Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

Abstract Artificial neural networks (ANNs) models were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the “ANNs” models are the cutting parameters: rotational speed, feed, depth of cut, pre-tool flank wear and vibration level. The output parameters of the model are the corresponding calculated fractal parameters: fractal dimension “ D ” and vertical scaling parameter “ G ”. The model consists of three-layered feed-forward back-propagation neural network. ANNs models were utilized successfully for modeling and predicting the fractal parameters “ D ” and “ G ” in face milling operations. Moreover, W–M fractal function was integrated with the developed ANNs models in order to generate an artificially fractal predicted profiles at different cutting conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens.

[1]  Prasanta Sahoo,et al.  A fractal analysis of adhesive wear at the contact between rough solids , 2002 .

[2]  Jan Lundberg,et al.  Influence of surface roughness on normal-sliding lubrication , 1995 .

[3]  O. Mahrenholtz,et al.  Influence of surface roughness on friction during metal forming processes , 2005 .

[4]  Shirong Ge,et al.  Experimental study on the characterization of worn surface topography with characteristic roughness parameter , 2003 .

[5]  B. Ramamoorthy,et al.  The influence of component inclination on surface finish evaluation using digital image processing , 2007 .

[6]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[7]  Ning Ren,et al.  Behavior of Elastic-Plastic Rough Surface Contacts as Affected by Surface Topography, Load, and Material Hardness , 1996 .

[8]  Uday S. Dixit,et al.  A knowledge-based system for the prediction of surface roughness in turning process , 2006 .

[9]  Kyriakos Komvopoulos,et al.  Head-disk interface contact mechanics for ultrahigh density magnetic recording , 2000 .

[10]  Mohsen A. Issa,et al.  Fractal dimension as a measure of roughness of concrete fracture trajectories , 1994 .

[11]  Li Xiao,et al.  A study on the effect of surface topography on rough friction in roller contact , 2003 .

[12]  B. Bhushan,et al.  Role of Fractal Geometry in Roughness Characterization and Contact Mechanics of Surfaces , 1990 .

[13]  Kee S. Moon,et al.  Modeling Surface Texture in the Peripheral Milling Process Using Neural Network, Spline, and Fractal Methods with Evidence of Chaos , 1999 .

[14]  Wisley Falco Sales,et al.  Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network , 2005 .

[15]  A. Majumdar,et al.  Fractal characterization and simulation of rough surfaces , 1990 .

[16]  Tuncay Erzurumlu,et al.  Comparison of response surface model with neural network in determining the surface quality of moulded parts , 2007 .

[17]  B. Bhushan.  Handbook of micro/nano tribology , 1995 .

[18]  T. Stolarski.  Modern Tribology Handbook , 2003 .