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A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys

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Abstract

In the present study, electric discharge machining process was used for machining of titanium alloys. Eight process parameters were varied during the process. Experimental results showed that current and pulse-on-time significantly affected the performance characteristics. Artificial neural network coupled with Taguchi approach was applied for optimization and prediction of surface roughness. The experimental results and the predicted results showed good agreement. SEM was used to investigate the surface integrity. Analysis for migration of different chemical elements and formation of compounds on the surface was performed using EDS and XRD pattern. The results showed that high discharge energy caused surface defects such as cracks, craters, thick recast layer, micro pores, pin holes, residual stresses and debris. Also, migration of chemical elements both from electrode and dielectric media were observed during EDS analysis. Presence of carbon was seen on the machined surface. XRD results showed formation of titanium carbide compound which precipitated on the machined surface.

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References

  1. A. Hascalik and U. Caydas, Electric discharge machining of titanium alloy (Ti-6Al-4V), Applied Surface Scence, 253 (2007a) 9007–9016.

    Article  Google Scholar 

  2. E. O. Ezugwu and Z. M. Wang, Titanium alloys and their machinability- a review, Journal of Materials Processing Technology, 68 (1997) 262–274.

    Article  Google Scholar 

  3. S. Pervaiz, I. Deiab and B. Darras, Power consumption and tool wear assement when machining titanium alloys, International Journal of Precision Engineering Manufacturer, 14 (6) (2013) 925–936.

    Article  Google Scholar 

  4. D. A. Dornfeld, J. S. Kim, H. Dechow, J. Hewsow and L. J. Chen, Drilling burr formation in titanium alloy Ti-6Al-4V, Annals of CIRP., 48 (1999) 73–76.

    Article  Google Scholar 

  5. M. A. Norliana, D. G. Solomon and Md. F. Bahari, Areview on current research trends in electrical discharge machining (EDM), International Journal of Machine Tools & Manufacture, 47 (2007) 1214–1228.

    Article  Google Scholar 

  6. B. H. Yoo, B. K. Min and J. S. Lee, Analysis of the machining characteristics of EDM as functions of the mobilities of electrons and ions, International Journal of Precision Engineering Manufacturer, 11 (4) (2010) 629–632.

    Article  Google Scholar 

  7. K. H. Ho and S. T. Newman, State of the art electrical discharge machining, International Journal of Machine Tools & Manufacture, 43 (2003) 1287–1300.

    Article  Google Scholar 

  8. S. S. Gill and J. Singh, Effect of deep cryogenic treatment on machinability of titanium alloy (Ti-6246) in electric discharge drilling, Materials and Manufacturing Processes, 25 (2010) 378–385.

    Article  Google Scholar 

  9. S. Abdulkareem, A. A. Khan and M. Konneh, Reducing electrode wear ratio using cryogenic cooling during electrical discharge machining, International Journal of Advanced Manufacturing Technology, 45 (2009) 1146–1151.

    Article  Google Scholar 

  10. K. A. Venugopal, S. Paul and A. B. Chattopadhyay, Tool wear in cryogenic turning of Ti-6Al-4V alloy, Cryogenics, 47 (2007) 12–18.

    Article  Google Scholar 

  11. J. S. Soni and G. Chakraverti, Machining characteristics of titanium with rotary electro-discharge machining, Wear, 171 (1994) 51–58.

    Article  Google Scholar 

  12. R. K. Jain and V. K. Jain, Optimum selection of machining conditions in abrasive flow using neural network, Journal of Materials Processing Technology, 108 (2000) 62–67.

    Article  Google Scholar 

  13. K. Wang, H. L. Gelgele, Y. Wang, Q. Yuan and M. Fang, hybrid intelligent method for modelling the EDM process, International Journal of Machine Tools & Manufacture, 43 (2003) 995–999.

    Article  Google Scholar 

  14. P. S. Bharti, S. Maheshwari and C. Sharma, Multi-objecttive optimization of electric-discharge machining process using controlled elitist NSGA-II, Journal of Mechanical Science and Technology, 26 (6) (2012) 1875–1883.

    Article  Google Scholar 

  15. J. Y. Kao and Y. S. Tarng, A neutral-network approach for the online monitoring of the electrical discharge machining process, Journal of Materials Processing Technology, 69 (1–3) (1997) 112–119.

    Article  Google Scholar 

  16. Fenggou and Y. Dayong, The study of high efficiency and intelligent optimization system in EDM sinking process, Journal of Materials Processing Technology, 149 (1–3) (2004) 83–87.

    Article  Google Scholar 

  17. [17] Fenggou and Q. Zhang, Neural network modeling and parameters optimization of increased explosive electric discharge grinding process for large area polycrystalline diamond, Journal of Materials Processing Technology, 149 (2004) 106–111.

    Article  Google Scholar 

  18. Tzeng and F. Chen, Multi-objective optimisation of highspeed electrical discharge machining process using a Taguchi fuzzy-based approach, Materials and Design, 28 (2007) 1159–1168.

    Article  Google Scholar 

  19. S. Assarzadeh and M. Ghoreishi, Neural-network-based modeling and optimization of the electro-discharge machining process, International Journal of Advanced Manufacturing Technology, 39 (2008) 488–500.

    Article  Google Scholar 

  20. A. P. Markopoulos, D. E. Manolakos and N. M. Vaxevanidis, Artificial neural network models for the prediction of surface roughness in electrical discharge machining, J. Intell Manuf., 19 (2008) 283–292.

