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Modelling and multi-response optimization of hole sinking electrical discharge micromachining of titanium alloy thin sheet

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Abstract

Thin sheets of titanium alloys are widely used in aerospace and automotive industries for specific applications. The creation of micro holes with requisite hole quality in thin sheets of these alloys using energy of electric discharge is a challenging task for manufacturing engineers. Hole sinking electrical discharge micromachining (HS-EDMM) is one of the most promising micromachining processes to create symmetrical and non-symmetrical micro holes. The present paper is related to selection of optimum parameter settings for obtaining maximum material removal, minimum tool wear and minimum hole taper in HS-EDMM. In this paper an attempt has been made to develop an integrated model (ANN-GRA-PCA) of single hidden layer back propagation neural network (BPNN) for prediction and grey relational analysis (GRA) coupled with principal component analysis (PCA) hybrid optimization strategy with multiple responses of HSEDMM of Ti-6Al-4V. Experiments have been conducted to generate dataset for training and testing of the network where input parameters consist of gap voltage, capacitance of capacitor and the resulting performance parameters are represented by material removal rate (MRR), tool wear rate (TWR), and hole taper (Ta). The results indicate that the integrated model is capable to predict and optimize process performance with reasonable accuracy under varied operating conditions of HS-EDMM. The proposed approach would be extendable to other configurations of EDMM processes for different materials.

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References

  1. V. K. Jain, Introduction to micromachining, Narosa Publication House Pvt. Ltd., India (2010).

    Google Scholar 

  2. Y. S. Wong, M. Rahman, H. S. Lim, H. Han and N. Ravi, Investigation of micro-EDM material removal characteristics using single RC-pulse discharges, Journal of Materials Processing Technology, 140(1–3) (2003) 303–307.

    Article  Google Scholar 

  3. Z. Wansheng, Y. Yang, W. Zhenlong and Z. Yong, A CAD/CAM system for micro-ED-milling of small 3D freeform cavity, Journal of Materials Processing Technology, 149(1–3) (2004) 573–578.

    Google Scholar 

  4. I. Yoshihito, N. Takayuki, M. Hidetaka, H. Hirofumi and T. Hitoshi, Local actuator module for highly accurate micro-EDM, Journal of Materials Processing Technology, 149(1–3) (2004) 328–333.

    Google Scholar 

  5. H. Fuzhu, S. Wachi and M. Kunieda, Improvement of machining characteristics of micro-EDM using transistor type isopulse generator and servo feed control, Journal of Precision Engineering, 28(4) (2004) 378–385.

    Article  Google Scholar 

  6. H. S. Liu, B. H. Yan, F. Y. Huang and K. H. Qiu, A study on the characterization of high nickel alloy micro-holes using micro-EDM and their applications, Journal of Material Processing Technology, 169(3) (2005) 418–426.

    Article  Google Scholar 

  7. S. Son, H. Lim, A. S. Kumar and M. Rahman, Influences of pulsed power condition on the machining properties in micro EDM, Journal of Materials Processing Technology, 190(1–3) (2007) 73–76.

    Article  Google Scholar 

  8. M. P. Jahan, Y. S. Wong and M. Rahman, A study on the fine-finish die-sinking micro-EDM of tungsten carbide using different electrode materials, Journal of Materials Processing Technology, 209(8) (2009) 3956–3967.

    Article  Google Scholar 

  9. E. Uhlmann, S. Piltz and U. Doll, Machining of micro/miniature dies and moulds by electrical discharge machining- Recent developments, Journal of Materials Processing Technology, 167(1) (2005) 488–499.

    Article  Google Scholar 

  10. Y. S. Tarng, S. C. Ma and L. K. Chung, Determination of optimal cutting parameters in wire electric discharge machining, International Journal of Machine Tools and Manufacture, 43(6) (1995) 995–999.

    Google Scholar 

  11. E. O. Ezugwu, D. A. Fadare, J. Bonney, R. B. Da Silva and W. F. Sales, Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network, International Journal of Machine Tools and Manufacture, 45(6–7) (2005) 1375–1385.

    Article  Google Scholar 

  12. P. Allen and X. Chen, Process simulation of micro electrodischarge machining on molybdenum, Journal of Materials Processing Technology, 186(1) (2007) 346–355.

    Article  Google Scholar 

  13. G. Indurkhya and K. P. Rajurkar, Artificial neural network approach in modeling of EDM process, Proc. Artificial neural networks in engineering, St. Louis, Missouri, USA (1992) 845–850.

    Google Scholar 

  14. A. P. Markopoulos, D. E. Manolakos and N. M. Vaxevanidis, Artificial neural network models for the prediction of surface roughness in EDM, Journal of Intelligent Manufacturing, 19(1) (2008) 283–292.

    Article  Google Scholar 

  15. K. P. Somashekhar, J. Mathew and N. Ramachandran, Multi-objective optimization of micro wire electric discharge machining parameters using grey relational analysis with Taguchi method, Proc. IMechE Part C: Journal of Mechanical Engineering Science, 225(7–8) (2010) 1742–1753.

    Google Scholar 

  16. K. Wang, H. L. Gelgele, Y. Wang, Q. Yuan and M. Fang, A hybrid intelligent method for modelling the EDM process, International Journal of Machine Tools and Manufacture, 43(10) (2003) 995–999.

    Article  Google Scholar 

  17. Y. Y. Yang, J. R. Shie and C. H. Huang, Optimization of dry machining parameters for high purity graphite in endmilling process, International Journal of Material Manufacturing Process and Technology, 209(3) (2006) 832–837.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, India

    Rajesh Kumar Porwal & Vinod Yadava

  2. Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India

    J. Ramkumar

Authors
  1. Rajesh Kumar Porwal
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  2. Vinod Yadava
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  3. J. Ramkumar
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Correspondence to Rajesh Kumar Porwal.

Additional information

Recommended by Associate Editor In-Ha Sung

Rajesh Kumar Porwal received his B.E. and M.E. from SVNIT Surat, and BIT, Mesra, Ranchi, India, in 1998 and 2003, respectively. He is currently pursuing his Ph.D. in the Department of Mechanical Engineering, MNNIT, Allahabad, India. He is having eleven years of teaching and three years of industry experience. His research interests include advanced manufacturing processes, design of experiments and application of soft computing techniques in manufacturing.

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Porwal, R.K., Yadava, V. & Ramkumar, J. Modelling and multi-response optimization of hole sinking electrical discharge micromachining of titanium alloy thin sheet. J Mech Sci Technol 28, 653–661 (2014). https://doi.org/10.1007/s12206-013-1129-0

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  • DOI: https://doi.org/10.1007/s12206-013-1129-0

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