Abstract
Titanium and its alloys are largely used in various applications due its prominent mechanical properties. However, the machining of titanium alloys is associated with assured challenges, including high-strength, low thermal conductivity, and long chips produced in conventional machining processes, which result in its poor machinability. Advanced and new machining techniques have been used to improve the machinability of these alloys. Ultrasonic vibration assisted turning (UVAT) is one of these progressive machining techniques, where vibrations are imposed on the cutting insert, and this process has shown considerable improvement in terms of the machinability of hard-to-cut alloys. Therefore, selecting the right cutting parameters for conventional and assisted machining processes is critical for obtaining the anticipated dimensional accuracy and improved surface roughness of Ti-alloys. Hence, fuzzy-based algorithms were developed for the ultrasonic vibration assisted turning (UVAT) and conventional turning (CT) of the Ti-6Al7Zr3Nb4Mo0.9Nd alloy to predict the maximum process zone temperature, cutting forces, surface roughness, shear angle, and chip compression ratio for the selected range of input parameters (speed and depth-of-cut). The fuzzy-measured values were found to be in good agreement with the experimental values, indicating that the created models can be utilized to accurately predict the studied machining output parameters in CT and UVAT processes. The studied alloy resulted in discontinued chips in both the CT and UVAT processes. The achieved results also demonstrated a significant decline in the cutting forces and improvements in the surface quality in the UVAT process. Furthermore, the chip discontinuity is enhanced by the UVAT process due to the higher process zone temperature and the micro-impact imposed by the cutting tool on the workpiece.