Processing, characterization, and wear analysis of short glass fiber-reinforced polypropylene composites filled with blast furnace slag

Blast furnace slag (BFS) is a major solid waste in iron industries that results from the fusion of fluxing stone with coke and the siliceous and aluminous residues remaining after the reduction and separation of iron from the ore. Using this BFS in different proportions (0, 10, 20, and 30wt%) in thermoplastic polypropylene matrix base, hybrid composites are prepared with and without 20wt% short glass fiber reinforcement, by injection-molding route. The composites are characterized in regard to their physical and mechanical properties and their dry sliding wear characteristics are studied experimentally. For this, a standard pin on-disk test set-up and Taguchi's orthogonal arrays are used. Taguchi's experimental design method eliminates the need for repeated experiments and thus saves time, materials, and cost. It identifies the significant control factors predominantly influencing the wear rate. From the experimental findings, optimal combinations of control factors are obtained for minimum wear rate and on that basis, predictive correlations are proposed. The morphology of worn surfaces is then examined by scanning electron microscopy and possible wear mechanisms are discussed. Furthermore, a model based on artificial neural networks (ANN) for the prediction of sliding wear properties of these thermoplastic polymer composites is implemented. The ANN prediction profiles for the characteristic wear properties exhibit very good agreement with the measured results demonstrating that a well-trained network has been created. The simulated results explaining the effect of significant process variables on the wear rate indicate that the trained neural network possesses enough generalization capability of predicting wear rate even beyond the experimental range.