Thermomechanical behavior of a novel hybrid epoxy/ZnO nanocomposite adhesive in structural bonding: Experimental analysis and ANN modeling

Epoxy adhesives are advanced materials, but suffer from high brittleness and low toughness due to their high crosslinking degree, which limits their service life in structural applications. The lack of appropriate thermal stability at high temperatures is another constraint of epoxy adhesives. The main aim of this research was to increase the thermal stability and toughness of epoxy adhesives using phenolic resin, zinc oxide (ZnO) nanoparticles, and poly (butyl acrylate-block-styrene) copolymer as a toughening agent. Furthermore, the mechanical properties of epoxy adhesives were predicted by designing two feed-forward multilayer perceptron (MLP) networks. Epoxy adhesives with different contents of phenolic resin (10, 20, and 30 phr), copolymer (1.25, 2.5, and 3.75 phr), and ZnO (0.5, 1, 2, and 5 phr) nanoparticles were synthesized under mechanical mixing by using xylene as solvent. Then, the epoxy adhesive samples for tensile and lap shear tests were cured at room temperature for 7 and 2 days, respectively. The mechanical properties of adhesive samples were measured by tensile and lap shear tests. The thermal stability of the epoxy adhesive samples was investigated by thermogravimetric analysis (TGA). The kinetics of the curing reaction of the optimum epoxy adhesive samples was also studied by differential scanning calorimetry (DSC). The toughness of epoxy adhesive containing 10 phr phenolic, 2.5 phr block copolymer, and 2 phr ZnO nanoparticles increased by 20%, and shear strength by 99% compared to pure epoxy adhesive, notifying a significant synergistic effect. The TGA results showed that all three mentioned additives have increased the thermal stability of pure epoxy. The highest thermal stability was observed for epoxy adhesive containing 2.5 phr block copolymer and 2 phr ZnO nanoparticles. In addition, DSC analysis proved the positive effect of ZnO nanoparticles incorporation and block copolymer on the curing kinetics of epoxy adhesive. Moreover, the predicted tensile strength, tensile modulus, toughness, and shear strength of epoxy adhesives by MLP networks showed a very good consistency between the experimental data and the model predictions (e.g., MRE < 1.1 for lap shear). In the current study, the artificial neural networks (ANN) results showed that there was very good consistency between the ANN predictions and the experimental data.