Skip to main content
SpringerLink
Log in

Assessment of the dynamical properties in EDM process—detecting deterministic nonlinearity of EDM process

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

Abstract

Time series of gap state were often used as feedback signal in electrical discharge machining (EDM) adaptive control systems. However, models precisely describing the EDM process have never been built because of the once believed stochastic nature of the EDM process. In this case, the power of adaptive controls in EDM had not been fully brought into play. Before building a feasible model, it is prerequisite to determine whether an efficient stable EDM process is nonlinear or linear, deterministic or stochastic. The main purpose of this paper is to investigate the deterministic nonlinearity of the process. A discriminating method was first provided to judge states in the gap at sampling intervals from voltage and current. Gap state was then statistically quantified from a train of discriminated states at sampling intervals within a specified period of time. Based on a time series of gap state data, we took use of surrogate data method to detect the nonlinearity of the process. From the results of two kinds of tests, it can be concluded that the deterministic nonlinearity of the process reflected by gap states is intrinsic.

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. Ishida T, Takeuchi Y (2001) L-shaped curved hole creation by means of electrical discharge machining and an electrode curved motion generator. Int J Adv Manuf Technol 19(4):260–265. doi:10.1007/s001700200032

    Article  Google Scholar 

  2. Sanchez JA, Cabanes I, Lopez de Lacalle LN, Iamikiz A (2001) Development of optimum electrodischarge machining technology for advanced ceramics. Int J Adv Manuf Technol 18(12):897–905. doi:10.1007/PL00003958

    Article  Google Scholar 

  3. Rajurkar, Wang MW (1997) Improvement of EDM performance with advanced monitoring and control system. J Manuf Sci Eng 119:770–775

    Article  Google Scholar 

  4. Wang WM, Rajurkar KP (1992) Modeling and adaptive control of EDM system. J Manuf Syst 115:334–345

    Article  Google Scholar 

  5. Theiler J, Galdrikian B, Longtin A, Eubank S, Farmer JD (1992) Using surrogate data to detect nonlinearity in time series. In: Nonlinear modeling and forecasting. Santa Fe Institute Studies in the Science of Complexity, Proc Vol XII, Addison-Wesley, Reading, MA

  6. Theiler J, Prichard D (1996) Constrained-realization Monte-Carlo method for hypothesis Testing. Physica D 94:221–235. doi:10.1016/0167-2789(96)00050-4

    Article  MATH  Google Scholar 

  7. Theiler J, Eubank S, Longtin A, Galdrikian B, Farmer JD (1992) Testing for nonlinearity in time series: the method of surrogate data. Physica D 58:77–94. doi:10.1016/0167-2789(92)90102-S

    Article  Google Scholar 

  8. Schreiber T, Schmitz A (1996) Improved surrogate data for nonlinearity tests. Phys Rev Lett 77:635–638. doi:10.1103/PhysRevLett.77.635

    Article  Google Scholar 

  9. Schreiber T, Schmitz A (1997) Discrimination power of measures for nonlinearity in a time series. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 55:5443–5447. doi:10.1103/PhysRevE.55.5443

    Google Scholar 

  10. Weiss G (1975) Time-reversibility of linear stochastic processes. J Appl Probab 12:831–836. doi:10.2307/3212735

    Article  MATH  Google Scholar 

  11. Keylock CJ (2007) Identifying linear and nonlinear behavior in reduced complexity modeling output using surrogate data methods. Geomorphology 90:356–366

    Article  Google Scholar 

  12. Weck M, Dehmer JM (1992) Analysis and adaptive control of EDM sinking process using the ignition delay time and fall time as parameter. Ann CIRP 41(1):243–246. doi:10.1016/S0007-8506(07)61195-0

    Article  Google Scholar 

  13. Liu HS, Tarng YS (1997) Monitoring of the electrical discharge machining process by abductive networks. Int J Adv Manuf Technol 13:264–270. doi:10.1007/BF01179608

    Article  Google Scholar 

  14. Kao JY, Tarng YS (1997) A neutral-network approach for the online monitoring of the electrical discharge machining process. J Mater Process Technol 69:112–119. doi:10.1016/S0924−0136(97)00004-6

    Article  Google Scholar 

  15. Boccadoro KPM, Dauw DF (1995) About the application of fuzzy controllers in high-performance die-sinking EDM machines. Ann CIRP 44(1):147–150. doi:10.1016/S0007-8506(07)62294-X

    Article  Google Scholar 

  16. Tarng YS, Tseng CM, Chung LK (1997) A fuzzy pulse discriminating system for electrical discharge machining. Int J Mach Tools Manuf 37(4):511–522. doi:10.1016/S0890-6955(96)00033-8

    Article  Google Scholar 

  17. Ljung L (1999) System Identification-Theory for the user, 2nd edn. Prentice Hall PTR, pp 187–188, 441–444

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Tribology, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China, 100084

    Ming Zhou, Fuzhu Han & Yongxain Wang

  2. Makino Milling Machine Co., Ltd, Tokyo, Japan, 243-0308

    Isago Soichiro

Authors
  1. Ming Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fuzhu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongxain Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Isago Soichiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming Zhou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhou, M., Han, F., Wang, Y. et al. Assessment of the dynamical properties in EDM process—detecting deterministic nonlinearity of EDM process. Int J Adv Manuf Technol 44, 91–99 (2009). https://doi.org/10.1007/s00170-008-1817-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-008-1817-6

Keywords

Search

Navigation