A System for Resource Efficient Process Planning for Wire EDM

  • Sandeep Dhanik
  • Paul Xirouchakis
  • Roberto Perez
Conference paper


Efficient utilization of available resources for a particular machine tool technology is an important aspect of sustainable manufacturing. The objective of this paper is to present a method for efficient resource process planning for a Wire EDM Machine Tool System. For a given part information (required surface finish and workpiece height), the developed program automatically selects the feasible wires and generates the operation sequence and corresponding cutting conditions. The system then presents a multi objective framework consisting of both ecological criteria (waste generation, electricity consumption) and economic criteria (different costs and machining time) to suggest the wire which provides the best compromise solution for the considered criteria.


Wire EDM Energy Consumption Waste Generation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Snoyes, R.; (1986): Current trends in non-conventional material removal processes, in: Annals of CIRP, vol. 35, No.2, pp. 467–480.CrossRefGoogle Scholar
  2. 2.
    DiBonoto, D.D; (1989): Theoretical models of electrical discharge machining process—I. A simple cathode erosion model,in: Journal of Applied Physics Vol. 66, No. 9, pp. 4095–4103.Google Scholar
  3. 3.
    Dhanik, S; Joshi, S.S.; (2005): Modeling of a single resistance capacitance pulse discharge in micro-electro discharge machining, in: Journal of Manufacturing Science and Engineering, Vol.127, pp.759–756CrossRefGoogle Scholar
  4. 4.
    Das S., and Joshi, S.S.; (2010): Modeling of spark erosion rate in micro wire-EDM, in: International Journal of Advanced Manufacturing Technology (2010), Vol.48, pp. 581–596.CrossRefGoogle Scholar
  5. 5.
    Kuriakose, S.; Mohan, K.; Shunmugam, M.S.; (2003) Data mining applied to wire-EDM process, in: Journal of Materials Processing Technology, Vol. 142, No.1, pp. 182–189.CrossRefGoogle Scholar
  6. 6.
    Chen, H.C.; Lin, J.-C.; Yang Y.-Y.; Tsai, C.-H.; (2010): Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach, in: Expert Systems with Applications: An International Journal, Vol. 37, No.10, pp. 7147–7153.CrossRefGoogle Scholar
  7. 7.
    Kuriakose, S.; Shunmugam, M.S.; (2005): Multi-objective optimization of wire-electro discharge machining process by Non-Dominated Sorting Genetic Algorithm, in: Journal of Materials Processing Technology, Vol. 170 No. 1–2, pp. 133- 141.CrossRefGoogle Scholar
  8. 8.
    Tonshoff, H.K.; Egger, R.; Klocke, F.;(1996): Environmental and safety aspects of electrophysical and electrochemical processes, in: CIRP Annals - Manufacturing Technology, Vol. 45, No.2, pp. 553–568.Google Scholar
  9. 9.
    Yeo, S.H.; H.C. Tan; and A.K. New; (1998): Assessment of waste streams in electric-discharge machining for environmental impact analysis, in: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 212, No.5, pp. 393–400.CrossRefGoogle Scholar
  10. 10.
    Yeo, S.H.; and New, A.K.; (1999): Method for green process planning in electric discharge machining, in: International Journal of Advanced Manufacturing Technology, Vol. 15, No.4, pp. 287–291.CrossRefGoogle Scholar
  11. 11.
    Deiss, C; (2009): Eco machine Study on Energy Consumption of wire and wire Erosion Machine (published in german), Master Thesis, RWTH Aachen University.Google Scholar
  12. 12.
    Charmilles; (2005): Technologies Manual Robofil 240 CC 440CC, GF Agie Charmilles SA.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sandeep Dhanik
    • 1
  • Paul Xirouchakis
    • 1
  • Roberto Perez
    • 2
  1. 1.Institute of Production and Robotics, Mechanical Engineering Department, Swiss Federal Institute of TechnologyLausanneSwitzerland
  2. 2.R&D GF AgieCharmilles Technologies S.A.GenevaSwitzerland

Personalised recommendations