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|Title:||Characterization and modeling of burr formation in micro-end milling|
|Publisher:||ELSEVIER SCIENCE INC|
|Citation:||PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY,35(4)625-637|
|Abstract:||Mechanical micromachining is increasingly finding applications in fabrication of components in various fields, such as, biomedical devices, optics, electronics, medicine, communications and avionics. In order to ensure adequate functionality, there are stringent requirements for form and finish in case of biomedical devices like cochlear implants and metallic optics. This necessitates that the post machined surface must be burr free. To address these issues in micromachining, this paper presents results of an experimental study to investigate the influence of main process parameters i.e. speed, feed rate, depth of cut, tool diameter and number of flutes on the formation of the various types of burrs i.e. exit burrs and top burrs produced during micro-end milling operation. The experiments performed using Taguchi method shows that three types of burr formation mechanisms prevail during micro-end milling operations; these are: lateral deformation of material, bending and tearing of the chip. Also, three types of burrs were observed include: Poisson burr, rollover burr in down milling and tear burr in up milling. Further, it is observed that the depth of cut and the tool diameter are the main parameters, which influence the burr height and thickness significantly. However, the speed and the feed rate have small to negligible effect on the burr thickness and height. Besides the experimental analysis, the paper presents an analytical model to predict the burr height for exit burr. The model is built on the geometry of burr formation and the principle of continuity of work at the transition from chip formation to burr formation. Note that prediction of burr height in micro-end milling is extremely challenging due to the complex geometry of material removal and microstructural effects encountered during cutting at that length scales. The model fares well and the prediction errors range between 0.65 and 25%. (C) 2011 Elsevier Inc. All rights reserved.|
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