Erosion wear response of ramie‐epoxy composites using artificial neural network integrated with Taguchi technique

Abstract

This paper aims on evaluating the erosion wear behavior of epoxy composites reinforced with ramie fibers. The possibility of reinforcing ramie fiber to improvise the wear resistance of epoxy is investigated in this study. Composites are fabricated by reinforcing multiple layers of woven ramie fiber mats into epoxy resin using conventional wet lay‐up technique and erosion wear trials are conducted using solid particle erosion test setup. Taguchi analysis is done to assess the relative significance of each of the factors influencing the erosion rate using L16 orthogonal array. The analysis reveals that the impact velocity followed by impingement angle are the most significant control factors affecting the erosion wear rate of ramie‐epoxy composites. Steady state erosion analysis is done to ascertain the effect of each of the significant factors while keeping other factors fixed. Further, an analytical and predictive model based on the principle of neural computation is used to predict the rate of erosion wear of the composites and the obtained results are compared with the experimental outcomes. The worn morphologies of the eroded surfaces of the composites are studied and analyzed to identify possible mechanisms causing wear.

Development of machine learning models for the prediction of erosion wear of hybrid composites

“…8,9 It is evident from the reported literature that the use of natural fibers in polymers enhances the strength but decreases the erosion resistance of the composites. [10][11][12][13][14][15] This problem can be solved by adding hard particulate fillers in fiber reinforced polymers resulting in enhancement of their wear properties. In this context, the erosion behavior of composites containing natural fibers and conventional ceramics has been reported in earlier investigations.…”