ANN based prediction of the absorption characteristics of additive manufactured glycol‐modified polyethylene terephthalate nanocomposites reinforced with short‐carbon fibers and nanoclay fillers

Abstract In this research work, an artificial neural network (ANN) based data driven prediction technique is proposed to evaluate the absorption characteristics of glycol‐modified polyethylene terephthalate (PETG) polymer nanocomposites reinforced with short carbon fibers (SCFs) and organically modified montmorillonite (OMMT) nanoclay (NC) fillers. The specimens are fabricated through additive manufacturing routine. The data points owing to the absorption of PETG nanocomposites are collected with the aid of the experimentation, which are then used to train the ANN model using Levenberg–Marquardt backward propagation algorithm. Different variants of PETG nanocomposites are prepared using various weight percentages of NC (1%, 3%, and 5%) and SCFs (5%, 10%, and 15%). The required filaments were prepared with the aid of compounding and extrusion processes. The specimens were printed using the fused deposition modeling technique. Further, the absorption behavior of these nanocomposites when exposed to distilled water, salt solution, and sugar solution has been experimentally studied following the ASTM D570 standard. The individual and combined effects of reinforcing OMMT‐NC and SCFs in the PETG matrix are also studied. The experimental results suggest that the absorption characteristics of PETG nanocomposites are significantly affected by the NC and SCFs reinforcements. The outcomes of this work will aid the design/material engineers in making a good trade‐off between the mechanical and absorption properties of the proposed PETG nanocomposites.