Simultaneous optimization of thermal and electrical conductivity of high density polyethylene-carbon particle composites by artificial neural networks and multi-objective genetic algorithm

Prediction and achievement of, simultaneously, the highest thermal and lowest electrical conductivity in polymer composites prior to their manufacturing is of great interest to avoid excessive experimentation and to achieve the best material performance for diverse applications. However, traditional modeling and optimization methods are not effective due to the non-linear complex behavior of both properties in polymer composites. In this study, two artificial neural networks (ANN) were developed with the aim of approximate the thermal conductivity and the electrical conductivity of high density polyethylene (HDPE)-carbon particle composites, based on data obtained experimentally. Composites were prepared by twin-screw extrusion using four different types of carbon particles at different concentrations. The obtained ANN models were used as objective functions in a multi-objective genetic algorithm (GA) to optimize the design parameters of the composites to maximize their thermal conductivity and minimize their electrical conductivity. ANN models showed a good correlation between simulated and experimental data, evidenced by correlation factors, R, above 0.97. Multi-layer perceptron ANN with three neurons in a single hidden layer and trained by the Levenberg-Marquardt algorithm exhibited the best predictive performance in both models. As a result of the multi-objective optimization process by GA, a set of Pareto optimal solutions for maximizing thermal conductivity and minimizing electrical conductivity was obtained. Conformity tests were performed to validate the optimization capability of the GA method. The optimization and modeling procedure developed can be applied to other properties of polymer composites.