Statistical and Artificial Neural Network Coupled Technique for Prediction of Tribo-Performance in Amine-Cured Bio-Based Epoxy/MMT Nanocomposites

Abstract

This study explores the effects of four independent variables—the nanoclay weight percentage, sliding velocity, load, and sliding distance—on the wear rate and frictional force of nanoclay-filled FormuLITETM amine-cured bio-based epoxy composites. An experimental design based on the Taguchi method revealed diverging optimal conditions for minimizing the wear and frictional force. These observations were further validated using a Back-propagation Artificial Neural Network (BPANN) model, demonstrating its proficiency in predicting complex system behavior. Material characterization, conducted through Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray Spectroscopy (EDS), illustrated the homogeneous distribution of the nanoclay within the FormuliteTM matrix, which is crucial for enhancing the load transfer and stress distribution. Atomic Force Microscopy (AFM) analysis indicated that the incorporation of nanoclay increases the surface roughness and peak height, which are important determinants of the material performance. However, an increase in the nanoclay percentage decreased these attributes, suggesting an interaction saturation point. Due to their augmented mechanical properties, the present study underscores the potential of amine-cured bio-based epoxy systems in diverse applications, such as automotive, aerospace, and biomedical engineering.