In order to optimize turning processes cutting forces need to be accurately predicted. This in turn requires accurate extraction of the geometry of tool-workpiece engagements (TWE) at critical points during machining. TWE extraction is challenging because the in-process workpiece geometry is continually changing as each tool pass is executed. This paper describes research on a hybrid analytical, solid modeler and feature-based methodology for extracting TWEs generated during general turning. While a pure solid modeler based solution can be developed it will be shown that because of the ability to capture different cutting tool inserts with similar geometry and to model the process in 2D, an analytical solution can be used instead of the solid modeler in many instances. This leads to more efficient computation during extraction. Further, by identifying features in the removal volumes where the engagement conditions are not changing or changing predictably additional enhancements in the efficiency of the TWE extraction can be achieved. The methodology developed will be demonstrated in extracting engagements for a typical industrial component. TWE extraction is one component in a Virtual Machining system currently under development.

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