Thermal stress effect on dielectric strength of rubber blends using feed-forward neural network

Abstract EN

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.