Norman B A, Bean J C. A genetic algorithm methodology for complex scheduling problems. Naval Research Logistics, 1999, 46(2): 199–211
|
94 |
Lin M H, Tsai J F, Yu C S. A review of deterministic optimization methods in engineering and management. Mathematical Problems in Engineering, 2012, 756023
|
95 |
Dang X P. General frameworks for optimization of plastic injection molding process parameters. Simulation Modelling Practice and Theory, 2014, 41: 15–27
|
96 |
Lam Y C, Deng Y M, Au C K. A GA/gradient hybrid approach for injection moulding conditions optimisation. Engineering with Computers, 2006, 21(3): 193–202
|
97 |
Zhang J, Wang J, Lin J,
|
98 |
Kashyap S, Datta D. Process parameter optimization of plastic injection molding: A review. International Journal of Plastics Technology, 2015, 19(1): 1–18
|
/
〈 | 〉 |