Data‐driven modeling for predicting tribo‐performance of graphene‐incorporated glass‐fabric reinforced epoxy composites using machine learning algorithms

Abstract

The present article aims to investigate a comparative effect of the mechanical properties and tribological operating variables (applied load and sliding distance) on the tribo‐performance of graphene incorporated woven glass fabric reinforced epoxy (GFRE) composites. Computational and data‐driven machine learning (ML) approach has been extensively applied to examine the advancement of the tribological systems. For the study, tribo‐mechanical data, gathered from previous investigations by the present authors, have been used as well. Accordingly, a predictive model, based on the ML algorithms for predicting specific wear rates (SWR), has been developed. Further, a co‐relation between the SWR and the mechanical properties of materials has been determined. For the investigation process, three ML approaches, have been applied, viz., artificial neural network (ANN), Random Forest (RF), and Gradient Boosting Machine (GBM). Feature score analysis has been done as well revealing that variation of the sliding distance, interlaminar shear strength (ILSS) and product of tensile strength (s) and elongation (e) significantly influence the SWR prediction. The trained ML models can predict the tribo‐performance from mechanical properties variables and tribological test conditions, which is impossible with conventional two‐parameter correlations. During the present study, the coefficient of determination (R2 value) in different models was found to be 0.9883, 0.9884, and 0.9762 for the ANN, RF, and GBM, respectively. The best‐performing model was RF.

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“…The erosion wear analysis and prediction of any hybrid material system are difficult and complex because the erosion rate of these systems depends on different variables such as impact angle and striking velocity of the erodent particles, feed rate, erodent temperature, reinforcing material, weight of reinforcement, and so on 33 . These variables influence the erosion rate of the composites either individually or in interaction with one another adding complexity to the problem and therefore it requires a lot of computational and experimental exercise for the analysis leading to high experimental cost and time 34 . Nevertheless, it is significant to predict the wear performance of any functional composite which will help in saving the failure and maintenance cost in the actual workplace.…”

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