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

This study explores the effects of four independent variables—the nanoclay weight percentage, sliding velocity, load, and distance—on wear rate frictional force nanoclay-filled FormuLITETM amine-cured bio-based epoxy composites. An experimental design based on Taguchi method revealed diverging optimal conditions for minimizing 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) Energy-dispersive X-ray Spectroscopy (EDS), illustrated homogeneous distribution within FormuliteTM matrix, which is crucial enhancing load transfer stress distribution. Atomic Force (AFM) analysis indicated that incorporation increases surface roughness peak height, are important determinants material performance. However, an increase percentage decreased these attributes, suggesting interaction saturation point. Due to their augmented mechanical properties, present underscores potential systems diverse applications, such as automotive, aerospace, biomedical engineering.