Acoustic absorption of 3D printed glycol-modified polyethylene terephthalate composites with organically modified montmorillonite and short carbon fibers: Experimentation and ANN based predictive strategy

In this article, the acoustic properties of 3D printed glycol-modified polyethylene terephthalate (PETG) reinforced with organically modified montmorillonite (OMMT) nanoclay/short carbon fiber (SCF) nanocomposites are experimentally investigated using ASTM E1050-08 standard. To this end, the sound absorption coefficient (SAC) of different PETG composites was calculated with the aid of two microphone impedance tube, working in the frequency range of 50-6300 Hz. The effect of different weight percentages (wt%) of OMMT nanoclay, SCFs, and 3D printing infill density on the acoustic behavior of PETG nanocomposites is studied. The experimental results reveal that higher wt% of OMMT nanoclay and SCFs has a beneficial effect on sound absorption. Further, the trend of variation of SAC is justified with morphological studies. Also, an artificial neural network (ANN) based prediction methodology to predict SAC is developed using the datasets obtained from the experimentation. Levenberg-Marquardt backward propagation algorithm with 20 neurons trains the ANN model. Using the trained ANN model, the acoustic properties of PETG/OMMT/SCF nanocomposites with different operating frequencies, infill density and wt% of reinforcements are predicted with less than 5% average error. This can be beneficial in eliminating the fabrication and experimentation costs incurred while assessing the acoustic properties of the PETG composites.