Skip to main content

Advertisement

SpringerLink
Log in

A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In micro-electrical discharge machining (EDM), processing parameters greatly affect processing efficiency and stability. However, the complexity of micro-EDM makes it difficult to determine optimal parameters for good processing performance. The important output objectives are processing time (PT) and electrode wear (EW). Since these parameters influence the output objectives in quite an opposite way, it is not easy to find an optimized combination of these processing parameters which make both PT and EW minimum. To solve this problem, supporting vector machine is adopted to establish a micro-EDM process model based on the orthogonal test. A new multi-objective optimization genetic algorithm (GA) based on the idea of non-dominated sorting is proposed to optimize the processing parameters. Experimental results demonstrate that the proposed multi-objective GA method is precise and effective in obtaining Pareto-optimal solutions of parameter settings. The optimized parameter combinations can greatly reduce PT while making EW relatively small. Therefore, the proposed method is suitable for parameter optimization of micro-EDM and can also enhance the efficiency and stability of the process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Jia ZY, Gu F, Wang FJ, Zhou M (2007) Parameter optimization of EDM micro-and-small holes based on signal-to-noise and grey relational grade. Chinese J Mech Eng 43(7):63–67, in Chinese

    Article  Google Scholar 

  2. Ho KH, Newman ST (2003) State of the art electrical discharge machining (EDM). Int J Mach Tools Manuf 43(13):1287–1300

    Article  Google Scholar 

  3. Pradhan MK, Das R, Biswas CK (2009) Comparisons of neural network models on surface roughness in electrical discharge machining. Proceedings of the institution of mechanical engineers, part B. J Eng Manuf 223(7):801–808

    Article  Google Scholar 

  4. Assarzadeh S, Ghoreishi M (2008) Neural-network-based modeling and optimization of the electro-discharge machining process. Int J Adv Manuf Technol 39(5–6):488–500

    Article  Google Scholar 

  5. Dhara SK, Kuar AS, Mitra S (2008) An artificial neural network approach on parametric optimization of laser micro-machining of die-steel. Int J Adv Manuf Technol 39:39–46

    Article  Google Scholar 

  6. Zhou M (2005) The methodology of discharging-state identification in micro-electrical discharging machining (micro-EDM). Dissertation, Dalian University of Technology (in Chinese)

  7. Singh PN, Raghukandan K, Pai BC (2004) Optimization by Grey relational analysis of EDM parameters on machining Al-10% SiCP composites. J Mater Process Technol 155–156(1–3):1658–1661

    Article  Google Scholar 

  8. Kuriakose S, Shunmugam MS (2005) Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm. J Mater Process Technol 170(1–2):133–141

    Article  Google Scholar 

  9. Mandal D, Pal SK, Saha P (2007) Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. J Mater Process Technol 186(1–3):154–162

    Article  Google Scholar 

  10. Rao SS (1991) Optimization theory and application. Wiley, New Delhi

    Google Scholar 

  11. Ramesh R, Ravi Kumar KS, Anil G (2009) Automated intelligent manufacturing system for surface finish control in CNC milling using support vector machines. Int J Adv Manuf Technol 42:1103–1117

    Article  Google Scholar 

  12. Vapnik V (1998) Statistical learning theory. Wiley Interscience, New York

    MATH  Google Scholar 

  13. Zhao CY, Zhang HX, Zhang XY, Liu MC, Hu ZD, Fan BT (2006) Application of support vector machine (SVM) for prediction toxic activity of different data sets. Toxicology 217(2–3):105–119

    Article  Google Scholar 

  14. Duric PM (1990) Model selection by cross-validation. IEEE ISCAS 4:2760–2763

    Google Scholar 

  15. Lela B, Bajić D, Jozić S (2009) Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling. Int J Adv Manuf Technol 42:1082–1088

    Article  Google Scholar 

  16. Tang HW, Qin XZ (2004) Practical methods of optimization. Press of Dalian University of Technology, Dalian

    Google Scholar 

  17. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  18. Kanagarajan D, Karthikeyan R, Palanikumar K, Davim JP (2008) Optimization of electrical discharge machining characteristics of WC/Co composites using non-dominated sorting genetic algorithm (NSGA-II). Int J Adv Manuf Technol 36:1124–1132

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian, 116024, China

    Lingxuan Zhang, Zhenyuan Jia, Fuji Wang & Wei Liu

Authors
  1. Lingxuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenyuan Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fuji Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenyuan Jia.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, L., Jia, Z., Wang, F. et al. A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM. Int J Adv Manuf Technol 51, 575–586 (2010). https://doi.org/10.1007/s00170-010-2623-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-010-2623-5

Keywords

Search

Navigation