Modeling and Optimization of EDM Process Parameters : A Review

Authors

  • M. Mahalingam  Department of Manufacturing Engineering, Annamalai University, Annamalainagar, Tamilnadu, India

Keywords:

Electric Discharge Machining, Modeling, Optimization

Abstract

Electric discharge machining (EDM) process is one of the most extensively used non-conventional machining processes. The EDM process removes metal without having any direct contact with the work piece. There are different performance parameters which influence on the response parameters such as Metal removal rate and Surface Roughness. Optimization is a techniques used in the manufacturing field to arrive the best manufacturing conditions, which is an essential need for the industries towards manufacturing of quality products. In machining process, it is very difficult to determine optimal cutting parameters for improving machining performance and modeling techniques is necessary to relate the precise relationship between the input and output parameters. This paper provides a review on the various research activities carried out in modeling and optimization of EDM process parameters by various techniques.

References

  1. DiBitonto,D.D., Eubank,P.T., Patel,M.R., & Barrufet,M.A. (1989). Theoretical models of the electrical discharge machining process. I. A simple cathode erosion model. Journal of Applied Physics, Vol. 66, pp.4095–4103.
  2. Perez,R., Rojas,H., Walder,G., & Flukiger,R. (2004). Theoretical modeling of energy balance in electro erosion. Journal of Materials Processing Technology, Vol. 149, pp.198–203.
  3. Taha Ali EI-Taweel., (2006). Parametric study and optimization of AL-Cu-TiC-Si P / M composite. International Journal of Machining and Machinability of Materials. Vol. 1(4), pp.380-395.
  4. Yougshun Zhao. (2004). Geometric modeling of the linear motor driven EDM die sinking process. International Journal of Machine Tools Manufacturing ,Vol. 44, pp.1-9.
  5. Sarkar, S. (2005). Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminide alloy. Journal of Materials Processing Technology, Vol. 159, pp.286-294.
  6. McGeough,J.A., & Rasmussen,H. (1997). A theoretical model of electro discharge texturing. Journal of Materials Processing Technology, Vol. 68, pp.172–178.
  7. Eubank,P.T., Patel,M.R., Barrufet,M.A., & Bozkurt,B. (1993). Theoretical models of the electrical discharge machining process. III. The variable mass, cylindrical plasma model. Journal of Applied Physics, Vol. 73, pp.7900–7909. 
  8. Hewidey, M.S., El-Taweel, T.A., & El-Safty, M.F.,(2005). Modelling the Machining Parameters of Wire Electrical Discharge Machining of Inconel 601 using RSM. Journal of Materials Processing Technology, Vol. 169, pp. 328 – 336.
  9. Patel,M.R., Barrufet,M.A., Eubank,P.T., & DiBitonto,D.D (1989). Theoretical models of the electrical discharge machining process. II. The anode erosion model. Journal of Applied Physics, Vol. 66, pp.4104– 4111.
  10. Ogun,C.C., Kocabas,B., & Zgedik,A.O. (2004). Experimental and theoretical investigation of workpiece surface profiles in electrical discharge machining (EDM). Journal of the Faculty of Engineering and Architecture of Gazi University, Vol. 19, pp.97–106.
  11. Deepak Kumar, P., & Rajat Kumar, B. (2008). Study of Surface Damage due to the Thermal Stresses in Electro Discharge Machining. Competitive Manufacturing, Proc. of 2nd International & 23rd AIMTDR Conf., IITMadras, Chennai, India, pp.741 – 746.
  12. Kuriakose, S., & Shunmugam, M.S., (2005). Multi objective optimization of wire EDM process by non- dominating sorting genetic algorithm. Journal of Materials Processing Technology, Vol. 170, pp.133-141.
  13. Lin, J.L., & Lin, C.L.,(2005). The use of Grey-Fuzzy Logic for the Optimisation of the Manufacturing Process. Journal of Materials Processing Technology, Vol. 160, pp.9-14.
  14. Oguzhan Yilmiz., (2006) .A user friendly fuzzy based system for the selection of EDM process parameters. Journal of Materials Processing Technology, Vol. 172, pp. 363-371.
