Skip to main content

Advertisement

Log in

Design of a decision support system for machine tool selection based on machine characteristics and performance tests

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Economic globalization, together with heightened market competition and increasingly short product life cycles are motivating companies to use advanced manufacturing technologies. Use of high speed machining is increasingly widespread; however, as the technology is relatively new, it lacks a deep-rooted knowledge base which would facilitate implementation. One of the most frequent problems facing companies wishing to adopt this technology is selecting the most appropriate machine tool for the product in question and own enterprise characteristics. This paper presents a decision support system for high speed milling machine tool selection based on machine characteristics and performance tests. Profile machining tests are designed and conducted in participating machining centers. The decision support system is based on product dimension accuracy, process parameters such as feed rate and interpolation scheme used by CNC and machine characteristics such as machine accuracy and cost. Experimental data for process error and cycle operation time are obtained from profile machining tests with different geometrical feature zones that are often used in manufacturing of discrete parts or die/moulds. All those input parameters have direct impact on productivity and manufacturing cost. Artificial neural network models are utilized for decision support system with reasonable prediction capability.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Abbreviations

MT:

Machine tool

SG:

Machine guide system

E:

Machine configuration

TC:

Temperature control

G:

Geometrical feature

A:

Machine tool acceleration

V:

Volume

V f :

Feed rate

I:

Interpolation scheme used by CNC

T:

Tool holding system

E m :

Machine tool accuracy/error

E p :

Process error

tc :

Process time

C p :

Process cost

C MT :

Machine tool cost

References

  • Albertí M., Ciurana J., Rodríguez C. A.: Experimental analysis of dimensional error vs. cycle time in high speed milling of aluminium alloy. International Journal of Machine Tools & Manufacture 47(2), 236–246 (2007)

    Article  Google Scholar 

  • Altan T., Lilly B., Yen Y. C.: Manufacturing of dies and molds. Annals of the CIRP 50, 405–423 (2001)

    Article  Google Scholar 

  • Arslan M. C., Çatay B., Budak E.: A decision support system for machine selection. Journal of Manufacturing Technology Management 15(1), 101–109 (2004)

    Article  Google Scholar 

  • Aspinwall D. K., Dewes R. C., Burrows J. M., Paul M. A.: Hybrid High speed machining (HSM): System design and experimental results for grinding/HSM and EDM/HSM. Annals of the CIRP 50(1), 145–148 (2001)

    Article  Google Scholar 

  • Ayağ A., Özdemir R. G.: A Fuzzy AHP approach to evaluating machine tool alternatives. Journal of Intelligent Manufacturing 17(2), 179–190 (2006)

    Article  Google Scholar 

  • Baskar N., Asokan P., Saravanan R., Prabhaharan G.: Selection of optimal machining parameters for multi-tool milling operations using a memetic algorithm. Journal of Materials Processing Technology 174, 239–249 (2006)

    Article  Google Scholar 

  • Bock L.: Material process selection methodology: design for manufacturing and cost using logic programming. Cost Engineering 33(5), 9–14 (1991)

    Google Scholar 

  • Brown S. M., Wright P. K.: A progress report on the manufacturing analysis service, an internet-based reference tool. Journal of Manufacturing Systems 17(5), 389–398 (1998)

    Article  Google Scholar 

  • Chan F., Swarnkar R.: Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS. Robotics and Computer-Integrated Manufacturing 22, 353–362 (2006)

    Article  Google Scholar 

  • Chtourou H., Masmoudi W., Maalej A.: An expert system for manufacturing systems machine selection. Expert Systems with Applications 28, 461–467 (2005)

    Article  Google Scholar 

  • Chung C., Peng Q.: The selection of tools and machines on web-based manufacturing environments. International Journal of Machine Tools & Manufacture 44, 317–326 (2004)

    Article  Google Scholar 

  • Dagiloke I. F., Kaldos A., Douglas S., Mills B.: High-speed machining: an approach to process analysis. Journal of Materials Processing Technology 54, 82–87 (1995)

    Article  Google Scholar 

  • Duran O., Aguilo J.: Computer-aided machine-tool selection based on a Fuzzy-AHP approach. Expert Systems with Applications 34, 1787–1794 (2008)

    Article  Google Scholar 

  • Giachetti R. E.: A decision support system for material and manufacturing selection. Journal of Intelligent Manufacturing 9, 265–276 (1998)

    Article  Google Scholar 

  • Jung J. Y.: Manufacturing cost estimation for machined parts based on manufacturing features. Journal of Intelligent Manufacturing 13(4), 227–238 (2002)

    Article  Google Scholar 

  • Kaschka U., Auerbach P.: Selection and evaluation of rapid tooling process chains with Protool. Rapid Prototyping Journal 6(1), 60–65 (2000)

    Article  Google Scholar 

  • Keung K. W., Ip W. H., Lee T. C.: A genetic algorithm approach to the multiple machine tool selection problem. Journal of Intelligent Manufacturing 12(4), 331–342 (2001)

    Article  Google Scholar 

  • Lin Z. C., Yang C. B.: Evaluation of machine selection by the AHP method. Journal of Materials Processing Technology 57, 253–258 (1996)

    Article  Google Scholar 

  • Lópezde Lacalle N., Sánchez J. A., Lamikiz A.: Mecanizado de Alto Rendimiento. Ediciones Técnicas Izaro, Bilbao (2004)

    Google Scholar 

  • Malakooti B., Raman V.: An interactive multi-objective artificial neural network approach for machine setup optimization. Journal of Intelligent Manufacturing 11, 41–50 (2000)

    Article  Google Scholar 

  • Matlab User’s Guide. (2002). Neural network toolbox. Massachusetts: The MathWorks.

  • Önüt S., Kara S. S., Efendigil T.: A hybrid fuzzy MCDM approach to machine tool selection. Journal of Intelligent Manufacturing 19(4), 443–453 (2008)

    Article  Google Scholar 

  • Özel T., Karpat Y.: Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture 45(4-5), 467–479 (2005)

    Article  Google Scholar 

  • Raj Aggarwal T.: General theory and its application in the high-speed milling of aluminium. In: King, R. I.(eds) Handbook of high-speed machining technology., pp. 197–240. Chapman & Hall, New York (1995)

    Google Scholar 

  • Smith C. S., Wright P. K., Séquin C.: The manufacturing advisory service: web-based process and material selection. International Journal of Computer Integrated Manufacturing 16(6), 373–381 (2003)

    Article  Google Scholar 

  • Tabucanon M. T., Batanov D. N., Verma D. K.: Intelligent decision support system (DSS) for the selection process of alternative machines for flexible manufacturing systems (FMS). Computers in Industry 25, 131–143 (1994)

    Article  Google Scholar 

  • Vivancos, J., Luis, C. J., Costa, L., & Ortíz J. A., (2004). Optimal machining parameters selection in high speed milling of hardened steels for injection moulds. Journal of Materials Processing Technology, 155–156, 1505–1512.

  • Yurdakul M.: AHP as a strategic decision-making tool to justify machine tool selection. Journal of Materials Processing Technology 146, 365–376 (2004)

    Article  Google Scholar 

  • Yurdakul M., Tansel Y.: Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. Journal of Materials Processing Technology 209, 310–317 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joaquim Ciurana.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alberti, M., Ciurana, J., Rodríguez, C.A. et al. Design of a decision support system for machine tool selection based on machine characteristics and performance tests. J Intell Manuf 22, 263–277 (2011). https://doi.org/10.1007/s10845-009-0286-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-009-0286-6

Keywords