ANFIS Prediction of the Polymer and Polymer Composite Properties and Its Optimization Technique
Prediction and optimization of polymer properties and polymer composite properties are a complex and highly non-linear problem with no any easy method to predict polymer properties directly and accurately. The effect of modifying a monomer (polymer repeat unit) on polymerization and the resulting polymer properties is not an easy task to investigate experimentally, given the large number of possible changes. We utilize a database of polymer properties to train the ANFIS, which accurately predict specific polymer properties. In polymer composites, a certain amount of experimental results is required to train a well-designed ANFIS. The ANFIS approach for predicting certain properties of polymer composite materials are discussed here. These include fatigue life; wear performance, response under combined loading situations, and dynamic mechanical properties. Prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes is proposed. The finding shows that ANFIS demonstrates high prediction accuracy as reflected by the small root mean square error (RMSE) value and high correlation coefficient (r) and coefficient of determination (R2) values. ANFIS prediction results are found to be compatible to linear regression estimations. The goal of this paper is to promote more consideration of using ANFIS in the field of polymer composite property prediction and design. The predicted results by ANFIS are in good agreements with experimental values. The predicted results also show the supremacy of ANFIS in comparison with other earlier developed models.