  15. Rao, P.S., Prasad, K.E., & Reddy, B.S. (2011) Fuzzy modelling for electrical discharge machining of aluminium alloy. International Journal of Research and Reviews in Applied Sciences, Vol. 9, pp. 112-125.
  16. Kuo-Ming Tsai., & Pei-Jen Wang.,(2001). Comparisons of Neural Network Models on Material Removal Rate in Electrical Discharging Machining. Journal of Materials Processing Technology, Vol. 117, pp. 111-124.
  17. Panda, D.K., & Bhogi, R.K.(2005). ANN prediction of material removal rate in EDM. Materials and Manufacturing process, Vol. 20, pp.645-672.
  18. Gopal,I., & Rajurkar,K.P. (1992). Arti?cial Neural Network approach inmodelling of EDM process. Intelligent Engineering SystemsThrough Arti?cial Neural Networks, Vol. 2, pp.845–850.
  19. Caydas, U., Hascal?k, A., & Ekici, A. (2009) . An adaptive neuro model for wire-EDM. Expert Systems with Applications, Vol. 36, pp. 6135
  20. Probir Saha.,(2008). Soft computing models based prediction of cutting speed and surface roughness in wire EDM of Tungsten carbide cobalt composite. International Journal of Advanced Manufacturing Technology, Vol. 39(1-2), pp.74-84.
  21. Kuo-Ming Tsai., & Pei-jen Wang.,(2001). Predictions on surface finish in electrical discharge machining based upon neural network models. International Journal of Machine Tools and Manufacture, Vol. 41, pp. 1385-1403.
  22. Joshi, S.N., & Pande,S. S.,(2007). Integrated process modeling of EDM using FEM and ANN. Proceedings of international Conference on Computer Aided Engineering, IIT Madras, Chennai, pp.552-559.
  23. Mohan Kumar Pradhan., & Biswas, C.K.,(2008). Competitive Manufacturing. Proc. of 2nd International & 23rd AIMTDR Conf., IITMadras, Chennai, India, pp. 469 - 474,.
  24. Tsai,K.M. & Wang,P.J. (2001). Comparisons of neural network models on material removal rate in electrical discharge machining. Journal of Materials Processing Technology,Vol. 17, pp.111–124.
  25. Pradhan, M.K., Das, R., & Biswas, C.K., (2009). Comparisons of neural network models on surface roughness in electrical discharge machining. Journal of Engineering Manufacture, Vol. 223, pp.801-808.
  26. Wang,K., Gelgele,H.L., Wang,Y., Yuan,Q., & Fang,M. (2003). A hybrid intelligent method for modeling the EDM process. International Journal of Machine Tools & Manufacture, Vol. 43 pp.995–999.
  27. Mandal, D., (2007). Modeling of EDM using BPNN and multi- objective optimization using non dominating sorting Algorithm-II. Journal of Material Processing Technology, Vol. 186(1-3),pp. 154-162.
  28. Panda,D.K., & Bhoi,R.K.(2009). Arti?cial neural network prediction of material removal rate in electro discharge machining. Materials and Manufacturing Processes, Vol. 20,pp. 645–672.
  29. Agrawal, A., Dubey, A. K., & Shrivastava, P.K.(2013). Modeling and optimization of tool wear rate in powder mixed EDM of MMC. 2nd International conference on Mechanical and Robotics Engineering (ICMRE2013), Pattaya (Thailand), pp.1-6.
  30. Joshi, S.N., & Pande, S.S., (2008). Intelligent Process Modeling and Optimisation of EDM. Competitive Manufacturing, Proceedlngs of 2nd International & 23rd AIMTDR Conference, IITMadras, Chennai, India, pp. 475 – 480.
  31. Ramezan Ali MahdaviNejad., (2011). Modeling and optimization of Electrical Discharge Machining of SiC parameters, using Neural Networks and Non-Dominating Sorting Genetic Algorithm. Material Science and Applications, Vol. 2, pp.669-675.
  32. Bharti, P.S., Maheshwari, S., & Sharma, C. (2012). Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II. Journal of Mechanical Science and Technology, Vol. 26(6), pp.1875-1883.
  33. Amit Kumar Pal., Simul, B & Laha Dipak. (2008) . Competitive Manufacturing. Proceedings of 2nd International & 23rd AIMTDR Conference, IITMadras, Chennai, India, pp. 481- 487.
  34. Wang,K., Gelgele,H.L., Wang,Y., Yuan,Q., & Fang,M. (2003) . A hybrid intelligent method for modelling the EDM process. International Journal of Machine Tools and Manufacture, Vol. 43, pp. 995–999.
