Micro-milling machinability prediction for crystalline materials via numerical-analytical hybrid modelling and strain rate-dependent grain-scale simulation

Micro-milling is a promising technology to produce miniature-sized components, while the machinability prediction of crystalline material micro-milling is difficult due to the inhomogeneous microstructure, anisotropic properties, nonlinear strain rate sensitivity and complex process characteristics. To achieve accurate and effective micro-milling machinability prediction of crystalline materials, this paper proposes a numerical-analytical hybrid method that comprehensively and simultaneously considers core material and process characteristics of the crystalline material micro-milling technique, which evaluates micro-milling forces and surface roughness via a series of adaptive strain rate-dependent micro-orthogonal cutting emulations in grain-scale and a set of analytical transformations. Taking Inconel-718 as the sample material, grain-scale mechanical parameters under 10⁻²–10⁴ s⁻¹ strain rate range were calibrated. Strain rate effect-considered grain-scale constitutive theory is proposed and strain rate-dependent micro-orthogonal cutting emulations were executed, which were experimentally verified to be accurate and robust on the forces and surface roughness prediction of micro-cutting. Furthermore, numerical-analytical hybrid modelling and machinability prediction of Incoenl-718 micro-milling process under 12 mm/min and 20 mm/min feed rates were carried out, the relative prediction errors of micro-milling forces and surface roughness are <15 % and 14 %, which confirm that the proposed hybrid method is accurate and robust on the micro-milling machinability prediction of crystalline materials.