Modeling and prediction of surface roughness profile in longitudinal turning

Abstract This paper presents the methodology for modeling and predicting the roughness profile shape in longitudinal turning. The research considers the roughness profile as the sum of two profiles designated as R k i n e m . − g e o m e t . and R d i s t o r t i n g . The profile R k i n e m . − g e o m e t . is the theoretical profile obtained as the result of the kinematical-geometrical copying of the cutting tool onto the machined surface. The input values for the modelling R k i n e m . − g e o m e t . are the feed f , the nose radius r e and the angles λ s , γ o , κ r . R d i s t o r t i n g is the profile obtained as a result of the impact of the other cutting conditions and factors that do not participate in the formation of R k i n e m . − g e o m e t . The shape of R d i s t o r t i n g directly depends on the condition of the surface roughness formation process. The Parameter of statistic equality of sampling lengths in surface roughness measurement ( S E ) indicates the condition of the surface roughness formation process. The proposed methodology was verified on work pieces made of 42CrMo (EN) using finishing inserts. The predicted and measured roughness profiles were compared using the following indicators: visual comparison of the roughness profiles, the R a , R p , R v and R z parameters respectively, the material ratio curves and the histograms.

[1]  E. Miko,et al.  Analysis and Verification of Surface Roughness Constitution Model After Machining Process , 2012 .

[2]  B. K. Lambert,et al.  Mathematical models to predict surface finish in fine turning of steel. Part I. , 1981 .

[3]  Ashok Kumar Sahoo,et al.  Multi-Objective Optimization and Predictive Modeling of Surface Roughness and Material Removal Rate in Turning Using Grey Relational and Regression Analysis , 2012 .

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

[5]  M. Kuzinovski,et al.  Engineering of surface layer in material removal machining , 2004 .

[6]  Bengt-Göran Rosén,et al.  Interactive surface modelling, an implementation of an expert system for specification of surface roughness and topography , 1995 .

[7]  Joseph C. Chen,et al.  On-line surface roughness recognition system using artificial neural networks system in turning operations , 2003 .

[8]  S. J. Hu,et al.  An Integrated Model of a Fixture-Workpiece System for Surface Quality Prediction , 2001 .

[9]  Adam Boryczko Profile irregularities of turned surfaces as a result of machine tool interactions , 2011 .

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

[11]  E. Kirubakaran,et al.  Surface Roughness Prediction using Artificial Neural Network in Hard Turning of AISI H13 Steel with Minimal Cutting Fluid Application , 2014 .

[12]  Mikolaj Kuzinovski,et al.  A New Parameter of Statistic Equality of Sampling Lengths in Surface Roughness Measurement , 2013 .

[13]  Nouredine Ouelaa,et al.  Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool , 2012 .

[14]  J. I. Nanavati,et al.  Optimisation of machining parameters for turning operations based on response surface methodology , 2013 .

[15]  Tuğrul Özel,et al.  Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks , 2005 .

[16]  Chang-Xue Feng,et al.  Surface roughness predictive modeling: neural networks versus regression , 2003 .