Particle swarm optimization based selection of optimal polymeric blend

This paper presents a novel and efficient method to estimate the optimal blend of Silicone Rubber (SiR) and Ethylene Propylene Diene Monomer (EPDM). The SiR and EPDM are widely used insulating materials. Blending of these polymers is one way of obtaining a new material with superior properties of both. Hence blending of SiR/EPDM with different blend ratios has been done. These blends are tested for their electrical and mechanical characteristics as per ASTM and IEC standards. The mechanical properties investigated are tensile strength (TS) and elongation at break (EB). The electrical properties like volume resistivity (VRY) and surface resistivity (SRY), arc resistance (AR) and comparative tracking index (CTI) are also analyzed. Amidst various electro-mechanical parameters, it is really hard to choose a suitable blend ratio for a specific application. In order to identify the suitable blend with superior properties compared to that of the constituent polymers, optimization technique is used. The problem of determining the optimal blend is formulated as a multiobjective optimization problem with the objective of maximizing the electrical and mechanical properties. The problem of finding the optimal blend ratio for improved performance is based on the weights assigned for various electrical and mechanical parameters. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. In this paper, the PSO is used to find the suitable blend ratio for cable applications. A comparison has been made with Genetic Algorithm (GA) technique, in order to validate the result. To have an overview about the thermal behavior of these blends under a practical situation, thermal analysis using Differential Scanning Calorimetry (DSC) and Thermo-gravimetric Analysis (TGA) has been done.