Development of mathematical models for surface roughness parameter prediction in turning depending on the process condition

Abstract This paper presents mathematical models for predicting R z , R p , R v , R a , R S m and R m r ( c ) roughness parameters based on the kinematical–geometrical copying of the cutting tool onto the machined surface. The feed f , the radius r e and the angles λ s , γ o , κ r have been used as input parameters in mathematical models. In order to increase the mathematical models’ accuracy, as well as their practical applicability, this paper proposes connecting the R-parameter prediction models with the condition of the surface roughness formation process. The parameter of statistic equality of sampling lengths in surface roughness measurement ( S E ) has been used for this purpose. The proposed mathematical models were verified using two different CNC lathes, working pieces made of the material 42CrMo (EN), using finishing inserts. The paper provides the theoretically calculated values, the measured values and the percentage differences between them for the considered R-parameters. The paper proposes an algorithm of steps for predicting and realizing the considered roughness parameters for industrial purposes.

[1]  Pawel Pawlus,et al.  The influence of stylus flight on change of surface topography parameters , 2005 .

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

[3]  Pawe P Pawlus.  Mechanical filtration of surface profiles , 2004 .

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

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

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

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

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

[9]  D C Watts,et al.  Comparison of two stylus methods for measuring surface texture. , 1999, Dental materials : official publication of the Academy of Dental Materials.

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

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

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

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

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

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

[16]  B. Muralikrishnan,et al.  Recent advances in separation of roughness, waviness and form , 2002 .

[17]  J. Raja,et al.  Digital filtering of surface profiles , 1979 .