Abstract
The prediction of cutting forces is critical for the control and optimization of machining processes. This paper is concerned with developing prediction model for cutting forces in micro-grinding. The approach is based on the probabilistic distribution of undeformed chip thickness. This distribution is a function of the process kinematics, properties of the workpiece, and micro-topography of the grinding tool. A Rayleigh probability density function is used to determine the distribution of the maximum chip thickness as an independent parameter. The prediction model further includes the effect of dressing parameters. The integration of the dressing model enables the prediction of static grain density of the grinding tool at various radial dressing depths. The tool deflection is also considered in order to account for the actual depth of cut in the modeling process. The dynamic cutting-edge density as a function of the static grain density, the local tool deflection, elastic deformation, and process kinematics can hence be calculated. Once the chip thickness is calculated, the single-grain forces for individual abrasive grains are predicted and the specific tangential and normal grinding forces simulated. The simulation results are experimentally validated via cutting-force measurements in micro-grinding of Ti6Al4V. The results show that the model can predict the tangential and normal grinding forces with a mean accuracy of 10% and 30%, respectively. The observed cutting forces further imply that the flow stress of the material did not change with changing the cutting speed and the cutting strain rate. Moreover, it was observed that the depth of cut and grinding feed rate had the same neutral effect on the resultant grinding forces.
Highlights
In many industrial sectors the demands for manufacturing precise micro-parts with a high level of accuracy and complexity is increasing
Contrary to the conventional grinding process, which is typically employed for grinding of simple components, the micro-grinding offers a high degree of geometric flexibility to produce micro-parts with sophisticated fea tures
Its further distinction to conven tional grinding includes inherent tool deflection and the size effect associated with very small chip thicknesses (e.g. 0.4 μm) [1,2,3]
Summary
In many industrial sectors the demands for manufacturing precise micro-parts with a high level of accuracy and complexity is increasing. Since a grinding process involves material removal with a large number of abrasive grains (with geometrically undefined cutting edges) which are randomly districted over the surface of the grinding tool with different shapes and cutting edges, it is challenging to study and model the chip thickness and material removal mechanisms. Cheng and Gong [17] modeled the process forces considering crystallographic effects in micro-grinding of single-crystal silicon They used the undeformed chip thickness to connect the process parameters and grinding forces. An attempt is made to develop a model to predicting cutting forces considering the effects of dressing parameters and tool deflection In this approach, a probabilistic function of chip thickness, a chip thickness model based on the kinematics of the process, a topography of the tool, and material properties are considered. The results show that the prediction model can precisely predict the tangential and normal grinding forces with a mean accuracy of 10% and 28%, respectively without considering the correction factor
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