Investigation of tool flank wear effect on system stability prediction in the milling of Ti-6AI-4 V thin-walled workpiece

Abstract

Excessive tool wear can shorten tool life and cause low machining surface quality and efficiency in the milling of Ti-6AI-4 V thin-walled workpieces. The influence mechanism between the tool flank wear and stability predication is of significant for its further development in milling Ti-6Al-4 V. However, the relationship between tool flank wear and stability predication needs to be further investigated as the effect of time-varying tool flank wear is ignored in conventional methods. In this work, a system stability prediction model considering time-varying tool flank wear effect in milling of Ti-6AI-4 V thin-walled workpiece is proposed. The tool flank wear region is discretized into differential elements, and then, the friction effect and process damping effect caused by extruding function between tool and workpiece are analyzed, and a time-varying milling force model is established. In this process, the relationship of cutting tool flank wear band width VB and section radius difference NB is determined, and the indentation volume between tool and workpiece is iteratively calculated, which is used to investigate process damping. After, the time-varying milling force coefficients are derived considering different tool flank wear status. Then, in modal space, the evolutionary process of stability lobe diagrams considering tool flank wear effect is determined. Subsequently, to effectively predict system stability, tool flank wear curves and dynamic cutting force coefficients are calibrated by slot milling, and the average errors between cutting force prediction values considering tool flank wear effect and experimental values in feed direction and normal direction are 7.3% and 12.1%, respectively. Finally, a series of machining tests are conducted to verify the effectiveness of tool flank wear on the machining stability in the milling of Ti-6AI-4 V thin-walled workpieces to some extent, and the experimental results show that the system stability prediction accuracy of the proposed method is improved by 23.8% compared with that using conventional method.