Application of Grey Relational Analysis and Simulated Annealing Algorithm for Modeling and Optimization of EDM Parameters on 40CrMnMoS86 Hot Worked Steel

The present study is aimed at optimizing the Material Removal Rate (MRR), Surface Roughness (SR) and Tool Wear Rate (TWR) of die sinking Electrical Discharge Machining (EDM) by considering the simultaneous affect of various input parameters. The experiments are carried out on 40CrMnMoS86 hot worked steel parts. Experiments were conducted by varying the peak current (I), voltage (V), pulse on time (Ton), pulse off time (Toff) and duty factor (η). The corresponding values of material removal rate, tool wear rate and surface roughness were measured. The relation between machining parameters and performance can be found out with the Grey Relational Analysis (GRA). Developed multi objective model is optimized by Simulated Annealing algorithm (SA) and machining optimal parameters setting is found. A confirmation test is also performed to verify the effectiveness of optimization procedure in determining the optimum levels of machining parameters. The consequences show that the combination of Taguchi technique, grey relational analysis and simulated annealing algorithm is quite efficient in determining optimal EDM process parameters.

[1]  Kazuo Yamazaki,et al.  A fundamental study on Ti–6Al–4V's thermal and electrical properties and their relation to EDM productivity , 2008 .

[2]  Farhad Kolahan,et al.  Simulated Annealing Application for Structural Optimization , 2007 .

[3]  Won Tae Kwon,et al.  Optimization of EDM process for multiple performance characteristics using Taguchi method and Grey relational analysis , 2010 .

[4]  Bijoy Bhattacharyya,et al.  Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model , 2006 .

[5]  K. W. LaBarge.  Electrical discharge machining. , 1997, Journal of dental technology : the peer-reviewed publication of the National Association of Dental Laboratories.

[6]  Farhad Kolahan,et al.  A Statistical Approach for Predicting and Optimizing Depth of Cut in AWJ Machining for 6063-T6 Al Alloy , 2009 .

[7]  G. Krishna Mohana Rao,et al.  HYBRID MODELING AND OPTIMIZATION OF HARDNESS OF SURFACE PRODUCED BY ELECTRIC DISCHARGE MACHINING USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM , 2010 .

[8]  J. L Lin,et al.  The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics , 2002 .

[9]  P. Asokan,et al.  Development of multi-objective optimization models for electrochemical machining process , 2008 .

[10]  G. Krishna Mohana Rao,et al.  Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm , 2009 .