Enhanced Erosion Wear Performance of Talc Particulate–Filled Glass Fiber–Reinforced Nylon66 Composites: Experimental Analysis and Machine Learning Based Prediction

In this study nylon 66 (N66), glass fiber–reinforced nylon 66 (GFN66), and talc particulate–filled glass fiber–reinforced nylon 66 (T-GFN66) hybrid composites were fabricated using compounding process and injection moulding technique. Test was performed by varying the input factors (impact velocity, impingement angle, and constitute of composite). Study intended to explore the combined effect of input factors on erosion wear rate (EWR) of the composites. Data-driven machine learning (ML) approach was applied to analyse and predict the EWR of the N66 and its hybrid composites. Experimental results showed that EWR increased with increase in impact velocity and decreased with an addition of glass fiber and talc filler contents. T-GFN66 composite exhibited superior erosion wear resistance than N66 and GFN66. Furthermore, experimental data were fed into the four ML models and compared using their performance metrics. It was observed that among all the developed ML models, gradient boosting machine (GBM) model found to be superior in predicting the erosion wear performance of N66 composite with R2 value of 0.9666. Eroded surface topography was analysed using 3D optical profilometer to establish the relationship between surface parameters and EWR. Worn morphology was conducted using field emission scanning electron microscopy (FESEM) to observe wear mechanisms endured by the N66 composites.