Abstract
Referring to water absorption rate of poly (methyl methacrylate) (PMMA) and its composites is hard to obtain under some working conditions, BP neural network prediction model was constructed. Regarding water absorption rate predictions of exfoliated PMMA/MMT nanocomposites in 0.1 mol/L H2SO4 solution, 0.1 mol/L NaOH solution and deionized water respectively as examples, the applicability of model established in water absorption rate prediction of PMMA and its composites was researched. The results show that the relative errors between prediction value obtained from model established and actual value of water absorption rate of composites soaking 63min in three kinds of mediums are 1.50%, 0.47% and 1.04% respectively, prediction accuracy is higher than that (relative errors are 3.89%, 3.40% and 4.43% respectively) obtained from GM (1, 1) model obviously. BP neural network can be used to predict water absorption rate of PMMA and its composites.
Highlights
(methyl methacrylate) (PMMA), called organic glass, has excellent transparency, weather resistance, electrical insulation and processability
The existing of polar side-chain methyl leads to the fact that water can be absorbed by PMMA and its products [1], which may cause performances decline and even lead to deformation in severe cases, and affects safe use of PMMA and its products seriously [2]
Taking the water absorption rate predictions of PMMA/MMT nanocomposites soaked in acid, base and deionized water as examples, the applicability of the BP neural network prediction model established above in water absorption rate prediction of PMMA and its composites was researched
Summary
(methyl methacrylate) (PMMA), called organic glass, has excellent transparency, weather resistance, electrical insulation and processability. The data collection of water absorption rate is an important work before the application of PMMA and its products under some special conditions. This job can be done through experiments under artificial accelerated or actual working conditions. According to experimental data related, forecasting properties of materials through mathematical model is one of the focus of attention and study [4]. Taking the water absorption rate prediction of Poly (methyl methacrylate)/montmorillonite (PMMA/MMT) nanocomposites as an example, a BP (Back propagation) neural network approach was employed to predict the water absorption rate of PMMA and its composites in the presented paper. The prediction effect of the BP neural network model was compared to that obtained by the GM (1, 1) model
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