Preparation and Process Optimization of LLDPE/EVA/Modified Nano-ZnO Composites

Abstract:

The mechanism and method were deduced on surface modification of nano-ZnO by titanate coupling agent. EVADPE/ZnO nanocomposite material was prepared, and the tensile strength was tested. Three relatively optimal groups of process parameters were obtained by orthogonal experiment, BP-GA model and BP-PSO model respectively, in which the parameter obtained through BP-GA model was equal to that obtained through BP-PSO model. The iterative times of BP-PSO algorithm were smaller than that of BP-PSO algorithm. With analysis, it was showed that the group of process parameters obtained by BP-GA model and BP-PSO model based on the orthogonal experiment data were much better than that group obtained by orthogonal experiment. The optimal velocity of BP-PSO algorithm was faster than that of BP-PSO model.

• Periodical: Advanced Materials Research (Volumes 236-238)
• Pages: 1803-1806
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.236-238.1803
• Online since: May 2011
• Authors: Jia Liu, Shun Xiang Li
• Keywords: BP-GA Model, BP-PSO Model, Nano-Composite Material, Orthogonal Experiment, Process Optimisation

References

[1] Jihui He, Me Shui, Jianqing Zhao. Polymers and Polymer Composites, 14(6): 611-622. (2006)

[2] Y Q Huang, S L Jiang, L B Wu, et al. Polymer Testing, 23:9-15. (2004)

[3] YQ Hua, YQ Zhang, LB Wu, et al. Journal of Macromolecular Science Physics, 44(2):149-159. (2005)

[4] Shouren Hu: Introduction to Neural Networks (National Defense University Press, Beijing 1993).

[5] Eberhart R Kennedy J. Proc of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan: lEEE Press, 39-43. (1995)

[6] Funahashi KI. Neural Networks, 2(3):183-192. (1989)

[7] Fei Sike Technology R & D center: MATLAB 6.5 Auxiliary Neural Network Analysis and Design (Electronic Industry Press, Beijing 2003).