Implementation of a process and structure model for turning operations

Abstract

The consideration of the dynamic interaction between the machine tool structure and the cutting process is a prerequisite for the simulative prediction and optimization of machining tasks. However, existing cutting force models are either dedicated to already examined manufacturing operations or require extensive measurements for the determination of cutting coefficients. In this context this paper outlines a modular, analytical cutting force model applicable to common turning processes. It takes into account the dynamic material behavior, nonlinear friction ratios on the rake face as well as heat transfer phenomena in the deformation zones. On the part of the machine tool structure a parametric model based on the Finite Element Method (FEM) is implemented. Both models are coupled for the simulation of process and structure interactions, whereas the influence of the control system is considered as well. The simulation results were verified experimentally on a turning center.

A macroscopic approach towards the finite element simulation of tapping and thread milling of continuously reinforced extrusions

“…The machining process itself was divided into load steps, each representing a specific time step of the whole process, since the modeling approach did not model the tool movement in a continuous machining process, as shown in [10]. With respect to the computation time and the model size, only a relatively coarse segmentation of the model respectively the reference process was chosen.…”

Simulation of the Energy Consumption of Machine Tools for a Specific Production Task

“…Also needed the needed information for the cutting force formulas, for example cutting force coefficients, are already available from other experimental work. Therefore empirical models are able to provide good results with relatively low cost for the prediction of the cutting force [14]. It is also possible to use FEM based approaches, but was not implemented due to the computation complexity and model performance.…”