Abstract
This study proposes a parameter optimization system for a multi-LED lighting lens, which uses design of experiment (DOE) for screening the process parameters, computer-aided engineering (CAE) for mold flow analysis, analysis of variance (ANOVA) for determining the significant parameters, and response surface methodology (RSM) for finding the initial parameter settings in terms of multi-objective quality characteristics. In addition, two regression models, obtained from RSM, are employed as the quality predictors which are combined with the particle swarm optimization (PSO) to generate the optimal molding parameter settings. The numerical results show that the proposed approach, RSM with PSO, is beneficial to obtain the better process parameter settings in the injection molding process.
Publisher
Trans Tech Publications, Ltd.
Reference15 articles.
1. A.H. Gandomi and A.H. Alavi: A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput Appl 21(1) (2012), pp.171-187.
2. A. Mirigul: Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Mater Des 31(1) (2010), pp.599-604.
3. B. Ozcelik and T. Erzurumlu: Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. J Mater Process Technol 171(3) (2006), pp.437-445.
4. C.F. Ng, S. Kamaruddin, A.N. Siddiquee, Z.A. Khan: Experimental Investigation on the Recycled HDPE and Optimization of Injection Moulding Process Parameters via Taguchi Method. IJMME 6(1) (2011), pp.81-91.
5. F. Shi, Z. L. Lou, Y. Q. Zhang, and J. G. Lu: Optimisation of Plastic Injection Moulding Process with Soft Computing. Int J Adv Manuf Technol 21(9) (2003), pp.656-661.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献