High-speed milling of titanium alloys: Modeling and optimization

Abstract

High-speed machining (HSM) has established its dominant position among other rapid manufacturing techniques; however, high-speed applications on the difficult-to-cut materials, such as titanium alloys, are still relatively new. Titanium alloys have been widely used in industries. However, it is very difficult to machine them due to their poor machinability, thus selecting the optimal machining conditions and parameters is crucial. In this study, the cutting performance of a new tool material binder-less cubic boron nitride (BCBN) has been investigated for high-speed milling of Ti-6Al-4V. The wear mechanism is also analyzed. The Johnson-Cook (JC) strength model is used to describe the deformation behavior of Ti-6Al-4V. After obtaining the JC model and the 2-D equivalent element representation, finite element method (FEM) is used to simulate the high-speed milling of Ti-6Al-4V. Based on FEM-simulation and Oxleya??s model, a hybrid cutting force model is proposed to predict cutting forces when machining Ti-6Al-4V. Experimental verification is also provided to justify accuracy of the model. A new optimization method parallel genetic simulated annealing (PGSA) is developed to determine optimal cutting strategies for milling operations. For the multi-objective optimization of milling process, non-dominated sorting methodology is employed to handle the fitness assignment for PGSA. Based on experimental results and comparison with other algorithms, PGSA is found to be much more suitable for multi-objective optimization of the cutting parameters for milling operation.