Prediction of Surface Hardness in Laser-Assisted Milling

Abstract The control of work hardening in laser-assisted milling process while keeping a desirable cutting efficiency is quite challenging. Surface hardness is a good indicator of the work hardening. Therefore, it is valuable to predict surface hardness in laser-assisted milling such that the effects of process parameters can be better quantified to facilitate process planning. In the current study, a general surface hardness predictive model based on theories of metal machining and microstructure evolution in laser-assisted milling process is proposed to describe the grain size variation-induced hardness change. The laser preheating temperature field is first calculated by treating the laser beam as a moving heat source. Then, the oblique milling process is transferred to equivalent orthogonal cutting process at each rotation angle to predict the grain size dependent on dynamic recrystallization process. The inverse relationship between the grain diameter and surface hardness is applied to decide grain size variation-induced hardness change. The model is validated through laser-assisted milling experiments on Ti-6Al-4V and Ti-6Al-4V ELI. The proposed predictive model is able to match the experimental measurements in all cases with an average error of 3% for Ti-6Al-4V and 3.3% for Ti-6Al-4V ELI. In addition, a sensitivity analysis is conducted on Ti-6Al-4V to study the influences of cutting speed, depth of cut, laser power, and laser-tool distance on hardness. The proposed analytical model is valuable for providing a fast, credible, and physics-based method for the prediction of surface hardness in laser-assisted milling of various materials. Through sensitivity analysis, the model is able to guide the selection of cutting and laser parameters when the control of surface hardness is the main target.