Support vector regression and genetic-algorithm-based multiobjective optimization of mesoscopic geometric characteristic parameters of ball-end milling tool

The poor machinability of titanium alloys results in the serious wear of the rake face of a ball-end milling tool. Previous studies indicated that the mesoscopic geometric characteristics of the tool can effectively improve the wear resistance. Therefore, in this thesis, a milling force model and a milling temperature model of a ball-end milling tool were established to verify the effect of the blunt and negative chamfer tool edges. Setting up a test platform for milling titanium alloy, the influence of mesoscopic geometric characteristic parameters on cutting performance of the ball-end milling tool was analyzed. In addition, based on the support vector regression and genetic algorithm, the optimal mesoscopic geometric characteristic parameters were obtained, which were under the four evaluation indices, such as mechanical-thermal characteristics, tool wear, and surface quality of the workpiece. It was verified experimentally that the tool life of the optimized micro-textured tools with the blunt and chamfer tool edges were improved by 33% and 25%, respectively, and the surface roughness was reduced by 26% and 23%, respectively, which were compared to the non-optimized tools. This thesis provides a reference for improving the processing efficiency and the quality of titanium alloys.