Deepen the analysis of the cutting process mechanics beneath the tool edge radius is one of the primary concerns in micro cutting. This paper shows that coupled Eulerian Lagrangian (CEL) technique applied to micro machining process modelling can be a valid alternative comparison to traditional Finite Element (FE) chip formation modelling methods. CEL does not require separation criteria since it implements workpiece modelling by means of plastic deformation and considering it as Eulerian domain – whilst keeping tool in Lagrangian domain – and avoiding mesh topography changes. The developed 3D CEL entails a full set of thermo-mechanical input parameters and is validated by conducting micro orthogonal cutting experiments on ultra-high precision machining. CEL approach resulted capable to predict the process outputs, i.e., namely chip thickness and cutting/thrust forces, within a mean error of around 20%, comparable to existing techniques and showed better accuracy in actual chip thickness prediction.
Coupled Eulerian-Lagrangian technique for microcutting FE-modelling of AISI1045 steel
Afsharhanaei, Ali;Parenti, Paolo;Annoni, Massimiliano
2019-01-01
Abstract
Deepen the analysis of the cutting process mechanics beneath the tool edge radius is one of the primary concerns in micro cutting. This paper shows that coupled Eulerian Lagrangian (CEL) technique applied to micro machining process modelling can be a valid alternative comparison to traditional Finite Element (FE) chip formation modelling methods. CEL does not require separation criteria since it implements workpiece modelling by means of plastic deformation and considering it as Eulerian domain – whilst keeping tool in Lagrangian domain – and avoiding mesh topography changes. The developed 3D CEL entails a full set of thermo-mechanical input parameters and is validated by conducting micro orthogonal cutting experiments on ultra-high precision machining. CEL approach resulted capable to predict the process outputs, i.e., namely chip thickness and cutting/thrust forces, within a mean error of around 20%, comparable to existing techniques and showed better accuracy in actual chip thickness prediction.File | Dimensione | Formato | |
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