A physically based constitutive model of microstructural evolution of Ti6Al4V hard machining under different lubri-cooling conditions

The metallurgical phenomena taking place during machining processes affect the thermo-mechanical properties of the severely deformed materials, influencing, consequently, the process behavior. The microstructural modifications are difficult to be evaluated when the material is subjected to high speed deformations that are typical of material removal processes. Therefore, the microstructure-based numerical simulations can represent a useful tool able to properly predict their mechanics. Hard turning experiments were conducted on Ti6Al4V alloy, involving different process parameters and lubri-cooling conditions. The worked samples surfaces were assessed in terms of resulting microstructural changes and microhardness. The obtained results (cutting forces, temperature, and surface metallurgical modifications) were considered to develop and validate a physics-based model able to describe the microstructural phenomena occurring under large deformation processes, taking into account the influence of the physical phenomena that accommodate the material plastic strengthening and their resulting effects on the process variables. The dislocations reciprocal influence and their interaction with the material lattice were considered to understand the material viscoplastic flow. Moreover, also the recrystallization phenomena influencing the grain size related strengthening were considered to formulate the model. Then, the developed material model was implemented via user sub-routine in a commercial finite element (FE) software. The FE model was used to in-depth analyze the inner evolution of the processed material and to predict the variables of industrial interest. A good agreement was shown between the experimentally measured variables and the numerically predicted results. Moreover, the model was employed to investigate additional machining conditions via finite element analysis (FEA), demonstrating a huge capability to improve the manufacturing process performances, leading to a deeper knowledge of microstructural evolution and the material machinability under various process conditions.