Full Text:   <2798>

CLC number: TP277

On-line Access: 2012-05-04

Received: 2011-11-02

Revision Accepted: 2012-01-04

Crosschecked: 2012-04-05

Cited: 15

Clicked: 3256

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2012 Vol.13 No.5 P.382-394

10.1631/jzus.A1100250


Multi-objective process parameter optimization for energy saving in injection molding process


Author(s):  Ning-yun Lu, Gui-xia Gong, Yi Yang, Jian-hua Lu

Affiliation(s):  College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; more

Corresponding email(s):   luningyun@nuaa.edu.cn

Key Words:  Injection molding process, Energy saving, Multi-objective optimization, Genetic algorithm, Lexicographic method


Ning-yun Lu, Gui-xia Gong, Yi Yang, Jian-hua Lu. Multi-objective process parameter optimization for energy saving in injection molding process[J]. Journal of Zhejiang University Science A, 2012, 13(5): 382-394.

@article{title="Multi-objective process parameter optimization for energy saving in injection molding process",
author="Ning-yun Lu, Gui-xia Gong, Yi Yang, Jian-hua Lu",
journal="Journal of Zhejiang University Science A",
volume="13",
number="5",
pages="382-394",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1100250"
}

%0 Journal Article
%T Multi-objective process parameter optimization for energy saving in injection molding process
%A Ning-yun Lu
%A Gui-xia Gong
%A Yi Yang
%A Jian-hua Lu
%J Journal of Zhejiang University SCIENCE A
%V 13
%N 5
%P 382-394
%@ 1673-565X
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1100250

TY - JOUR
T1 - Multi-objective process parameter optimization for energy saving in injection molding process
A1 - Ning-yun Lu
A1 - Gui-xia Gong
A1 - Yi Yang
A1 - Jian-hua Lu
J0 - Journal of Zhejiang University Science A
VL - 13
IS - 5
SP - 382
EP - 394
%@ 1673-565X
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1100250


Abstract: 
This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process. It combines an experimental design by Taguchi’s method, a process analysis by analysis of variance (ANOVA), a process modeling algorithm by artificial neural network (ANN), and a multi-objective parameter optimization algorithm by genetic algorithm (GA)-based lexicographic method. Local and global Pareto analyses show the trade-off between product quality and energy consumption. The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests, and at the same time, the product quality can meet the pre-determined requirements.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Abdelaziz, E.A., Saidur, R., Mekhilef, S., 2011. A review on energy saving strategies in industrial sector. Renewable and Sustainable Energy Reviews, 15(1):150-168.

[2]Alander, J.T., 1992. On Optimal Population Size of Genetic Algorithms. Proceedings of Computer Systems and Software Engineering, p.65-70.

[3]Altan, M., 2010. Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Materials and Design, 31(1):599-604.

[4]Ames, K., 1982. Saving Energy in Injection Molding Machine. Society of Plastics Engineering Annual Technical Conference, p.305-306.

[5]Carlos, C.A.C., Lamont, G.B., Van Veldhuizen, D.A., 2007. Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd Edition. Springer, New York, p.31-47.

[6]Chen, W.C., Wang, M.W., Chen, C.T., Fu, G.L., 2009. An integrated parameter optimization for MISO plastic injection molding. The International Journal of Advanced Manufacturing Technology, 44(5-6):501-511.

[7]Lafreninere, E.L., 1981. Energy Conservation in Injection Molding Machines. Society of Plastics Engineering Annual Technical Conference, p.812-813.

[8]Lin, P., 2008. Study on Injection Energy Consumption in the Injection Molding Machine Experimental System. MS Thesis, Beijing University of Chemical Technology, China (in Chinese).

[9]Marler, R.T., Arora, J.S., 2004. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6):369-395.

[10]Mattis, J., Sheng, P., DiScipio, W., Leong, K., 1996. A Framework for Analyzing Energy Efficient Injection-Molding Die Design. Proceedings of the IEEE International Symposium on Electronics and Environment, Dallas, USA, p.207-212.

[11]Mezura-Montes, E., 2009. Constraint-Handling in Evolutionary Optimization. Springer, Berlin Heidelberg, p.51-72.

[12]Mok, S.L., Kwong, C.K., 1999. Review of research in the determination of process parameters for plastic injection molding. Advances in Polymer Technology, 18(3):225-236.

[13]Mok, S.L., Kwong, C.K., Lau, W.S., 2001. A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding. The International Journal of Advanced Manufacturing Technology, 18(6):404-409.

[14]Montgomery, D.C., 2001. Design and Analysis of Experiments, 5th Edition. John Widely, New York, p.427-492.

[15]Munoz, A.A., Sheng, P., 1995. An analytical approach for determining the environmental impact of machining processes. Journal of Materials Processing Technology, 53(3-4):736-758.

[16]Nunn, R.E., Ackerman, K., 1981. Energy Efficiency in Injection Molding. Society of Plastics Engineering Annual Technical Conference, p.786-792.

[17]Ozcelik, B., Erzurumlu, T., 2006. Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. Journal of Materials Processing Technology, 171(3):437-445.

[18]Peng, X.M., Yang, Z.F., 2008. Research on the actuality and measures of technological innovation of Chinese plastic injection molding machine industry. Science and Technology Management Research, 28(7):473-474.

[19]Ruzika, S., Wiecek, M.M., 2005. Approximation methods in multiobjective programming. Journal of Optimization Theory and Applications, 126(3):473-501.

[20]Safe, M., Carballido, J., Ponzoni, I., Brignole, N., 2004. On Stopping Criteria for Genetic Algorithms. Proceedings of SBIA, Brazil, p.405-413.

[21]Shen, C.Y., Wang, L.X., Li, Q., 2007. Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. Journal of Materials Processing Technology, 183(2-3):412-418.

[22]Sheng, P., Sutanto, J., 1995. Environmental factors in parametric design of computer chassis. Journal of Electronics Manufacturing, 5(3):199-216.

[23]Srinivas, M., Patnail, L.M., 1994. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems Man and Cybernetics, 24(4):656-667.

[24]Srinivasan, K., Srinivasan, T., Maul, G.P., 1991. Improvements in Closed Loop Control of Thermoplastic Injection Molding Processes. 49th Annual Technical Conference, Montreal, Canada, p.343-345.

[25]Sun, C.H., Chen, J.H., Sheu, L.J., 2010. Quality control of the injection molding process using an EWMA predictor and minimum-variance controller. The International Journal of Advanced Manufacturing Technology, 48(1-4):63-70.

[26]Tan, K.C., Lee, T.H., Khor, E.F., 2001. Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Transactions on Evolutionary Computation, 5(6):565-588.

[27]Thiriez, A., 2006. An Environmental Analysis of Injection Molding. MS Thesis, Massachusetts Institute of Technology, Cambridge, USA.

[28]Yang, Y., Gao, F., 2006. Injection molding part weight: online prediction and control based on a nonlinear principal component regression model. Polymer Engineering & Science, 46(4):540-548.

[29]Zhang, Y., 2008. Analysis of energy-saving technique for injection molding machine (Part One). China Rubber/ Plastics Technology and Equipment, 34(3):52-60 (in Chinese).

[30]Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G., 2003. Performance assessment of multi-objective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation, 7(2):117-132.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE