Studies on micro-EDM surface performance using a comprehensive method

  • Wujun Feng
  • Xuyang Chu
  • Yongqiang Hong
  • Kai Wang
  • Li Zhang


The surface performances of aerospace materials machined by micro-EDM directly affect the reliability of aeronautical components. However, there are two main problems that must be solved by research in this field. The first is that even if all the machining parameters are kept the same, different materials, pulse generators, and micro-EDM machining types also affect the surface performance. The second problem is that traditional surface evaluation parameters cannot reflect the actual situation accurately. However, research on the surface performance is always a small part of a larger study, and usually not systematic enough. In this study, a systematic investigation of surface performance was conducted using a comprehensive method. A novel mathematical model that combines support vector machine with a multi-objective genetic algorithm was established. Three materials, two pulse generators, three machining types, and various machining parameters were used as inputs to this new model. From this, new evaluation parameters of surface performance, such as the fractal dimension, recast layer thickness, and surface hardness, were generated to use as output parameters. Afterwards, relevant experiments that matched the model input parameters were conducted for comparison. Based on the comprehensive method, the comparative results indicated that the errors between the predicted and experimental values were less than 7%. The developed mechanism based on the predicted and experimental results is discussed in depth in this report, and suggestions on how to utilize this information to machine components with improved surface performance are proposed.


Micro-EDM Surface performance Support vector machine Genetic algorithm Fractal dimension Recast layer thickness Surface hardness 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


Funding information

This work was supported by the National Natural Science Foundation of China for Young Scientists under Grant No.51505400 and the National Natural Science Foundation of China under Grant No. 51676085.

Supplementary material

170_2018_1711_MOESM1_ESM.pdf (8 mb)
ESM 1 (PDF 8160 kb)