    Article  Google Scholar 

  21. P. Fonda, Z. Wang, K. Yamazaki and Y. Akutsu, A fundamental study on Ti-6Al-4V’s thermal and electrical properties and their relation to EDM productivity, Journal of Materials Processing Technology, 202 (2008) 583–589.

    Article  Google Scholar 

  22. G. Rao, G. Krishna Mohana, D. Rangajanardhaa, H. Rao and M. S. Rao, Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm, Journal of Materials Processing Technology, 209 (2009) 1512–1520.

    Article  Google Scholar 

  23. M. M. Rahman, Md. Khan, A. Rahman, K. Kadirgama, M. M. Noor and R. A. Bakar, Modeling of material removal on machining of Ti-6Al-4V through EDM using copper tungsten electrode and positive polarity, International Journal of Mechanical Material Engineering, 1 (3) (2010) 135–140.

    Google Scholar 

  24. P. K. Patowari, P. Saha and P. K. Mishra, Artificial neural network model in surface modification by EDM using tungsten-copper powder metallurgy sintered electrodes, International Journal of Advanced Manufacturing Technology, 51 (2010) 627–638.

    Article  Google Scholar 

  25. C. J. Tzeng and R. Y. Chen, Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach, International Journal of Precision Engineering Manufacturer, 14 (5) (2013) 709–717.

    Article  Google Scholar 

  26. T. T. Nguyen, Y. S. Yang and J. W. Kim, An artificial neural network system for predicting the deformation of steel plate in triangle induction heating process, International Journal of Precision Engineering Manufacturer, 14 (4) (2013) 551–557.

    Article  Google Scholar 

  27. S. S. Sidhu, A. Batish and S. Kumar, Neural-network-based modeling to predict residual stresses during electric discharge machining of Al/SiC-MMCs., Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture (2013) doi: 10.7/0954405413492505.

    Google Scholar 

  28. M. M. Rahman, Modeling of machining parameters of Ti-6Al-4V for electric discharge machining: A neural network approach, Scientific Research and Essays, 7 (8) (2012) 881–890.

    Google Scholar 

  29. J. Y. Kao, C. C. Tsao, S. S. Wang and C. Y. Hsu, Optimization of the EDM parameters on machining Ti-6Al-4V with multiple characteristics, International Journal of Advanced Manufacturing Technology, 47 (2010) 395–402.

    Article  Google Scholar 

  30. P. C. Tan, S. H. Yeo and Y. V Tan, Effects of nano powder additives in micro electrical discharge machining, International Journal of Precision Engineering Manufacturer, 9 (3) (2008) 22–26.

    Google Scholar 

  31. R. N. Yadav and V. Yadava, Experimental study of erosion and abrasion based hybrid machining of hybrid metal matrix composite, International Journal of Precision Engineering Manufacturer, 14 (8) (2013) 1293–1299.

    Article  Google Scholar 

  32. B. H. Yan, H. C. Tsai and F. Y. Huang, The effect in EDM of a dielectric of a urea solution in water on modifying the surface of titanium, International Journal of Machine Tools & Manufacture, 45 (2005) 194–200.

    Article  Google Scholar 

  33. B. Jabbaripour, M. H. Sadeghi, S. Faridvand and M. R. Shabgard, Investigating the effects of EDM parameters on surface integrity, MRR and TWR in Machining of Ti-6Al-4V, Machining Science and Technology: AnInternational Journal, 16 (2012) 419–444.

    Article  Google Scholar 

  34. A. Hascalik and U. Caydas, A comparative study of surface integrity of Ti-6Al-4V alloy machined by EDM and AECG, Journal of Materials Processing Technology, 190 (2007b) 173–180.

    Article  Google Scholar 

  35. S. Kalia, Cryogenic processing: a study of materials at low temperatures, Journal of Low Temperature Physics, 158 (2010) 934–945.

    Article  Google Scholar 

  36. V. Srivastava and M. P. Pandey, Effect of process parameters on the performance of EDM process with ultrasonic assisted cryogenically cooled electrode, Journal of Manufacturing Processes, 14 (3) (2012) 393–402.

    Article  Google Scholar 

  37. H. T. Lee and T. Y. Tai, Relationship between EDM parameters and surface crack formation, Journal of Materials Processing Technology, 142 (3) (2003) 676–683.

    Article  Google Scholar 

  38. P. J. Ross, Taguchi techniques for quality engineering, USA 2nd ed., McGraw hill New York (1996).

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Thapar University, Patiala, 147004, Punjab, India

    Sanjeev Kumar & Ajay Batish

  2. Department of Production Engineering, GNDEC, Ludhiana, 141006, Punjab, India

    Rupinder Singh

  3. Symbiosis Institute of Technology, Pune, 411042, Maharashtra, India

    T. P. Singh

Authors
  1. Sanjeev Kumar
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  2. Ajay Batish
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  3. Rupinder Singh
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  4. T. P. Singh
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Corresponding author

Correspondence to Ajay Batish.

Additional information

Recommended by Associate Editor Sung Hoon Ahn

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Kumar, S., Batish, A., Singh, R. et al. A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys. J Mech Sci Technol 28, 2831–2844 (2014). https://doi.org/10.1007/s12206-014-0637-x

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  • DOI: https://doi.org/10.1007/s12206-014-0637-x

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