期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2015
卷号：4
期号：2
页码：579
DOI：10.15680/IJIRSET.2015.0402068
出版社：S&S Publications
摘要：Prediction and optimization of polymer properties and polymer composite properties are a complex andhighly non-linear problem with no any easy method to predict polymer properties directly and accurately. The effect ofmodifying a monomer (polymer repeat unit) on polymerization and the resulting polymer properties is not an easy taskto investigate experimentally, given the large number of possible changes. We utilize a database of polymer propertiesto train the ANFIS, which accurately predict specific polymer properties. In polymer composites, a certain amount ofexperimental results is required to train a well-designed ANFIS. The ANFIS approach for predicting certain propertiesof polymer composite materials are discussed here. These include fatigue life; wear performance, response undercombined loading situations, and dynamic mechanical properties. Prediction of effective thermal conductivity (ETC) ofdifferent fillers filled in polymer matrixes is proposed. The finding shows that ANFIS demonstrates high predictionaccuracy as reflected by the small root mean square error (RMSE) value and high correlation coefficient (r) andcoefficient of determination (R2) values. ANFIS prediction results are found to be compatible to linear regressionestimations. The goal of this paper is to promote more consideration of using ANFIS in the field of polymer compositeproperty prediction and design. The predicted results by ANFIS are in good agreements with experimental values. Thepredicted results also show the supremacy of ANFIS in comparison with other earlier developed models.
