Design of Optimization Parameters with Hybrid Genetic Algorithm Method in Multi-Cavity Injection Molding Process

Abstract:

This paper combines an artificial neural network (ANN) with a traditional genetic algorithm (GA) method, called hybrid genetic algorithm (HGA), to analyze the warpage of multi-cavity plastic injection molding parts. Simulation results indicate that the minimum and the maximum warpage of the hybrid genetic algorithm (HGA) method were lower than that of the traditional GA method and CAE simulation. These results reveal that, when HGA is applied to multi-cavity plastic warpage analysis, the optimal process conditions are significantly better than those using the traditional GA method or CAE simulation.

• Periodical: Advanced Materials Research (Volumes 463-464)
• Pages: 587-591
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.463-464.587
• Online since: February 2012
• Authors: Wen Jong Chen, Jia Ru Lin
• Keywords: Artificial Neural Network (ANN), Traditional Genetic Algorithm, Warpage

References

[1] B.H.M. Sadeghi (2000). A BP-neural network predictor model for plastic injection molding process. Journal of Materials Process Technology, Vol. 103, 411-416.

DOI: 10.1016/s0924-0136(00)00498-2

[2] Wen-sheng, Jiang; Zu-gao, pang; Wei, Xia; Ping, Xiao (2007). Optimization of process parameters for thin plastic shell injection molding based on neural network and genetic algorithm, Mondern Manufacturing Engineernign, Vol. 1, 60-62.

[3] Wen-Chin Chen; Gong-Loung Fu; Pei-Hao Tai and Wei-Jaw Deng (2009). Process parameter optimization for MIMO plastic injection molding via soft computing, Expert Systems with Applications, Vol. 36, No. 2, 1114-1122.

DOI: 10.1016/j.eswa.2007.10.020

[4] Hamdy Hassan, Nicolas Regnier, Cyril Pujos, Eric Arquis, Guy Defaye. Modeling the effect of cooling system on the shrinkage and temperature of the polymer by injection molding, Applied Thermal Engineering, Vol 30, No. 13, 1547-1557.

DOI: 10.1016/j.applthermaleng.2010.02.025

[5] 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 Process Technology, vol. 171, 437-445.

DOI: 10.1016/j.jmatprotec.2005.04.120

[6] Kurtaran, H.; Ozcelik, B. (2005). Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm, Journal of Materials Process Technology, Vol. 169, 314–319.

DOI: 10.1016/j.jmatprotec.2005.03.013

[7] Chien-Yu Huang, Long-Hui Chen, Yueh-Li Chen and Fengming M. Chang, Evaluating the process of a genetic algorithm to improve the back-propagation network: A Monte Carlo study, Expert Systems with ApplicationsVolume 36, Issue 2, Part 1, March 2009, Pages 1459-1465. ).

DOI: 10.1016/j.eswa.2007.11.055

[8] Tai-Yue Wang and Chien-Yu Huang Optimizing back-propagation networks via a calibrated heuristic algorithm with an orthogonal array, Expert Systems with Applications, Volume 34, Issue 3, April 2008, Pages 1630-1641. ).

DOI: 10.1016/j.eswa.2007.01.013

[9] Rob Law，Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting Tourism Management, Volume 21, Issue 4, August 2000, Pages 331-340).

DOI: 10.1016/s0261-5177(99)00067-9

[10] Matin T. Hagan, Howard B. Demuth and Mark Beale, "Neural Network Design, Inc. 978).