An integrated model to use drilling modular machine tools

  • Ana VafadarEmail author
  • Majid Tolouei-Rad
  • Kevin Hayward


Modular machine tools provide a platform for drilling-related operations within automotive companies. The use of these machine tools is widespread; however, manufacturers wishing to use this technology frequently face the challenge of selecting the most appropriate manufacturing system. Accordingly, a comprehensive feasibility analysis procedure is required to assist decision-makers before any investment is made on the preparation of detailed machine design or purchase one. This paper presents a model, which collects the previous works of the authors. To do this, an integrated framework for decision-making of using machine tools is developed. The aim of this model is to enable users to make a logical decision by assessing the strengths and limitations of machine tools. To do this, the parameters which have a key influence on the decision-making process and relevant procedures are identified and integrated into a model. A case study is presented to illustrate the application of proposed model, and results are discussed. The results show that the proposed model is useful in assisting manufacturers in evaluating the performance of a modular machine tool in comparison with other alternatives.


Integrated decision-making model Manufacturing systems Drilling-related operations Feasibility analysis Machine tool selection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors would like to thank Dr. Helen Renwick for helpful writing advice to improve this paper.


  1. 1.
    Abdi MR (2009) Fuzzy multi-criteria decision model for evaluating reconfigurable machines. Int J Prod Econ 117:1–15CrossRefGoogle Scholar
  2. 2.
    Molina A, Rodriguez CA, Ahuett H, Cortes J, Ramírez M, Jiménez G, Martinez S (2005) Next-generation manufacturing systems: key research issues in developing and integrating reconfigurable and intelligent machines. Int J Comput Integr Manuf 18:525–536CrossRefGoogle Scholar
  3. 3.
    Koren Y, Heisel U, Jovane F, Moriwaki T, Pritschow G, Ulsoy G, Van Brussel H (1999) Reconfigurable manufacturing systems. CIRP Ann Manuf Technol 48:527–540CrossRefGoogle Scholar
  4. 4.
    Xi FJ, Macwan A (2005) Optimal module selection for preliminary design of reconfigurable machine tools. J Manuf Sci Eng:104–115Google Scholar
  5. 5.
    Landers RG, Min B-K, Koren Y (2001) Reconfigurable machine tools. CIRP Ann Manuf Technol 50:269–274CrossRefGoogle Scholar
  6. 6.
    Lorenzer T, Weikert S, Bossoni S, Wegener K (2007) Modeling and evaluation tool for supporting decisions on the design of reconfigurable machine tools. J Manuf Syst 26:167–177CrossRefGoogle Scholar
  7. 7.
    Tolouei-Rad M, Zolfaghari S (2009) Productivity improvement using special-purpose modular machine tools. Int J Manuf Res 4:219–235CrossRefGoogle Scholar
  8. 8.
    Suhner general catalogue (2012) Automation Expert.., Switzerland. [Accessed 25 April 2015].
  9. 9.
    Vafadar A, Tolouei-Rad M, Hayward K, Abhary K (2016) Technical feasibility analysis of utilizing special purpose machine tools. J Manuf Syst 39:53–62CrossRefGoogle Scholar
  10. 10.
    Tolouei-Rad M (2012) In: Koleshko PVM (ed) Intelligent analysis of utilization of special purposes machines for drilling operations. InTech, Croatia, pp 297–320Google Scholar
  11. 11.
    Yurdakul M (2004) AHP as a strategic decision-making tool to justify machine tool selection. J Mater Process Technol 146:365–376CrossRefGoogle Scholar
  12. 12.
    Önüt S, Soner Kara S, Efendigil T (2008) A hybrid fuzzy MCDM approach to machine tool selection. J Intell Manuf 19:443–453CrossRefGoogle Scholar
  13. 13.
    Samvedi A, Jain V, Chan FTS (2012) An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis. Int J Prod Res 50:3211–3221CrossRefGoogle Scholar
  14. 14.
    Ayağ Z, Özdemir RG (2006) A fuzzy AHP approach to evaluating machine tool alternatives. J Intell Manuf 17:179–190CrossRefGoogle Scholar
  15. 15.
    Abdi MR, Labib AW (2003) A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): a case study. Int J Prod Res 41:2273–2299CrossRefGoogle Scholar
  16. 16.
    Aloini D, Dulmin R, Mininno V (2014) A peer IF-TOPSIS based decision support system for packaging machine selection. Expert Syst Appl 41:2157–2165CrossRefGoogle Scholar
  17. 17.
    Klocke F, Zeis M, Klink A, Veselovac D (2013) Technological and economical comparison of roughing strategies via milling, sinking-EDM, wire-EDM and ECM for titanium- and nickel-based blisks. CIRP J Manuf Sci Technol 6:198–203CrossRefGoogle Scholar
  18. 18.
    Battaïa O, Dolgui A, Guschinsky N (2016) Decision support for design of reconfigurable rotary machining systems for family part production. Int J Prod Res:1–18Google Scholar
  19. 19.
    Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51:363–376CrossRefGoogle Scholar
  20. 20.
    Vafadar A, Tolouei-Rad M, Hayward K (2016) New cost model for feasibility analysis of utilising special purpose machine tools. Int J Prod Res 54:7330–7344CrossRefGoogle Scholar
  21. 21.
    Vafadar A, Hayward K, Tolouei-Rad M (2017) Drilling reconfigurable machine tool selection and process parameters optimization as a function of product demand. J Manuf Syst 45:58–69CrossRefGoogle Scholar
  22. 22.
    Vafadar A, Hayward K, Tolouei-Rad M (2017) Sensitivity analysis for justification of utilising special purpose machine tools in the presence of uncertain parameters. Int J Prod Res 55:3842–3861CrossRefGoogle Scholar
  23. 23.
    Xu X, Wang L, Newman ST (2011) Computer-aided process planning–a critical review of recent developments and future trends. Int J Comput Integr Manuf 24:1–31CrossRefGoogle Scholar
  24. 24.
    HSS Forum (2014) Think reliability, think HSSGoogle Scholar
  25. 25.
    Hitomi K (1996) Manufacturing systems engineering: a unified approach to manufacturing technology, production management and industrial economics, 2nd edn. CRC Press, LondonGoogle Scholar
  26. 26.
    Schmitz TL, Karandikar J, Kim NH, Abbas A (2011) Uncertainty in machining: workshop summary and contributions. J Manuf Sci Eng 133:051009CrossRefGoogle Scholar
  27. 27.
    Nathan (2015) Power steering pump. In: Grabcad. [Accessed 6 March 2015]
  28. 28.
    Vafadar A, Tolouei-Rad M, Hayward K (2017) Evaluation of the effect of product demand uncertainty on manufacturing system selection. Procedia Manuf 11:1735–1743CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of EngineeringEdith Cowan University (ECU)PerthAustralia

Personalised recommendations