Analysis and prediction of erosion behavior of epoxy composites using statistical and machine learning techniques

Abstract

This work reports on the application of different machine learning (ML) techniques and statistical methods to analyze and predict the erosion wear performance of ramie fiber-reinforced epoxy composites. These composites, fabricated by conventional hand layup technique, are subjected to high-temperature erosion wear following a test schedule based on the design-of-experiments. The experimental database is used to determine the relative significance and contribution of individual parameter on the erosion wear rate using the analysis of variance. It suggests the impact velocity as the most significant factor contributing 81.2%, followed by impingement angle (13.56%), erodent temperature (0.06%) and stand-off distance (0.05%) to the wear rate. The data generated from experimentation are further processed to predict the erosion performance of the composites with a ML approach following four different algorithms such as support vector machine, decision tree, random forest and gradient boosting machine (GBM). The feature scores for different parameters in the above models are also evaluated to ascertain the contribution of each parameter, which consequently revealed that impact velocity and impingement angle with respective mean feature scores of 0.7538 and 0.1824 have maximum influence on the erosion rate. The performances of four different ML models are compared on the basis of their coefficient of determination (R2 value). It is found that the GBM outperforms other models with a R2 value as high as 0.9543 and has thus emerged as the best-performing prediction model. The possible mechanisms causing the wear loss are identified using electron microscopy.