A Two-Stage Optimization System for the Plastic Injection Molding with Multiple Performance Characteristics

Abstract:

This study proposes a two-stage optimization system to generate the optimal process parameter settings of multi-quality characteristics in the plastic injection molding (PIM) products. In the first stage, Taguchi orthogonal array was employ to arrange the experimental work and to calculate the S/N ratio to determine the initial process parameter settings. Then, S/N ratio predictor and S/N quality predictor was constructed by employed the back-propagation neural network (BPNN). In addition, S/N ratio predictor was along with simulated annealing (SA) used to search for the first optimal parameter combination in order to reduce the PIM process variance. In the second stage, BPNN quality predictor and particle swarm optimization (PSO) was intended to find the optimal parameter settings for the best quality specification. Results from the experimental work show that the proposed two-stage optimization system can create the best process parameter settings which not only meet the quality specification, but also effectively reduce cost.

• Periodical: Advanced Materials Research (Volumes 468-471)
• Pages: 386-390
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.468-471.386
• Online since: February 2012
• Authors: Wen Chin Chen, G.L. Fu, Denni Kurniawan
• Keywords: BP Neural Network (BPNN), Particle Swarm Optimization Algorithm (PSO), Plastic Injection Molding, Simulated Annealing (SA), Taguchi Method

References

[1] J.F. Reilly, M. Doyle and D. Kazmer. An assessment of dynamic feed control in modular tooling. J. Inject. Mold. Technol. Vol. 5(1) (2001) 49-59.

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

DOI: 10.1016/j.jmatprotec.2005.04.120

[3] A. Mirigul. Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Materials &amp Design. Vol. 31(1) (2010) 599-604.

DOI: 10.1016/j.matdes.2009.06.049

[4] C.T. Su and H.H. Chang. Optimization of parameter design: an intelligent approach using neural network and simulated annealing. International Journal of System Science. Vol. 31(12)(2000) 1543-1549.

DOI: 10.1080/00207720050217313

[5] W.C. Chen, G.L. Fu, P.H. Tai and W.J. Deng. Process parameter optimization for MIMO plastic injection molding via soft computing. Expert System with Applications. Vol. 36(2)(2009) 1114-1122.

DOI: 10.1016/j.eswa.2007.10.020

[6] F.J. Shiou and C.H. Chen. Freeform surface finish of a plastic injection mold by using ball-burnishing process. International Journal of Materials Processing Technology. Vol. 140 (2003) 248-254.

DOI: 10.1016/s0924-0136(03)00750-7

[7] J.R. Shie. Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network. International Journal of Advanced manufacturing Technology Vol. 36 (2008) 1091-1103.

DOI: 10.1007/s00170-007-0940-0