Skip to main content
SpringerLink
Log in

An economic and reliable tool life estimation procedure for turning

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The conventional methods of tool life estimation take a long time and consume a lot of work piece material. In this paper, a quicker method for the estimation of tool life is proposed, which requires less consumption of work piece material and tools. In this method the tool life is estimated by fitting a best-fit line on the data falling in the steady wear zone and finding the time till tool failure by extrapolation. Neural networks are used to predict lower, upper and most likely estimates of the tool life. Comparison between neural networks and multiple regression shows the superiority of the former. The paper also proposes a methodology for continuous monitoring of tool use in the shop floor and updating/obtaining the tool life estimates based on the shop floor feed back.

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. Gilbert WW (1950) Economics of machining: machining theory and practice. American Society of Metals, Cleveland, OH

    Google Scholar 

  2. Ermer DS (1971) Optimization of constrained machining economics problem by geometric programming. Trans ASME J Eng Ind 93:1067–1072

    Article  Google Scholar 

  3. Petropoulos PG (1973) Optimal selection of machining rate variables by geometric programming. Int J Prod Res 11(4):305–314

    Article  Google Scholar 

  4. Lambert BK, Walvekar AG (1978) Optimization of multipass machining operations. Int J Prod Res 16(4):259–265

    Article  Google Scholar 

  5. Shin YC, Joo YS (1992) Optimization of machining condition with practical constraints. Int J Prod Res 30(12):2907–2919

    Article  Google Scholar 

  6. Kee PK (1996) Development of constrained optimization analysis and strategies for multipass rough turning operations. Int J Mach Tools Manuf 36(1):115–127

    Article  Google Scholar 

  7. Gupta R, Batra JL, Lal GK (1995) Determination of optimal subdivision of depth of cut in multipass turning with practical constraints. Int J Prod Res 33(9):2555–2565

    Article  Google Scholar 

  8. Chen MC, Tsai DM (1996) A simulated annealing approach for optimization of multipass turning operation. Int J Prod Res 34(10):2803–2825

    Article  Google Scholar 

  9. Liang M, Mgwatu M, Zuo M (2001) Integration of cutting parameter selection and tool adjustment decisions for multipass turning. Int J Adv Manuf Technol 17:861–869

    Article  Google Scholar 

  10. Onwubolu GC, Kumalo T (2001) Optimization of multipass turning operations with genetic algorithm. Int J Prod Res 39(16):3727–3745

    Article  Google Scholar 

  11. Al-Ahmari AMA (2001) Mathematical model for determining machining parameters in multipass turning operations with constraints. Int J Prod Res 39(15):3367–3376

    Article  Google Scholar 

  12. Baykasoglu A, Dereli T (2002) Novel algorithm approach to generate the ‘number of passes’ and ‘depth of cuts’ for the optimization routines of multipass machining. Int J Prod Res 44(7):1549–1565

    Article  Google Scholar 

  13. Wu SM (1964) Tool life testing by response surface methodology-part 1. Trans ASME J Eng Ind 86:105–110

    Article  Google Scholar 

  14. Wu SM (1964) Tool life testing by response surface methodology-part 2. Trans ASME J Eng Ind 86:111–116

    Article  Google Scholar 

  15. Vilenchich R, StrobeleK, Vebter R (1962) Tool life testing by response surface methodology coupled with a random strategy approach. Proc, 13th Int MTDR Conference. Birmingham, UK, pp 261–266, September 1962

  16. Ezugwu EO, Arthur SJ, Hines EL (1995) Tool wear prediction using artificial neural networks. J Mater Process Technol 49:255–264

    Article  Google Scholar 

  17. Tosun N, Özer L (2002) A study of tool life in hot machining usingartificial neural networks and regression analysis methods. J Mater Process Technol 124:99–104

    Article  Google Scholar 

  18. Dos Santos ALB, Duarte MAV, Abrato AM, Machado AR (1999) An optimization procedure to determine the coefficient of the extended Taylor’s equation in machining. Int J Mach Tools Manuf 39:17–31

    Article  Google Scholar 

  19. Axinte DA, Belluco W, De Chiffre L (2001) Reliable tool life measurements in turning – an application to cutting fluid efficiency evaluation. Int J Mach Tools Manuf 41:1003–1014

    Article  Google Scholar 

  20. Armarego EJA, Jawahir IS, Osfaviev VA, Patri VK (1996) Modeling of machining operations. In working paper STC ‘C’ working group. Hallway Publishers, Paris, France

  21. Stephenson DA, Agapiou JS (1997) Metal cutting theory and practice. Dekker, New York

  22. Hegginbotham WB, Pandey PC (1967) A variable rate machining test for tool life evaluation. Proc, 8th International MTDR conference, Manchester, September 1967

  23. Kohli A, Dixit US (2004) A neural network based methodology for prediction of surface roughness in turning process. Int J Adv Manuf Technol (online first)

  24. Dieter GE (1991) Engineering design: a materialistic and processing approach, 2nd edition. McGraw-Hill, New York

    Google Scholar 

  25. Ishibuchi H, Tanaka H (1991) Regression analysis with interval model by neural networks. Proc IEEE Int Joint Conf on Neural Networks. Singapore, pp 1594–1599

  26. Rao SS (1996) Engineering optimization: theory and practice, 3rd edition. New Age International, New Delhi

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, 781 039, India

    D.K. Ojha & U.S. Dixit

Authors
  1. D.K. Ojha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. U.S. Dixit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to U.S. Dixit.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ojha, D., Dixit, U. An economic and reliable tool life estimation procedure for turning. Int J Adv Manuf Technol 26, 726–732 (2005). https://doi.org/10.1007/s00170-003-2049-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-003-2049-4

Keywords

Search

Navigation