In this research work, we have investigated the effect of nanoclay fillers in the epoxy adhesive on improving the interfacial adhesion and the mechanical properties of glass-reinforced aluminum laminates. The laminates were prepared with a varying weight percentage of nanoclay fillers (i.e., 3, 4, and 5 wt%) in the epoxy resin using the hand lay-up method. During the tensile test, the laminate's anisotropic nature was investigated using the samples cut in different orientations to the rolling direction (i.e., 0, 45, and 90 ). The flexural, lap shear, and impact tests were conducted as per ASTM standards. After each test, the failure mechanisms of laminates were studied using the scanning electron microscope. The results showed that 3 wt% of nanoclay added in the epoxy enhanced the laminates' overall mechanical properties than other composites. The fiber metal laminates (FML) containing the nano clay more than 3 wt% showed deterioration in the mechanical property due to the improper distribution of nanoclay in the epoxy matrix. The FML's mechanical properties with nano clay filler were compared with other fillers' performance in the previous studies. It was found that the mechanical properties of FML with nano clay filler were more significant than others.
The ultrasonic-assisted stir-casting technique improves the uniform dispersion of nano-reinforcements in aluminum hybrid metal matrix composites. In the present study, the process parameters of the ultrasonic-assisted stir-casting method, such as ultrasonic vibration time, and depth of ultrasonic vibration along with the speed of mechanical stirrer, are optimized on A356 hybrid composite material optimally reinforced with aluminum nitride, multiwalled carbon nanotubes, graphite particles, and aluminum metal powder using the desirability function approach. The process parameters are optimized against the response factors such as porosity, ultimate tensile strength, and wear rate of the composites. The optimum combination of input factors is identified as stirring speed (600 r/min), ultrasonic vibration time (2 min), and depth of ultrasonic vibration (40 mm) among the selected range. The corresponding output response values are found to be porosity (1.4%), ultimate tensile strength (247 MPa), and wear rate (0.0013 mm3/min). The ANOVA results have revealed that depth of ultrasonic vibration showed significant contribution among the input factors. An artificial neural network model is developed and validated for the given set of experimental data.
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