Optimizing the ultrasonication effect in stir-casting process of aluminum hybrid composite using desirability function approach and artificial neural network

Abstract

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.