Abstract: Basalt fber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic fbers. Basalt fber strengthened vinyl ester matrix polymeric composite with fller addition of nano- and micro-sized silicon carbide (SiC) element spanning from 2 weight percent to 10 weight percent was studied for its mechanical and wear properties. The application of Artifcial Neural Network (ANN) to correlate the fller addition composition for optimum mechanical properties is required due to the non-linear mechanical and tribological features of composites. The stufng blend and composition of the composite are optimized using the hybrid model and Genetic Algorithm (GA) to maximize the mechanical and wear-resistant properties. The predicted and tested ANN–GA optimal values obtained for the composite combination had a tensile, fexural, impact resilience, hardness and wear properties of 202.93 MPa, 501.67 MPa, 3.460 J/s, 43 HV and 0.196 g, respectively, for its optimum combination of fller and reinforcement. It can be noted that the nano-sized SiC fller particle enhances most of the properties of the composite which diversifes its applications. The predicted mechanical and wear values of the developed ANN–GA model were in closer agreement with the experimental values which validate the model.

Key words: Hybrid polymer composite , · Prediction , · Process modelling , · Artifcial neural networks , · Genetic algorithm