Modeling of process mechanics in micromilling of AISI 1045 steel based on Coupled Eulerian-Lagrangian FE model with consecutive cuts

Micromilling is being extensively applied to manufacture the miniature and precise parts intended for biomedical fields, MEMS, optical engineering, and the avionic industries. However, accurate modeling of chip formation during micromilling, including the thermo-mechanical phenomena has not yet been developed sufficiently. Therefore, this paper proposes an original model of micromilling mechanics based on an analytic-numerical approach, considering two cutting tooth passes. It should be stated that the majority of finite elements (FE) models dedicated to microcutting include only the first tooth pass, and thus neglect important thermo-mechanical conditions appearing in on-going microcutting processes. Therefore, to provide better correlation of the FE model with measured microcutting process conditions, a novel approach was introduced that considers consecutive tool passes over the workpiece. This allowed the workpiece's material state resulting from the first pass to be accounted for when the second tool pass was simulated. In the current study, a Coupled Eulerian-Lagrangian (CEL) approach was adapted to predict the stresses and strains in the micro-cutting zone and to estimate specific force coefficients and minimum uncut chip thickness during micro-milling of AISI 1045 steel. Subsequently, the minimum uncut chip thickness and specific force coefficients values set for two teeth passing were employed in a comprehensive analytical model considering tool deflections, cutter run out and chip thickness accumulation. This analytical model has been applied to predict the cutting forces during micro-milling processes. It was established that the application of a novel analytic-numerical model can improve the accuracy of cutting force and minimum uncut chip thickness predictions. Moreover, analysis of a relative estimation error of RMS Fx, Fy force values revealed that the force model based on data from the second tool pass is characterized by a higher estimation accuracy comparing to a single pass FE model. The overall relative estimation error of RMS signal for both x and y directions is lower for the range of feed fz ≥ 1 μm/tooth, when the influence of the ploughing regime becomes less critical.