SpringerLink
Log in

Optimization of Milling Process Parameters Based on Real Coded Self-adaptive Genetic Algorithm and Grey Relation Analysis

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10464))

Included in the following conference series:

  • 4916 Accesses

Abstract

In this paper, a method to optimize the milling process parameters based on the real-coded self-adaptive genetic algorithm (RAGA) and Grey relational analysis (GRA) is proposed. Experiments have been designed with four input milling process parameters at four different levels. The RAGA coupled with GRA has been applied for solving the proposed optimization problem to achieve the desired machined surface quality characteristics. Simulation experiments give the optimal parametric combination. Furthermore, experiments for the machined surface topography with the initial and optimal combination of milling process parameters are implemented and the results verify the feasibility of the proposed method.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Tönshoff, H.K., Arendt, C., Amor, R.B.: Cutting of hardened steel. CIRP Ann. Manufact. Technol. 49(2), 547–566 (2000)

    Article  Google Scholar 

  2. Benardos, P.G., Vosniakos, G.C.: Prediction of surface roughness in CNC face milling using neural networks and taguchi’s design of experiments. Robot. Comput. Integr. Manufact. 18(5), 343–354 (2002)

    Article  Google Scholar 

  3. Zhang, J.Z., Chen, J.C., Kirby, E.D.: Surface roughness optimization in an end-milling operation using the Taguchi design method. J. Mater. Process. Technol. 184(1), 233–239 (2007)

    Article  Google Scholar 

  4. Sabanayagam, A., Kumar, V.A., Raju, S., Kumar, N.S.: Optimization of interconnects for signal integrity using self-adaptation in real-parameter genetic algorithms with simulated binary cross over (SBX). In: 10th International Conference on Electromagnetic Interference & Compatibility, pp. 123–129. IEEE, India (2008)

    Google Scholar 

  5. Gong, C., Xu, C., Wang, J.: An efficient adaptive real coded genetic algorithm to solve the portfolio choice problem under cumulative prospect theory. Comput. Econ. 1–26 (2017). doi:10.1007/s10614-017-9669-5,

  6. Lee, L.H., Fan, Y.: An adaptive real-coded genetic algorithm. Appl. Artif. Intell. 16(6), 457–486 (2002)

    Article  Google Scholar 

  7. Subbaraj, P., Rajnarayanan, P.N.: Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electr. Power Syst. Res. 79(2), 374–381 (2009)

    Article  Google Scholar 

  8. Subbaraj, P., Rengaraj, R., Salivahanan, S.: Enhancement of self-adaptive real-coded genetic algorithm using Taguchi method for economic dispatch problem. Appl. Soft Comput. 11(1), 83–92 (2011)

    Article  Google Scholar 

  9. Abbas, H.M., Bayoumi, M.M.: Volterra-system identification using adaptive real-coded genetic algorithm. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 36(4), 671–684 (2006)

    Article  Google Scholar 

  10. Oyama, A., Obayashi, S., Nakahashi, K.: Wing design using real-coded adaptive range genetic algorithm. In: 1999 IEEE International Conference on Systems, Man, and Cybernetics, pp. 475–480. IEEE, Japan (1999)

    Google Scholar 

  11. Deng, J.L.: Introduction to Grey system theory. J. Grey Syst. 1(1), 1–24 (1989)

    MathSciNet  MATH  Google Scholar 

  12. Deng, J.L.: A Course on Grey System Theory. Huazhong University of Science and Technology Press, Wuhan (1990)

    Google Scholar 

  13. Tzeng, C.J., Lin, Y.H., Yang, Y.K., Jeng, M.C.: Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis. J. Mater. Process. Technol. 209(6), 2753–2759 (2009)

    Article  Google Scholar 

  14. Yang, Y.S., Huang, W.: A Grey-fuzzy Taguchi approach for optimizing multi-objective properties of zirconium-containing diamond-like carbon coatings. Expert Syst. Appl. 39(1), 743–750 (2012)

    Article  Google Scholar 

  15. Wang, L., Tang, D.: An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem. Expert Syst. Appl. 38(6), 7243–7250 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 51505343) and the Postdoctoral Science Foundation of China (Grant No. 2015M572192).

Author information

Authors and Affiliations

  1. Hubei Key Laboratory of Waterjet Theory and New Technology, School of Power and Mechanical Engineering, Wuhan University, Wuhan, 430072, China

    Shasha Zeng

  2. Mechanical and Electrical Engineering College, Hainan University, Haikou, 570228, China

    Lei Yuan

Authors
  1. Shasha Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shasha Zeng .

Editor information

Editors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    YongAn Huang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Hao Wu

  3. Institute of Industrial Research, University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Zhou** Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Zeng, S., Yuan, L. (2017). Optimization of Milling Process Parameters Based on Real Coded Self-adaptive Genetic Algorithm and Grey Relation Analysis. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10464. Springer, Cham. https://doi.org/10.1007/978-3-319-65298-6_77

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65298-6_77

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65297-9

  • Online ISBN: 978-3-319-65298-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation