Comparative study and prediction of machining vibration during milling of AA6061 alloys under sunflower vegetable oil

Manufacturing is a significant aspect of transforming raw materials into a finished product. One of the standard methods is computer numerical control via the milling process, which involves removing unwanted materials from the workpiece. This process is carried out with a combination of cutting parameters such as feed rate, depth of cut, and spindle speed. During this process, heat generation occurs due to the chip's discontinuity at the cutting region, which also causes friction that leads to tool wear and high machining vibration. This study aimed to conduct an experimental analysis to study the cutting parameters effect on machining vibration during the milling of AA6061 alloys. The study involves three variable parameters, cutting depth, spindle speed, and feed rate, at three levels. The Box Behnken surface response methodology was implemented for the experimental study, as well as the prediction and optimization of the machining vibration. Two cutting fluids, mineral oil, and sunflower oil, were used as coolants. From the result, the sunflower oil cutting fluid reduces the vibration by 3.3% compared to the mineral oil. The results showed that the developed model predicted the machining vibration with 93% accuracy. The minimum machining vibration was achieved with optimal cutting parameters of spindle speed of 2827 rpm, feed rate of 143 mm/rev, cutting depth of 1.16 mm, and the lowest machining vibration of 40 mm/sec. This study of the interaction effects of cutting parameters on machining vibration significantly improved the machining performance for the aluminum alloy.