Dry-sliding wear properties of 3D printed PETG/SCF/OMMT nanocomposites: Experimentation and model predictions using artificial neural network

In this article, an attempt has been made to experimentally investigate the synergistic effect of organically modified montmorillonite (OMMT) nanoclay and short carbon fibers (SCFs) on tribological behaviour additively manufactured Polyethylene Terephthalate Glycol (PETG) based nanocomposites. The tribo-specimens are 3D printed using fused deposition modelling (FDM). properties, that is, specific wear rate (SWR) coefficient friction (CoF) various PETG/SCF/OMMT nanocomposites were assessed by performing dry-sliding test. addition, artificial neural network (ANN) methodology is proposed accurately predict performance PETG ANN model trained datasets obtained from experimentation. For training model, Levenberg–Marquardt optimisation algorithm with 10 neurons along a tangent sigmoid activation function utilised. Additional experimentation was performed for arbitrary load sliding velocity which not used results compared assess predictive capability unseen data. predicted SWR CoF agreeable accuracy. It believed adopting methodology, costs time can be significantly reduced without compromising accuracy results.