  35. Esme,U., A.Sagbas,A., & Kahraman,F. (2009). Prediction of Surface Roughness in Wire Electrical Discharge Machining using Design of Experiments and Neural Networks. Iranian Journal of Science and Technology, Transaction-B Engineering, Vol. 33(B3), pp. 231-240.
  36. Tsai,K.M., & Wang,P.J. (2001). Predictions on surface ?nish in electrical discharge machining based upon neural network models. International Journal of Machine Tools & Manufacture, Vol. 41, pp.1385–1403.
  37. Kansal, H. K., Singh, S., & Kumar, P.,(2005). Parametric Optimisation of Powder Mixed Electrical Discharge Machining by Response Surface Methodology. Journal of Materials Processing Technology, Vol. 169, pp. 427 – 436.
  38. Pradhan,M.K., & Biswas,C.K. (2009). Modeling and analysis of process parameters on surface roughness in EDM of AISI D2 tool steel by RSM approach. International Journal of Mathematical, Physical and Engineering Sciences, Vol. 3(1), pp. 66-71.
  39. Assarzadeh,S., & Ghoreishi,M., (2013). Statistical modeling and parametric optimization of process parameters in electro-discharge machining of cobalt-bonded tungsten carbide composite (WC/6%Co). Elsevier Procedia CIRP, Vol. 6, pp.463-468.
  40. Pragya Shandilya., Jain,P.K., & Jain,N.K. (2012). Parametric optimization during wire electric discharge machining using response surface methodology. Elsevier Procedia Engineering, Vol.38, pp.2371- 2377.
  41. Vats,U.K., & Singh,N.K. (2013). Optimization of surface roughness process parameters of Electrical discharge machining of EN-31 by response surface methodology. International Journal of Engineering Research and Technology. Vol. 6 (6) , pp.835-840.
  42. Ko-Ta Chiang.,(2008). Modelling and analysis of the effects of machining parameters on the performance characteristics in the EDM process of Al2O3+TiC mixes ceramic. International Journal of Advanced Manufacturing Technology , Vol.37, pp.523-533.
  43. Md. Ashikur Rahman Khan., Rahman,M.M., Kadigrama,K., Maleque,M.A., & Ishaq,M. (2011). Prediction of surface roughness of TI-6Al-4V in electrical discharge machining: A regression model. Journal of Mechanical Engineering and Sciences, Vol. I, pp.16-24.
  44. Pragya Shandilya., Jain,P.K., & Jain,N.K. (2012). Parametric optimization during wire electric discharge machining using response surface methodology. Elsevier Procedia Engineering, Vol.38, pp.2371- 2377.
  45. Puri,A.B., & Bhattacharyya,B. (2005). Modeling and analysis of white layerdepth in a wire-cut EDM process through response surface methodology. International Journal of Advanced ManufacturingTechnology, Vol.25, pp.301–307.
  46. Rajesh, R., & Devanand,M. (2012). Determination of a optimum Parametric combination using Surface Roughness in EDM process through response methodology. International Research Journal of Engineering Science,Technology and Innovation, Vol.4. pp.142-151.
  47. Pradhan,M.K., & Biswas,C.K. (2009). Modeling and analysis of process parameters on surface roughness in EDM of AISI D2 tool steel by RSM approach. International Journal of Mathematical, Physical and Engineering Sciences, Vol. 3(1), pp. 66-71.
  48. Rajmohan, T., Prabhu, R., Subba Rao, G., & Palanikumar, K.,(2012). Optimization of machining parameters in Elecrical Discharge Machining (EDM) of 304 stainless steel. Elsevier Procedia Engineering, Vol.38, pp.1030-1036.
  49. Swarup S. Mahapatra., & Amar Patnaik.(2006). Parametric Optimization of Wire electric discharge machining (WEDM) process using Taguchi Method. Journal of Brazil Society of Mechanical Science & Engineering, Vol. 28(4), pp.422-429.
  50. Liao, H.C.,(2006). Mult–response optimization using weighted Principal component. International Journal of Advanced Manufacturing Technology, Vol.27, pp.720 -725.
  51. Tzeng., Yith., Chen., & Fu.(2007). Multi Objective Optimisation of High Speed Electrical Discharge Machining Process using a Taguchi Fuzzy based Approach. Materials and Design, Vol. 28, pp. 1159 – 1168.
