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Comparative study of 3- and 5-axis CNC centers for free-form machining of difficult-to-cut material

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  • Zębala, Wojciech
  • Plaza, Malgorzata

Abstract

This paper examines the cost effectiveness of 3- versus 5-axis machines for the machining of a turbine blade made of alloy steel EN 34CrNiMo6. The acceptable surface finish cannot be achieved when a free surface is machined on a 3-axis CNC center due to the variation of the tool׳s position in relation to the part׳s surface. A feed-rate adjustment algorithm is proposed in this paper as a way to compensate for that limitation. To that end, the following two research questions are addressed: (1) if the algorithm is used, can the required surface finish be achieved on a 3-axis CNC center, and (2) which of two alternatives: a 3-axis (assuming the algorithm is executed) or 5-axis CNC center (without the algorithm) will be more cost effective?

Suggested Citation

  • Zębala, Wojciech & Plaza, Malgorzata, 2014. "Comparative study of 3- and 5-axis CNC centers for free-form machining of difficult-to-cut material," International Journal of Production Economics, Elsevier, vol. 158(C), pages 345-358.
    • Handle: RePEc:eee:proeco:v:158:y:2014:i:c:p:345-358
    DOI: 10.1016/j.ijpe.2014.08.006
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    References listed on IDEAS

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    1. Jaber, Mohamad Y. & Guiffrida, Alfred L., 2004. "Learning curves for processes generating defects requiring reworks," European Journal of Operational Research, Elsevier, vol. 159(3), pages 663-672, December.
    2. Langmaak, Stephan & Wiseall, Stephen & Bru, Christophe & Adkins, Russell & Scanlan, James & Sóbester, András, 2013. "An activity-based-parametric hybrid cost model to estimate the unit cost of a novel gas turbine component," International Journal of Production Economics, Elsevier, vol. 142(1), pages 74-88.
    3. Jaber, Mohamad Y. & Khan, Mehmood, 2010. "Managing yield by lot splitting in a serial production line with learning, rework and scrap," International Journal of Production Economics, Elsevier, vol. 124(1), pages 32-39, March.
    4. Quintana, Guillem & Ciurana, Joaquim, 2011. "Cost estimation support tool for vertical high speed machines based on product characteristics and productivity requirements," International Journal of Production Economics, Elsevier, vol. 134(1), pages 188-195, November.
    5. Kovac, Pavel & Sidjanin, Leposava, 1997. "Investigation of chip formation during milling," International Journal of Production Economics, Elsevier, vol. 51(1-2), pages 149-153, August.
    6. Abdel-Malek, Layek & Asadathorn, Nutthapol, 1996. "An analytical approach to process planning with rework option," International Journal of Production Economics, Elsevier, vol. 46(1), pages 511-520, December.
    7. Qian, Li & Ben-Arieh, David, 2008. "Parametric cost estimation based on activity-based costing: A case study for design and development of rotational parts," International Journal of Production Economics, Elsevier, vol. 113(2), pages 805-818, June.
    8. Plaza, Malgorzata & Ngwenyama, Ojelanki K. & Rohlf, Katrin, 2010. "A comparative analysis of learning curves: Implications for new technology implementation management," European Journal of Operational Research, Elsevier, vol. 200(2), pages 518-528, January.
    9. Folgado, R. & Peças, P. & Henriques, E., 2010. "Life cycle cost for technology selection: A Case study in the manufacturing of injection moulds," International Journal of Production Economics, Elsevier, vol. 128(1), pages 368-378, November.
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    Cited by:

    1. Plaza, Malgorzata, 2016. "Balancing the costs of human resources on an ERP project," Omega, Elsevier, vol. 59(PB), pages 171-183.

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    Keywords

    Free-form machining; Cost modeling;

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