Processing and Wear Analysis of Blast Furnace Slag Filled Polypropylene Composites Using Taguchi Model and ANN

Abstract

This paper analyses and reports on processing and solid particle erosion wear response of a new class of hybrid composites prepared by reinforcement of short glass fibers (SGF) in blast furnace slag (BFS) filled polypropylene (PP) matrix. In this investigation, composites with different BFS content (0, 10, 20 and 30 wt%) in a polypropylene matrix base, with and without 20 wt% SGF reinforcement, are prepared by injection molding route. To study the erosion wear response of these BFS filled composites, a plan of experiments based on the Taguchi technique is followed to acquire the wear data in a controlled way. This systematic experimentation has led to identification of significant process parameters and material variables that predominantly influence the erosion rate and also enables us to determine optimal parameter settings that lead to minimization of the erosion rate. An artificial neural network (ANN) approach is also implemented to predict the wear rate of the composites. The morphology of eroded surfaces is then examined by scanning electron microscopy (SEM) and possible erosion mechanisms are discussed. This study reveals that addition of blast furnace slag improves the erosion resistance of glass-polypropylene composites significantly and thus makes them suitable for tribological applications.