Investigation on machining characteristics of TC4 alloy by simultaneous application of ultrasonic vibration assisted milling (UVAM) and economical-environmental MQL technology

The optimization of machining methods is one of important guiding principles for achieving high-quality, cost saving and environmental protection machining of difficult-to-machine materials in modern manufacturing. Considering the principles and advantages of ultrasonic machining (UM) method and minimum quantity lubrication (MQL) technique, the simultaneous application of UM and MQL methods is anticipated to further improve machining performance in terms of cutting force, surface quality and chip morphology. Theoretical analysis indicates that the MQL system in UVAM could provide enhanced lubrication and cooling performance because of the separate-type cutting mechanism, the surface acoustic and the cavitation phenomenon of resonant workpiece in UM. Conventional milling (CM) trials, general UVAM trials and combined UVAM&MQL trials were conducted to investigate the effect of UVAM and MQL methods on machining performance of TC4 alloy, where the results of CM and general UVAM could provide indispensable reference. Multifaceted comparisons are presented in machining results of CM, UVAM and UVAM&MQL trials in terms of time-domain analysis and frequency-domain analysis of milling force, average cutting force, surface topography, surface profile and surface roughness. The results show that cutting force features could be significantly affected under UVAM and UVAM&MQL condition compared with that in general milling due to intermittent cutting mechanism and the coupling effect of UVAM and MQL methods. The uniform micro-textured surfaces with improved profile fluctuations could be obtained when applying the UVAM&MQL strategy. Compared with surface roughness in CM and UVAM, the surface roughness in UVAM&MQL is improved by about 30-50% and about 20-30%, respectively. The excellent machining performance demonstrates the feasibility and effectiveness of simultaneously applying UVAM and MQL methods.