Soft computing models and intelligent optimization system in electro-discharge machining of SiC/Al composites

In this paper, a multi-variable regression model, a back propagation neural network (BPNN) and a radial basis neural network (RBNN) have been utilized to correlate the cutting parameters and the performance while electro-discharge machining (EDM) of SiC/Al composites. The four cutting parameters are peak current (Ip), pulse-on time (Ton), pulse-off time (Toff), and servo voltage (Sv); the performance measures are material remove rate (MRR) and surface roughness (Ra). By testing a large number of BPNN architectures, 4-5-1 and 4-7-1 have been found to be the optimal one for MRR and Ra, respectively; and it can predict them with 10.61 % overall mean prediction error. As for RBNN architectures, it can predict them with 12.77 % overall mean prediction error. The multivariable regression model yields an overall mean prediction error of 13.93 %. All of these three models have been used to study the effect of input parameters on the material remove rate and surface roughness, and finally to optimize them with genetic algorithm (GA) and desirability function. Then, an intelligent optimization system with graphical user interface (GUI) has been built based on these multi-optimization techniques, in which users can obtain the optimized cutting parameters under the desired surface roughness (Ra).

[1]  Yung-Kuang Yang,et al.  Optimization of wire electrical discharge machining process parameters for cutting tungsten , 2012 .

[2]  Jose Mathew,et al.  Optimization of Material Removal Rate in Micro-EDM Using Artificial Neural Network and Genetic Algorithms , 2010 .

[3]  T. Senthilvelan,et al.  Application of response surface method on machining of Al–SiC nano-composites , 2013 .

[4]  Ushasta Aich,et al.  Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization , 2014 .

[5]  Zhen Zhang,et al.  Optimization of process parameters on surface integrity in wire electrical discharge machining of tungsten tool YG15 , 2015 .

[6]  Zhen Zhang,et al.  A new method for on-line monitoring discharge pulse in WEDM-MS process , 2015, The International Journal of Advanced Manufacturing Technology.

[7]  Wuyi Ming,et al.  Modeling and Optimization of Medium-Speed WEDM Process Parameters for Machining SKD11 , 2013 .

[8]  M. El-Gallab,et al.  Machining of Al/SiC particulate metal-matrix composites , 1998 .

[9]  S. S. Pande,et al.  Intelligent process modeling and optimization of die-sinking electric discharge machining , 2011, Appl. Soft Comput..

[10]  M. Ghoreishi,et al.  Mathematical modeling and optimization of the Electro-Discharge Machining (EDM) parameters on tungsten carbide composite: Combining response surface methodology and desirability function technique , 2015 .

[11]  Surjya K. Pal,et al.  Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite , 2008 .

[12]  R. N. Katz,et al.  The role of silicon in wetting and pressureless infiltration of SiCp preforms by aluminum alloys , 2000 .

[13]  Jagdev Singh,et al.  Artificial intelligent modeling to predict tensile strength of inertia friction-welded pipe joints , 2013 .

[14]  Feng Wang,et al.  Study on Wind Speed Prediction Based on Rbf Neural Network , 2012 .

[15]  Jianwen Guo,et al.  The multi-objective optimization of medium-speed WEDM process parameters for machining SKD11 steel by the hybrid method of RSM and NSGA-II , 2014 .

[16]  Jian Li,et al.  Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration , 2014 .

[17]  A. Varun,et al.  Simultaneous optimization of WEDM responses using grey relational analysis coupled with genetic algorithm while machining EN 353 , 2015 .

[18]  T. Senthilvelan,et al.  Optimization of machining parameters for EDM operations based on central composite design and desirability approach , 2014 .

[19]  P. Saha,et al.  Artificial neural network model in surface modification by EDM using tungsten–copper powder metallurgy sintered electrodes , 2010 .

[20]  S. N. Sivanandam,et al.  Introduction to genetic algorithms , 2007 .

[21]  M. Ghoreishi,et al.  Neural-network-based modeling and optimization of the electro-discharge machining process , 2008 .

[22]  Li Yiyi.  CERAMIC PARTICLES REINFORCED ALUMINUM ALLOY MATRIX FUNCTIONALLY GRADED COMPOSITE TUBE MANUFACTURED BY CENTRIFUGAL CASTING , 2001 .

[23]  Ajay Batish,et al.  A mathematical model to predict material removal rate during electric discharge machining of cryogenically treated titanium alloys , 2015 .