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.

超声辅助搅拌浇铸技术改善了纳米增强材料在铝杂化金属基复合材料中的均匀分散。在本研究中，超声辅助搅拌铸造方法的工艺参数，如超声振动时间、超声振动深度以及机械搅拌器的速度，在氮化铝最佳增强的 A356 杂化复合材料上进行了优化，多壁碳纳米管、石墨颗粒和铝金属粉末使用合意函数方法。工艺参数根据复合材料的孔隙率、极限拉伸强度和磨损率等响应因素进行优化。确定输入因素的最佳组合为搅拌速度（600 r/min）、超声振动时间（2 min）、和所选范围内的超声波振动深度（40 mm）。发现相应的输出响应值是孔隙率 (1.4%)、极限拉伸强度 (247 MPa) 和磨损率 (0.0013 mm³ /分钟）。方差分析结果表明，超声波振动的深度在输入因素中表现出显着的贡献。针对给定的一组实验数据开发并验证了人工神经网络模型。