Precision and Energy-Efficient Ball-End Milling of Ti6al4v Turbine Blades Using Particle Swarm Optimization

Abstract

Ti6Al4V is extensively employed in the aviation, biomedical, and energy industries due to its high strength at elevated temperatures, low thermal conductivity, and high corrosion/creep resistance. However, these intrinsic properties have brought many technical challenges during the machining process of turbine blade shape. However, the recent advancements in flexible and precision machining in the state-of-the-art turn-mill machine tools can enable the manufacturing of high-quality turbine blades. In this study, a methodology is developed for the precision and energy-efficient machining of Ti6Al4V turbine blades on turn-mill machine tools. The stock-to-part manufacturing procedure includes the initial turning operation on bar stock, rough flank milling, and final surface finish milling. The developed method encompasses five stages. Utilizing a reconfigured Particle Swarm Optimization algorithm, a 3D Pareto optimal solution set is generated to evaluate trade-offs between surface roughness, specific cutting energy, and material removal rate. Also, the effect of different inclination angles is analyzed, and the optimization method is repeated with varying lead and tilt angles. This work shows that the surface roughness can be effectively reduced by including inclination angles into optimization while improving the machining process's energy efficiency.