Artificial neural network coupled experimental investigation of tribological behavior of additive manufactured PETG/OMMT nanocomposites

Abstract

The advent of additive manufacturing has brought a paradigm shift in many engineering applications involving polymer composites. The current study aims at integrating artificial neural network (ANN) with experimentation to assess the wear properties of glycol‐modified polyethylene terephthalate (PETG) composites reinforced with organically modified montmorillonite (OMMT) nanoclay (1%, 3%, and 5% weight percentages). The specimens are fabricated using fused deposition modeling technology. The proposed composites are compounded and developed using a single screw extruder with a diameter of 1.75 mm. As per ASTM G99 standard, the tribological characteristics were investigated using sliding wear with the dry pin on disc arrangement. The data points owing to the wear properties of PETG/OMMT nanocomposites are collected with the aid of the experimentation, which is then used to train the ANN model using Levenberg– Marquardt backward propagation algorithm. The experimental results show that adding OMMT nanoclay at a 3% weight percentage improves the wear properties of the composites compared to virgin PETG. A scanning electron microscope study of the wear mechanism reveals that adding OMMT nanoclay to the proposed composites results in mild wear as opposed to heavy wear in the case of virgin PETG. The ANN model developed to predict the tribological performance of PETG/OMMT facilitates better predictability with 99.6% accuracy, thereby reducing the requirement of expensive experimentation and analysis. The proposed composites may be applicable in the prosthetic, aerospace and automobile industries.