A Method for the Determination of Theoretical Roughness in Face Milling Considering the Run-Out of the Inserts Csaba FELHO 1,a* , Janos KUNDRAK 1,b 1 Intitute of Manufacturing Science, University of Miskolc, Hungary a csaba.felho@uni-miskolc.hu, b janos.kundrak@uni-miskolc.hu Keywords: Surface Roughness, Face Milling; CAD Modelling Abstract. A method is introduced for determining theoretical values of roughness characteristics of surfaces generated by tools having a defined edge geometry. The method is based on the CAD modelling of the theoretical cut surface, and can be used to model practically any complex tool geometry. In application to rotating tools (e.g. face milling), besides the variety of tool designs, the setting accuracy was also taken into consideration during the determination of theoretical values due to the simultaneous cutting of more than one edge. It will be demonstrated that in addition to the determination of 2D roughness parameters, the method is suitable to determine the 3D roughness parameters as the surface topography can be more accurately described with these characteristics. Experimental data is shown to validate of the extended modelling and calculation method. Introduction Increasing attention is being given to increasing the efficiency of face milling processes [1]. As the applied feed rate value substantially influences the operation time at cutting processes, one solution is the application of as high a feed rate as possible. With the increasing feed (and if the depth of cut remains constant), the chip cross section and therefore the cutting forces and the required power will dramatically increase, thus another trend is to decrease the removed material thickness simultaneously (axial depth of cut). The objective is to obtain surface quality meeting the specifications by machining of the raw part in as few cuts as possible or even in one cut. Researchers have been intensively dealing with the modelling of the roughness of machined surfaces as well as with the determination of the expected roughness of surfaces machined with the planned parameters. Often different modelling procedures are utilized [2]. A modelling method is introduced in [3] that is based on a geometrical analysis of the recreation of the tool trail left on the machined surface. During the modelling, particular attention was paid to tool setting errors (axial and radial). Not only the theoretical values were determined with the developed procedure, but also two-dimensional theoretical roughness profiles were produced. A grey-fuzzy modelling method was applied in [4] for determination of the optimal process parameters for end milling of an aluminium alloy. Investigated process parameters were Centre Line Average Roughness (Ra), Root Mean Square Roughness (Rq) and Material Removal Rate (MRR). A hybrid approach is presented for the modelling of surface roughness in slot milling in [5], where the analytical calculation of the specific energy consumption (SCEC) and empirical relations between the SCEC and surface roughness are combined in one model. It was found that a direct connection exists between the specific energy consumption (the cutting power required to remove 1 mm 3 of workpiece material) and the Ra roughness parameter, thus a new model was proposed for the prediction of the expected roughness. Effects of such technological parameters as spindle speed, feed and depth of cut on the roughness, flatness and form control of machined surfaces were analysed experimentally in [6] by using ANOVA in face milling of wrought cast steel (WCB grade B) workpieces. FEM modelling was applied in [7] in order to investigate the effects of feed on surface roughness (Ra) and components of cutting force (F c , F f ) in face milling of a titanium alloy. Modelling and experiments showed that the prediction of the expected roughness can be done on the basis of an equation with the feed directional force component obtained by FEM modelling. Solid State Phenomena Submitted: 2017-04-19 ISSN: 1662-9779, Vol. 261, pp 251-258 Revised: 2017-05-11 doi:10.4028/www.scientific.net/SSP.261.251 Accepted: 2017-05-11 © 2017 Trans Tech Publications, Switzerland Online: 2017-08-21 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, www.ttp.net. (#98734656-22/08/17,17:12:40)