| Summary: | 碩士 === 國立勤益科技大學 === 化工與材料工程系 === 102 === This study discusses the process optimization of epoxy nanocomposites, epoxy/glass fiber nanocomposites, and epoxy/carbon fiber nanocomposites.
A prediction model for properties of composite materials reinforced with nanoparticles was developed using Taguchi’s method and regression analysis technique. Four main factors including nano-silica weight ratio concentration, alumina weight ratio, carbon black weight ratio, and diluent weight ratio each at three levels were considered for designing. Then we analyzed the optimization through a two-stage simulated annealing method. Finally, the sensitivity analysis was used to explore the influence of various parameters on quality characteristics.
The results show that the sensitive parameter for the thermal decomposition temperature of the epoxy/glass fiber and epoxy/carbon fiber nanocomposites is alumina powder content, nanocomposite is silica powder content. The sensitive parameter for the glass temperature of the epoxy/glass fiber is carbon black powder content, epoxy/carbon fiber nanocomposites and nanocomposite is silica powder content. The sensitive parameters for the thermal expansion coefficient (α1) of the epoxy/glass fiber and epoxy/carbon fiber nanocomposites is carbon black powder content, nanocomposite is alumina powder content. The sensitive parameter for the thermal expansion coefficient (α2) of the epoxy/glass fiber nanocomposites is carbon black powder content, the epoxy/carbon fiber nanocomposites is carbon black powder content, the nanocomposites is alumina powder content. The sensitive parameters for Shore D hardness of the epoxy/glass fiber nanocomposites is silica powder content, the epoxy/carbon fiber composites is carbon black powder content, the nanocomposite is silica powder content. The various mechanical and thermal properties of the material are dominated by different compositional elements.
Overall, the two-stage simulated annealing and sensitivity analysis methods can help identify the content of the most effective factor, efficiently reduce the number of experiments and improve the quality characteristics, and reaches to enhance effectiveness.
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