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Evaluation of dielectric strength of SiR/TiO(2) composites using feed-forward neural network
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Evaluation of dielectric strength of SiR/TiO(2) composites using feed-forward neural network
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Evaluation of dielectric strength of SiR/TiO(2) composites using feed-forward neural network
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Journal Article
Evaluation of dielectric strength of SiR/TiO(2) composites using feed-forward neural network
2020
Overview
Among the recently insulating materials broadly utilized in high voltage outdoor insulation, silicone rubber (SiR) has gotten the foremost consideration. Actually, SiR is becoming an efficient countermeasure to insulator contamination issues. To enhance different properties of polymeric materials, micro- and nanofillers have been used for dielectric applications. In this study, micron-sized titanium dioxide (TiO(2)) and nano-sized TiO(2) fillers were added to the SiR matrix to improve electrical and mechanical properties. Dielectric strength, tensile strength, and elongation at break tests were monitored. Also, a scanning electron microscope was carried out. The samples were prepared by mixing micro-TiO(2) into SiR with the content of 0, 10, 20, 30, and 40 wt% and also mixing nano-TiO(2) into SiR with the content of 0, 1, 3, 5, and 7 wt%. A feed-forward neural network technique was used to estimate the dielectric strength in different conditions and different percentages of fillers. Adding nano TiO(2) filler enhances the electrical and mechanical properties of SiR composites. SiR with 5 wt% nano TiO(2) showed the best improvement in electrical and mechanical properties.
