Evaluation of dielectric strength of SiR/TiO
            <sub>2</sub>
            composites using feed‐forward neural network

Qenawy, Shaymaa A.; Nasrat, Loai; Ismail, Hanafy M.; Asaad, J. N.

doi:10.1049/iet-nde.2020.0001

Cited by 7 publications

(5 citation statements)

References 16 publications

(16 reference statements)

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“…With development of electrical and electronic technologies, some polymer materials with excellent dielectric properties such as relatively high permittivity, low dielectric loss and high breakdown field at the frequencies and voltages of operation would be useful. [24,[113][114][115][116][117][118][119][120] Commonly, a high dielectric constant material is defined as a material with a dielectric constant higher than that of the silicon oxide (k 4.0). Unfortunately, polymer materials typically possess low dielectric constants as shown in Table 1, though they have a high breakdown strength.…”

Section: Pure High-k Polymersmentioning

confidence: 99%

Polymer-based dielectrics with high permittivity for electric energy storage: A review

et al. 2021

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Section: Pure High-k Polymersmentioning

confidence: 99%

Polymer-based dielectrics with high permittivity for electric energy storage: A review

et al. 2021

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“…Experimental results are used for ANN modeling and the different condition and blends ratio of the samples were determined as input parameters for ANN modeling. The feed forward neural network (FFNN) method is used to forecast the dielectric strength of untested samples for intermediate values [12]. We have a clear picture of the breakdown voltage with change in the different conditions of the EPDM/SiR mixture from previous laboratory experiments, and we have provided the experimental data used to feed the neural network with the necessary information about the behavior of the process's input and output during the learning phase.…”

Section: Ann Simulation and Propertiesmentioning

confidence: 99%

Evaluation of the Dielectric Strength Behavior of Rubber Blends Using Feed-Forward Neural Network in Different Environmental Conditions

Abdalla

Nasrat²,

Mansour³

et al. 2022

Journal of Al-Azhar University Engineering Sector

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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.

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“…The feed forward neural network (FFNN) technique is used to predict the dielectric strength for the intermediate values of untested samples [9].From previous laboratory experiments, we have a clear picture of the breakdown voltage with the change in the different temperatures of the EPDM/SiR mixture and provide the experimental data used to feed the neural network, during the learning phase, with the necessary information about the behavior of the input and output of the process. This stored behavior is used by the neural network as a reference that is retrieved during the operation phase as the network models the actual operation of specific test conditions.…”

Section: Ann Simulation and Propertiesmentioning

confidence: 99%

Thermal Stress Effect on Dielectric Strength of Rubber Blends Using Feed-Forward Neural Network

Abdalla

Nasrat

Mansour

et al. 2021

Journal of Al-Azhar University Engineering Sector

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Because of the Variety in characteristics, strength, economy, and ease of manufacturing, the rubber blends are very suitable for use in the field of insulating materials. The blending technology has effects on the chemical, physical, mechanical, and electrical properties of polymers; this effect is predominately convenient for electrical insulation purposes. Electrical systems are often subject to faults resulting from short circuits or any other cause, which naturally leads to an increase in temperature for Insulating materials; And from here required considering good electrical properties and additional to have desired mechanical properties for insulation and bear it for different temperatures. The effect of thermal stress on the blending ratio of ethylene propylene diene monomer (EPDM) and silicone rubber (SiR) at various temperatures is studied using the Feed-Forward Neural Network (FFNN) after laboratory testing in this paper. The five different samples of EPDM-SiR blends (100/0; 75/25; -50/50; 25/75; 0/100) were prepared. The Breakdown Voltage (BDV) was measured under various temperatures (25, 60,100 and 130°C) according to ASTM standards. The experimental data was used to train the FFNN model. The blends ratio and temperatures represent the input of the FFNN system while the breakdown voltage kV is the output. The outputs obtained from FFNN were compared and checked against the data obtained in the laboratory. This study indicates that FFNN can be trusted to simulate the effect of thermal stress of various blending ratio on breakdown voltage with a satisfactory rate. It also demonstrates that the FFNN approach is an active tool that can be adopted as a reference to reduce the time and cost required in preparing and testing samples in the experimenter.

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Evaluation of dielectric strength of SiR/TiO ₂ composites using feed‐forward neural network

Cited by 7 publications

References 16 publications

Polymer-based dielectrics with high permittivity for electric energy storage: A review

Polymer-based dielectrics with high permittivity for electric energy storage: A review

Evaluation of the Dielectric Strength Behavior of Rubber Blends Using Feed-Forward Neural Network in Different Environmental Conditions

Thermal Stress Effect on Dielectric Strength of Rubber Blends Using Feed-Forward Neural Network

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Evaluation of dielectric strength of SiR/TiO 2 composites using feed‐forward neural network

Cited by 7 publications

References 16 publications

Polymer-based dielectrics with high permittivity for electric energy storage: A review

Polymer-based dielectrics with high permittivity for electric energy storage: A review

Evaluation of the Dielectric Strength Behavior of Rubber Blends Using Feed-Forward Neural Network in Different Environmental Conditions

Thermal Stress Effect on Dielectric Strength of Rubber Blends Using Feed-Forward Neural Network

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Evaluation of dielectric strength of SiR/TiO ₂ composites using feed‐forward neural network