Skip to main content

Advertisement

SpringerLink
Log in

An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The aim of this study is to develop an integrated study of surface roughness to model and optimize the cutting parameters when end milling of AISI 1040 steel material with TiAlN solid carbide tools under wet condition. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental measurements and to show the effect of four cutting parameters on the surface roughness. Artificial neural network (ANN) based on Back-propagation (BP) learning algorithm is used to construct the surface roughness model exploiting a full factorial design of experiments. Genetic algorithm (GA) supported with the tested ANN is utilized to determine the best combinations of cutting parameters providing roughness to the lower surface through optimization process. GA improves the surface roughness value from 0.67 to 0.59 μm with approximately 12% gain. Then, machining time has also decreased from 1.282 to 1.0316 min by about 20% reduction based on the cutting parameters before and after optimization process using the analytical formulas. The final measurement experiment has been performed to verify surface roughness value resulted from GA with that of the material surface by 3.278% error. From these results, it can be easily realized that the developed study is reliable and suitable for solving the other problems encountered in metal cutting operations as the same as surface roughness.

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. Boothroyd G, Knight WA (1989) Fundamentals of machining and machine tools. Marcel Dekker Inc., New York

    Google Scholar 

  2. Dagnal H (1986) Exploring surface texture. Rank Taylor Habson Corp., England

    Google Scholar 

  3. Urbanski JP, Koshy P, Dewes RC, Aspinwall DK (2000) High speed machining of mold and dies for net shape manufacture. Mater Des 2:395–402

    Google Scholar 

  4. Koshy P, Dewes RC, Aspinwall DK (2002) High speed end milling of hardened AISI D2 tool steel (~58 HRC). J Mater Process Technol 127:266–273, doi:10.1016/S0924-0136(02)00155-3

    Article  Google Scholar 

  5. Huang BP, Chen JC (2003) An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations. Int J Adv Manuf Technol 21:339–347, doi:10.1007/s001700300039

    Article  MATH  Google Scholar 

  6. Mansour A, Abdalla H (2002) Surface roughness model for end milling: a semi-free cutting carbon casehardening steel (EN 32) in dry condition. J Mater Process Technol 124:183–191, doi:10.1016/S0924-0136(02)00135-8

    Article  Google Scholar 

  7. Reddy NS, Rao PV (2005) Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling. Int J Adv Manuf Technol 26:1202–1210, doi:10.1007/s00170-004-2110-y

    Article  Google Scholar 

  8. Huang BP, Chen JC (2008) Artificial-neural-network-based surface roughness Pokayoke system for end-milling operations. Neurocomputing 71:544–549, doi:10.1016/j.neucom.2007.07.029

    Article  Google Scholar 

  9. Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Rob Comput Integr Manuf 18:343–354, doi:10.1016/S0736-5845(02)00005-4

    Article  Google Scholar 

  10. Nalbant M, Gökkaya H, Toktaş I, Sur G (2008) The experimental investigation of the effects of uncoated, PVD-and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks. Rob Comput Integr Manuf (in press)

  11. Zhong ZW, Khoo LP, Han ST (2006) Prediction of surface roughness of turned surfaces using neural networks. Int J Adv Manuf Technol 28:688–693, doi:10.1007/s00170-004-2429-4

    Article  Google Scholar 

  12. Briceno JF, Mounayri HE, Mukhopadhyay S (2002) Selecting an artificial neural network for efficient modelling and accurate simulation of the milling process. Int J Mach Tools Manuf 42:663–674, doi:10.1016/S0890-6955(02)00008-1

    Article  Google Scholar 

  13. Özel T, Karpat Y, Figueira L, Davim JP (2007) Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. J Mater Process Technol 189:192–198, doi:10.1016/j.jmatprotec.2007.01.021

    Article  Google Scholar 

  14. Jesuthanam CP, Kumanan S, Asokan P (2007) Surface roughness prediction using hybrid neural networks. Mach Sci Technol 11:271–286, doi:10.1080/10910340701340141

    Article  Google Scholar 

  15. Song RG, Zhang QZ (2001) Heat treatment technique optimization for 7175 aluminum alloy by an artificial neural network and a genetic algorithm. J Mater Process Technol 117:84–88, doi:10.1016/S0924-0136(01)01114-1

    Article  Google Scholar 

  16. Srinivas V, Ramanjaneyulu K (2007) An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks. Adv Eng Software 38:475–487, doi:10.1016/j.advengsoft.2006.09.016

    Article  Google Scholar 

  17. Cus F, Balic J (2003) Optimization of cutting process by GA approach. Rob Comput Integr Manuf 19:113–121, doi:10.1016/S0736-5845(02)00068-6

    Article  Google Scholar 

  18. Iscur (2006) Cutting tool material and catalogue. IS-CUT, Israel

    Google Scholar 

  19. Coromant S (2005) New tools from sandvik, tool catalogue. AB Sandvik Coromant, Sweeden

    Google Scholar 

  20. Lippman RP (1987) An introduction to computing with neural nets. IEEE ASSP 4:4–22, doi:10.1109/MASSP.1987.1165576

    Article  Google Scholar 

  21. Anderson D, McNeil G (1992) Artificial neural network technology. Kaman Sciences Corporation

  22. Math Works Incorporation (2005) Matlab user manual version 7.1 R14. Math Works Inc., Natick, MA

    Google Scholar 

  23. David LC (1997) Genetic algorithms. University of Illinois, Champaign

    Google Scholar 

  24. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, New York

    MATH  Google Scholar 

  25. Ozcelik B, Oktem H, Kurtaran H (2005) Optimum surface roughness in end milling Inconel 718 by coupling neural network and genetic algorithm. Int J Adv Manuf Technol 27:234–241, doi:10.1007/s00170-004-2175-7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Gebze Vocational School, Department of Mechanical Engineering, University of Kocaeli, 41420, Gebze-Kocaeli, Turkey

    H. Öktem

Authors
  1. H. Öktem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. Öktem.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Öktem, H. An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation. Int J Adv Manuf Technol 43, 852–861 (2009). https://doi.org/10.1007/s00170-008-1763-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-008-1763-3

Keywords

Search

Navigation