Prediction of lap shear strength of GNP and TiO2/epoxy nanocomposite adhesives

In this study, graphene nanoplatelets (GNPs) and titanium dioxide nanofillers were added to epoxy resin P-5005 at five different weight percentages (wt%), viz., 1, 5, 10, 15, and 20 wt%. The tensile properties of the nanocomposites were experimentally tested following ASTM D638-14. Then, the above-mentioned nanocomposites were applied as adhesives for an overlap joint of two A5055 aluminum sheets. The apparent shear strength behavior of joints was tested following ASTM D1002-01. Moreover, experimentally obtained results were applied to train and test machine learning and deep learning models, i.e., adaptive neuro-fuzzy inference system, support vector machine, multiple linear regression, and artificial neural network (ANN). The peak tensile strength (TS) and joint failure load (FL) values were observed in epoxy/GNP samples. The ANN model exhibited the least error in predicting the TS and FL of the considered nanocomposites. The epoxy/GNP nanocomposites exhibited the highest TS of 28.49 MPa at 1 wt%, and the peak overlap joints exhibited an FL of 3.69 kN at 15 wt%.

Automated summary

In this study, graphene nanoplatelets and titanium dioxide nanofillers were added to epoxy resin and their effects on the tensile properties and shear strength behavior of the nanocomposites were investigated. The research showed that epoxy/graphene nanocomposites had the highest tensile strength and joint failure load, and the artificial neural network model provided the most accurate predictions for these nanocomposites.