Modeling and prediction of surface roughness in belt polishing based on artificial neural network

Surface roughness is a variable often used to describe the quality of ground surfaces as well as to evaluate the competitiveness of the overall polishing system, which makes it an ever-increasing concern in industries and academia nowadays. In this article, from microscopic point of view, based on the statistics analysis, and by the use of the elastic contact theory and the plastic contact theory, the model of the maximum cutting depth of abrasive grains is developed. Then based on back-propagation neural network, taking the maximum cutting depth of abrasive grains, the rotation speed of belt and the feed rate of workpiece as the input parameters, a prediction model of surface roughness in belt polishing is presented. The prediction model fully takes the characteristics of polishing tool and workpiece into consideration which makes the model more comprehensive. Compared with the model that takes the polishing force as the input parameter, the model in this article needs fewer experiment samples which will save the experiment cost and time. Moreover, it has a wider range of uses and is suitable for different polishing situations such as different workpieces and polishing tools. The results indicate a good agreement between the predicted values and experimental values which verify the model.

[1]  F. Xi,et al.  Modeling surface roughness in the stone polishing process , 2005 .

[2]  Shih-Hsiang Chang,et al.  Contact Mechanics of Superfinishing , 1998, Manufacturing Science and Engineering.

[3]  D. Bouzid,et al.  Correlation between contact surface and friction during the optical glass polishing , 2014 .

[4]  Girish Kant,et al.  Optimization of Machining Parameters to Minimize Surface Roughness using Integrated ANN-GA Approach , 2015 .

[5]  Hülya Durmuş,et al.  The use of neural networks for the prediction of wear loss and surface roughness of AA 6351 aluminium alloy , 2006 .

[6]  Robert Bauer,et al.  A survey of recent grinding wheel topography models , 2006 .

[7]  Juan J. Marquez,et al.  Process modeling for robotic polishing , 2005 .

[8]  Girish Kant,et al.  Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm , 2015 .

[9]  Xiang Zhang,et al.  A local process model for simulation of robotic belt grinding , 2007 .

[10]  S. Liang,et al.  Predictive modeling of surface roughness in grinding , 2003 .

[11]  A. Dehghan Ghadikolaei,et al.  Experimental study on the effect of finishing parameters on surface roughness in magneto-rheological abrasive flow finishing process , 2015 .

[12]  G. K. Lal,et al.  The Role of Grain Tip Radius in Fine Grinding , 1975 .

[13]  Yinbiao Guo,et al.  A novel method for aspheric polishing based on abrasive trajectories analysis on contact region , 2015 .

[14]  Yiqiang Wang,et al.  Modeling and analysis of the material removal depth for stone polishing , 2009 .

[15]  Jung-Hwan Ahn,et al.  Development of a sensor information integrated expert system for optimizing die polishing , 2001 .

[16]  Jinting Xu,et al.  Automatic robotic polishing on titanium alloy parts with compliant force/position control , 2015 .

[17]  Xiaoyuan Li,et al.  Investigation of factors influencing microscopic interactions between the diamond indenter and material surfaces in nano-indentation , 2015 .

[18]  Yiqiang Wang,et al.  RESEARCH ON POLISHING PROCESS OF A SPECIAL POLISHING MACHINE TOOL , 2009 .

[19]  D. Bogy,et al.  An Elastic-Plastic Model for the Contact of Rough Surfaces , 1987 .

[20]  John A. Williams,et al.  The prediction of friction and wear when a soft surface slides against a harder rough surface , 1996 .

[21]  Guilian Wang,et al.  Process optimization of the serial-parallel hybrid polishing machine tool based on artificial neural network and genetic algorithm , 2010, Journal of Intelligent Manufacturing.

[22]  Je Hoon Oh,et al.  Prediction of surface roughness in magnetic abrasive finishing using acoustic emission and force sensor data fusion , 2011 .

[23]  S. M. Wang,et al.  Investigation of increased removal rate during polishing of single-crystal silicon carbide , 2015 .