Cutting Force Modeling and Experimental Investigation of Axial Ultrasonic Vibration-Assisted Milling for Titanium Alloy Ti-6Al-4V

Titanium alloy (Ti-6Al-4V) exhibits exceptional comprehensive properties, which have resulted in its widespread adoption across diverse industrial sectors. However, its poor machinability poses a significant bottleneck to achieving high-efficiency and high-precision manufacturing. To address the challenges inherent in conventional milling (CM), axial ultrasonic vibration is introduced at the end of the milling spindle to enhance machining quality. Through kinematic analysis, a model of the tool-tip trajectory and tool-workpiece contact rate is established. By incorporating the vibration displacement vector, an instantaneous cutting thickness equation is derived. Subsequently, by integrating tool geometric parameters, instantaneous cutting thickness (IUCT), contact rate, and vibration impact effects, an analytical prediction model for transient cutting forces in ultrasonic vibration-assisted milling (UVAM) is developed. Concurrently, a finite element (FE) model is constructed to simulate the milling process, enabling numerical analysis of chip morphology, stress–strain fields, and the evolution of cutting forces. Finally, a test platform is established to verify the reliability and predictive accuracy of the proposed model. Comparative analysis reveals the advantages of UVAM over CM and elucidates the roles of UVAM parameters in machining performance. The results indicate that the experimentally measured tri-axial milling forces align well with the analytical model predictions and FE simulation outputs, exhibiting consistent trends. Compared with CM, axial UVAM reduces the tri-axial average cutting forces by 14.73%, 15.47%, and 14.49%, respectively, while simultaneously decreasing the surface roughness Ra by approximately 12.1%, indicating an improvement in surface quality. This research provides a reference for understanding the influence of process parameters and a basis for subsequent multi-parameter coupled optimization in the high-performance machining of difficult-to-machine materials.