EVALUATION OF THE DIELECTRIC STRENGTH BEHAVIOR OF RUBBER BLENDS USING FEED-FORWARD NEURAL NETWORK IN DIFFERENT ENVIRONMENTAL CONDITIONS

Abstract

Polymers have been frequently employed in electrical applications because of their strong thermal and electrical insulating qualities, low density, and chemical resistance. In this study, a comparison between the behaviour and electrical properties of polymer blends and the results of artificial neural network (ANN) modelling has been conducted. Five samples of silicon rubber (SiR) and ethylene propylene diene monomer (EPDM) were prepared in different proportions. A dielectric test was used to test the dielectric performance of insulation samples under various polluting conditions such as dry, wet, low salinity, and high salinity wet according to ASTM standards. Percentage of blend and dielectric strength were used by ANN modelling for varying ambient conditions. The observations on ANN results and the experimental results have shown sufficient accuracy mutually. The artificial intelligence modelling studies for this article prove the applicability of the behavioural and electrical properties of EPDM/SiR blends. These findings indicate that artificial neural networks can be a useful tool for conducting experiments on the behaviour and electrical properties of polymer materials.