  52. Srinivasa Rao, P., Ramji, K., & Satyanarayana, B., (2011). Effect of WEDM Conditions on Surface Roughness: A parametric Optimisation using Taguchi Method. International Journal of Advanced Engineering Sciences & Technologies, Vol. 6(1), pp. 041 – 048.
  53. Warrier Ashish,M., George,P.M., Raghunath,B.K., & Manocha,L.M. (2004). EDM machining of carbon–carbon composite – A Taguchi approach. Journal of Material Processing and Technology, Vol.145, pp.66–71.
  54. Kumar,K., & Ravikumar,R. (2013). Modeling and optimization of wire EDM process. International Journal of Modern Engineering Research, Vol. 3 ( 3), pp. 1645-1648.
  55. Lajis,M.A., Radzi,H.C.D.M., & Amin,A.K.M.N. (2009). The implementation of Taguchi method on EDM process of tungsten carbide. European Journal of Scientific Research, Vol.26, pp.609–617.
  56. Ramakrishnan, R., & Karunamoorthy, L., (2006). Multi response optimization of WEDM operations using robust design. International Journal of Advanced Manufacturing Technology, Vol.29, pp.105-112.
  57. Vamsi Krishna., Surendra Babu Battula., & Swapna, M., (2010). Optimizing surface finish in WEDM using the Taguchi parameter design method. Journal of the Brazil Society of Mechanical Science & Engineering, Vol.XXXII( 2), pp. 107-113.
  58. Rajmohan T., Prabhu R., Subba Rao G., & Palanikumar K., (2012). Optimization of machining parameters in Elecrical Discharge Machining (EDM) of 304 stainless steel. Procedia Engineering, Vol.38, pp.1030-1036.
  59. Petropoulos, G., (2004). Modeling of surface finish in electro - discharge machining based upon statistical multi- parameter analysis. Journal of Material Processing Technology, Vol.155-156, pp.1247-1251.
  60. Puertas, I., Luis, C. J., & Álvarez, L., (2004). Analysis of the influence of EDM parameters on surface quality, MRR and EW of WC-Co. Journal of Material Processing Technology, Vol.153-154, pp.1026-1032.
  61. Yuan, J., Wang. K., Tao, Y. & Fang. M., (2008). Reliable Multi-Objective Optimization of High-speed WEDM Process based on Gaussian Process Regression. International Journal of Machine Tools and Manufacture, Vol. 48, pp. 47- 60.
  62. Liu, N. M., Chiang, K. T., Horng, J. T., & Chen, C. C., (2010). Modeling and analysis of the edge disintegration in the EDM drilling cobalt-bonded tungsten carbide. International Journal of Advanced Manufacturing Technology, Vol.51, pp.587-598. 
  63. Chin- Teng Lin., I-Fang Chung., & Shih- Yu Huang. (2001). Improvement of machining accuracy by fuzzy logic at corner parts for wire-EDM. Fuzzy Sets and Systems, Vol. 122, pp.499-511.
  64. Muthu Kumar, V., Suresh Babu, A., Venkata Swamy, R., & Raajenthiren, M. (2010). Optimization of the WEDM Parameters on Machining Incoloy800 Super Alloy with Multiple Quality Characteristics. International Journal of Engineering Science and Technology, Vol. 2(6), pp 1538-1547.
  65. Dhar, S., Purohit, R., Saini, N., Sharma, A. and Kumar, G.H., (2007). Mathematical modeling of electric discharge machining of cast Al–4Cu–6Si alloy–10 wt.% SiCP composites. Journal of materials processing technology, Vol.194(1-3), pp.24-29.
  66. Tariq Jilani,S., & Pandey,P..C (1982). Analysis and modeling of EDM parameters. Precision Engineering, Vol.4, pp.215–221.
  67. Izquierdo,B., (2009). A numerical model of the EDM process considering the effect of multiple discharges. International Journal of Machine Tools & Manufacture, Vol. 49, pp.220–229.
  68. Pandit,S.M., & Rajurkar,K.P. (1983). Stochastic approach to thermal modeling applied to electro-discharge machining. Journal of Heat Transfer, Vol.105, pp.555–562.
  69. Rajurkar,K.P., & Wang,W.M. (1983).Thermal modeling and on-line monitoring of wire-EDM. Journal of Materials Processing Technology , Vol.38 , pp.417–430.

Downloads

Published

2017-03-30

Issue

Section

Research Articles

How to Cite

[1]
M. Mahalingam, " Modeling and Optimization of EDM Process Parameters : A Review, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 3, Issue 3, pp.694-703, March-April-2017.