  1. 1.
    Chu XY, Zhu K, Wang CM, Hu ZP, Zhang YR (2016) A study on plasma channel expansion in micro-EDM. Mater Manuf Process 31(4):381–390. CrossRefGoogle Scholar
  2. 2.
    Kuriachen B, Mathew J (2016) Spark radius modeling of resistance-capacitance pulse discharge in micro-electric discharge machining of Ti-6Al-4V: an experimental study. Int J Adv Manuf Technol 85(9–12):1983–1993. CrossRefGoogle Scholar
  3. 3.
    Feng XB, Wong YS, Hong GS (2016) Characterization and geometric modeling of single and overlapping craters in micro-EDM. Mach Sci Technol 20(1):79–98. CrossRefGoogle Scholar
  4. 4.
    Natarajan U, Suganthi XH, Periyanan PR (2016) Modeling and multiresponse optimization of quality characteristics for the micro-EDM drilling process. T Indian I Metals 69(9):1675–1686. CrossRefGoogle Scholar
  5. 5.
    Tong H, Li Y, Zhang L (2016) On-machine process of rough-and-finishing servo scanning EDM for 3D micro cavities. Int J Adv Manuf Tech 82(5–8):1007–1015. CrossRefGoogle Scholar
  6. 6.
    Tamang SK, Natarajan N, Chandrasekaran M (2017) Optimization of EDM process in machining micro holes for improvement of hole quality. J Braz Soc Mech Sci 39(4):1277–1287. CrossRefGoogle Scholar
  7. 7.
    Geng XS, Chi GX, Wang YK, Wang ZL (2014) Study on microrotating structure using microwire electrical discharge machining. Mater Manuf Process 29(3):274–280. CrossRefGoogle Scholar
  8. 8.
    Manivannan R, Kumar MP (2016) Multi-response optimization of micro-EDM process parameters on AISI304 steel using TOPSIS. J Mech Sci Technol 30(1):137–144. CrossRefGoogle Scholar
  9. 9.
    Li L, Bi FQ, Feng L, Bai X (2016) Effect of low-temperature gas-cooled electrode on the performance of EDM process. Int J Adv Manuf Tech 86(1–4):717–722. CrossRefGoogle Scholar
  10. 10.
    Li Y, Deng JX, Chai YS, Fan WL (2016) Surface textures on cemented carbide cutting tools by micro EDM assisted with high-frequency vibration. Int J Adv Manuf Tech 82(9–12):2157–2165. CrossRefGoogle Scholar
  11. 11.
    Puthumana G, Bissacco G, Hansen HN (2017) Modeling of the effect of tool wear per discharge estimation error on the depth of machined cavities in micro-EDM milling. Int J Adv Manuf Tech 92(9–12):3253–3264. CrossRefGoogle Scholar
  12. 12.
    Zhang Z, Cui H, Ding H, Guo L (2011) The reference plane by wavelets for 3D roughness evaluation of micro wire electrical discharge machining (MWEDM). Harbin Gongcheng Daxue Xuebao/J Harbin Eng Univ 32(9):1185–1189. Google Scholar
  13. 13.
    Surleraux A, Pernot JP, Elkaseer A, Bigot S (2016) Iterative surface warping to shape craters in micro-EDM simulation. Eng Comput 32(3):517–531. CrossRefGoogle Scholar
  14. 14.
    Liu HZ, Wang ZL, Wang YK, Li HC (2017) Effect of technological parameters on the process performance of pure Al2O3 layer of Ni-Al2O3 FGMs by self-induced EDM. Int J Adv Manuf Tech 90(9–12):3633–3641. CrossRefGoogle Scholar
  15. 15.
    Shen Y, Liu YH, Dong H, Zhang K, Lv L, Zhang XZ, Wu XL, Zheng C, Ji RJ (2017) Surface integrity of Inconel 718 in high-speed electrical discharge machining milling using air dielectric. Int J Adv Manuf Tech 90(1–4):691–698. CrossRefGoogle Scholar
  16. 16.
    Xu B, Wu XY, Lei JG, Liang X, Zhao H, Guo DJ, Ruan SC (2017) Micro-ECM of 3D micro-electrode for efficiently processing 3D micro-structure. Int J Adv Manuf Tech 91(1–4):709–717. CrossRefGoogle Scholar
  17. 17.
    Alavi F, Jahan MP (2017) Optimization of process parameters in micro-EDM of Ti-6Al-4V based on full factorial design. Int J Adv Manuf Tech 92(1–4):167–187. CrossRefGoogle Scholar
  18. 18.
    D'Urso G, Quarto M, Ravasio C (2017) A model to predict manufacturing cost for micro-EDM drilling. Int J Adv Manuf Tech 91(5–8):2843–2853. CrossRefGoogle Scholar
  19. 19.
    Ming WY, Zhang Z, Wang SY, Huang H, Zhang YM, Zhang Y, Shen DL (2017) Investigating the energy distribution of workpiece and optimizing process parameters during the EDM of Al6061, Inconel718, and SKD11. Int J Adv Manuf Tech 92(9–12):4039–4056. CrossRefGoogle Scholar
  20. 20.
    Eisavi V, Homayouni S (2016) Performance evaluation of random forest and support vector regressions in natural hazard change detection. J Appl Remote Sens 10:14. CrossRefGoogle Scholar
  21. 21.
    Li LM, Tu YQ, Guo LJ, Sun LY, Tian YY (2016) Optimization and control of extractive distillation with heat integration for separating benzene/cyclohexane mixtures. China Pet Process Petrochem Technol 18(4):117–127Google Scholar
  22. 22.
    Feng WJ, Chu XY, Hong YQ, Deng DX (2017) Surface morphology analysis using fractal theory in micro electrical discharge machining. Mater Trans 58(3):433–441. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Wujun Feng
    • 1
  • Xuyang Chu
    • 1
  • Yongqiang Hong
    • 1
  • Kai Wang
    • 1
  • Li Zhang
    • 1
  1. 1.School of Aerospace EngineeringXiamen UniversityXiamenChina

Personalised recommendations