Improving and Predicting the Surface Roughness and the Machining Accuracy in Ultrasonic Vibration-Assisted Milling

Abstract The present research work amis to develop a controlled vibratory system for improving the performance of slotting process based on the vibration-assisted milling technique. In addition, the establishment of a statistical model for predicting the process efficiency was a key aim target. In the current investigation, an ultrasonic vibratory device was designed to provide vibration oscillations in the cutting feed direction to the 7075 aluminum alloy workpiece side at 34.7 kHz frequency and 10 μm amplitude. A LabView code has been developed to control the input vibration parameters. A full factorial design of experiments with 4 factors and 2 levels was conducted. An in-depth statistical analysis is then implemented to study the effect of machining and vibration parameters on the process performance. Then, a statistical model for predicting the process response in terms of cutting force, surface roughness, and machining accuracy has been established. Some experiments showed a significant reduction in cutting force up to 50% in the feed direction. Additionally, an improvement in workpiece surface roughness was recorded. Regarding the machined surface accuracy, introducing ultrasonic vibrations significantly reduces the produced slot width error compared to Conventional Milling (CM). The developed statistical model shows a very well agreement with the experimental results. Therefore, the developed VAM technique is suitable for manufacturing parts that require high geometrical and dimensional accuracy.