DocumentCode
2662092
Title
Notice of Retraction
Optimization of injection molding process parameters based on Response Surface Methodology and genetic algorithm
Author
Baoshou Sun ; Zhenfan Wu ; Boqin Gu ; Xiaodiao Huang
Author_Institution
Fac. of Mech. Eng. & Mech., Ningbo Univ., Ningbo, China
Volume
5
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, a number of injection molding CAE simulations were carried out according to the Latin Square orthogonal array by utilizing the method of Design of Experiment (DOE). These experimental data were used to build a surrogate model to identify the relationship between the injection molding process parameters and warpage using the Response Surface Methodology (RSM), with proper validation of the model accuracy. By using the surrogate model, the RSM and genetic algorithm (GA) were combined to find the optimal injection molding processing parameters. The results show that the developed surrogate model is accurate and reliable, and the optimization efficiency is largely improved by applying the RSM method, hence the combination of RSM and GA proposed in this paper is useful for the optimization of injection molding process parameters and for minimizing the molding warpage.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, a number of injection molding CAE simulations were carried out according to the Latin Square orthogonal array by utilizing the method of Design of Experiment (DOE). These experimental data were used to build a surrogate model to identify the relationship between the injection molding process parameters and warpage using the Response Surface Methodology (RSM), with proper validation of the model accuracy. By using the surrogate model, the RSM and genetic algorithm (GA) were combined to find the optimal injection molding processing parameters. The results show that the developed surrogate model is accurate and reliable, and the optimization efficiency is largely improved by applying the RSM method, hence the combination of RSM and GA proposed in this paper is useful for the optimization of injection molding process parameters and for minimizing the molding warpage.
Keywords
computer aided engineering; design of experiments; genetic algorithms; injection moulding; production engineering computing; response surface methodology; CAE simulations; RSM method; design of experiment; genetic algorithm; injection molding process parameters; latin square orthogonal array; optimization; response surface methodology; Computer aided engineering; Design for experiments; Genetic algorithms; Injection molding; Optimization methods; Plastics; Response surface methodology; Surface fitting; Temperature; US Department of Energy; Injection modeling; Processing parameter optimization; Response Surface Methodology; Warpage;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5486093
Filename
5486093
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