Simulation Approach for Surface Roughness Interval Prediction in Finish Turning

Existing simulation models used in predicting the surface roughness of a workpiece in finish turning are based on an ideal circular cutting tool nose profile. This leads to a single predicted roughness value for a given set of input parameters. In this paper, a simulation approach that considers the random tool nose profile micro-deviations as well as the tool chatter vibration to predict a roughness interval is proposed. The nose profiles used in the simulation were extracted from images of the real cutting tool inserts using sub-pixel edge location. The chatter vibration signal was reconstructed from the measured signals and was superimposed onto the extracted nose profile. The roughness data were computed from 24 simulated workpiece surface profiles and used to determine the 95 % roughness prediction interval. Comparison with the experimental results showed that 100 %, 96 % and 96 % of the Rt, Ra and Rq roughness values obtained experimentally fell within the predicted roughness intervals. (Received in March 2015, accepted in September 2015. This paper was with the authors 1 month for 1 revision.)

[1]  Dazhong Wang,et al.  Investigations on the Effects of Different Tool Edge Geometries in the Finite Element Simulation of Machining , 2015 .

[2]  Shih-Chieh Lin,et al.  A study on the effects of vibrations on the surface finish using a surface topography simulation model for turning , 1998 .

[3]  Chi Fai Cheung,et al.  A theoretical and experimental investigation of surface roughness formation in ultra-precision diamond turning , 2000 .

[4]  Chen Lu,et al.  Study on prediction of surface quality in machining process , 2008 .

[5]  Mani Maran Ratnam,et al.  Determination of tool nose radii of cutting inserts using machine vision , 2011 .

[6]  M. Siddhpura,et al.  A review of chatter vibration research in turning , 2012 .

[7]  Tarek Mabrouki,et al.  On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations , 2013 .

[8]  Tomislav Šarić,et al.  Modelling and simulation of surface roughness in face milling , 2013 .

[9]  Jun Qu,et al.  Analytical Surface Roughness Parameters of a Theoretical Profile Consisting of Elliptical Arcs , 2003 .

[10]  Mani Maran Ratnam,et al.  Effect of tool nose profile tolerance on surface roughness in finish turning , 2015, ICRA 2015.

[11]  H. H. Shahabi,et al.  Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach , 2010 .

[12]  Bean Yin Lee,et al.  The model of surface roughness inspection by vision system in turning , 2004 .

[13]  Joaquim P. Marques de Sá,et al.  Applied statistics : using SPSS, STATISTICA, and MATLAB , 2003 .

[14]  Franci Cus,et al.  Surface Roughness Control Simulation of Turning Processes , 2015 .

[15]  Miloš Madić,et al.  MATHEMATICAL MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN TURNING OF POLYAMIDE BASED ON ARTIFICIAL NEURAL NETWORK , 2012 .

[16]  Xiaowen Wang,et al.  Development of Empirical Models for Surface Roughness Prediction in Finish Turning , 2002 .

[17]  Geeta Agnihotri,et al.  Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques - a Comparative Study , 2015 .

[18]  R. C. Skelton Surface finish produced by a vibrating tool during turning , 1969 .

[19]  Durmus Karayel,et al.  Prediction and control of surface roughness in CNC lathe using artificial neural network , 2009 .

[20]  Joseph C. Chen,et al.  Development of a fuzzy-nets-based surface roughness prediction system in turning operations , 2007, Comput. Ind. Eng..

[21]  Uday S. Dixit,et al.  A neural-network-based methodology for the prediction of surface roughness in a turning process , 2005 .

[22]  Mehmet Çunkas,et al.  Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method , 2011, Expert Syst. Appl..

[23]  M. S. Safizadeh,et al.  Analysis of machining parameters effects on surface roughness: a review , 2012 .

[24]  Pero Raos,et al.  Experimental analysis of surface roughness and surface texture of machined and fused deposition modelled parts , 2014 .

[25]  Claus Thorn Ekstrøm,et al.  Introduction to Statistical Data Analysis for the Life Sciences , 2014 .

[26]  Wassila Bouzid,et al.  Analytical modeling of surface profile in turning and burnishing , 2014 .

[27]  R. Patrikar Modeling and simulation of surface roughness , 2004 .

[28]  Ning Ma,et al.  Pre-evaluation on surface profile in turning process based on cutting parameters , 2010 .

[29]  Imtiaz Ahmed Choudhury,et al.  Surface roughness prediction in the turning of high-strength steel by factorial design of experiments , 1997 .