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

Abstract

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