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

The micromilling is being extensively applied to manufacture the miniature and precise parts intended to biomedical fields, MEMS, optical engineering, and the avionic industries. However, the accurate modeling of chip formation during micromilling, concerning the thermo-mechanical phenomena has not yet been recognized sufficiently. Therefore, this paper proposes the original model of micromilling mechanics based on analytic-numerical approach, considering two cutting teeth passes. It should be stated that majority of finite elements (FE) models dedicated to microcutting includes only the first tooth pass, and thus neglects important thermo-mechanical conditions appearing in on-going microcutting process. 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 material state resulting from the first pass to be accounted for when the second tool pass was simulated. In current study, a Coupled Eulerian-Lagrangian (CEL) approach was adapted to predict the stresses and strains in micro-cutting zone and to estimate a 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 a two teeth passes were employed in comprehensive analytic model considering tool deflections, cutter run out and chip thickness accumulation. This analytic model has been applied to predict the cutting forces during micro-milling process. It was established that application of a novel analytic-numerical model can improve the accuracy of cutting force and minimum uncut chip thickness prediction. Moreover, analysis of a relative error of RMS F x , F y force values revealed that the force model based on data from second tool pass is characterized by a higher estimation accuracy comparing to single pass FE model. The overall relative error of RMS signal for both x and y directions is lower for the range of feed f z ≥ 1 μm/tooth, when the influence of ploughing regime becomes less critical.