Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm

  • Mohammad Reza Razfar
  • Reza Farshbaf Zinati
  • Mahdiar Haghshenas


This paper presents a new approach to determine the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling artificial neural network (ANN) and harmony search algorithm (HS). In this regard, advantages of statistical experimental design technique, experimental measurements, analysis of variance, artificial neural network and harmony search algorithm were exploited in an integrated manner. To this end, numerous experiments on X20Cr13 stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness was created using a feed forward neural network exploiting experimental data. The optimization problem was solved by harmony search algorithm. Additional experiments were performed to validate optimum surface roughness value predicted by HS algorithm. The obtained results show that the harmony search algorithm coupled with feed forward neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.


Cutting parameters Optimization Surface roughness Artificial neural network Harmony search algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Huang B, 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 21:339–347MATHCrossRefGoogle Scholar
  2. 2.
    Kohli A, Dixit US (2005) A neural network based methodology for the prediction of surface roughness in a turning process. Int J Adv Manuf Technol 25:118–129CrossRefGoogle Scholar
  3. 3.
    Dagnal MA (1986) Exploring surface texture. Rank Taylor Hobson, LeicesterGoogle Scholar
  4. 4.
    Box GEP, Draper NR (1987) Empirical model-building and response surface. Wiley, New YorkGoogle Scholar
  5. 5.
    Luong LHS, Spedding TA (1995) Neural-network system for predicting machining behavior. J Mater Process Technol 52:585–591CrossRefGoogle Scholar
  6. 6.
    Petri KL, Billo RE, Bidanda B (1998) A neural network process model for abrasive flow machining operations. J Manuf Sci 17(1):52–64CrossRefGoogle Scholar
  7. 7.
    Zang HC, Huang SH (1995) Application of neural network in manufacturing-a state of art survey. Int J Prod Res 33(3):705–728CrossRefGoogle Scholar
  8. 8.
    Kalpakjian S, Schmid SR (2001) Manufacturing Engineering and Technology. Prentice Hall, New JerseyGoogle Scholar
  9. 9.
    Lou S (1997) Development of four in process surface recognition systems to predict surface roughness in end milling. University of Iowa State, DissertationGoogle Scholar
  10. 10.
    Godfrey C, Onwubolu (2006) Performance based optimization of multi pass face milling operations using Tribes. Int J Mach Tool Manuf 46:717–727CrossRefGoogle Scholar
  11. 11.
    Dong YJ, Young-Gu C, Hong-Gil K, Hsiao A (1996) Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning. Int J Mach Tools Manuf 36(4):453–464CrossRefGoogle Scholar
  12. 12.
    SECO Milling 1 (2007) Catalogue & Technical Guide. SECO Tools AB, 737 82 Fagersta, SwedenGoogle Scholar
  13. 13.
    Keppel G, Zedeck S (1989) Data analysis for research designs: analysis of variance and multiple research/correlation approaches. Freeman, New YorkGoogle Scholar
  14. 14.
    Rosse P (1996) Taguchi techniques for quality engineering, loss funktion, orthogonal experiments, parameter and tolerance design. McGraw-Hill, TorontoGoogle Scholar
  15. 15.
    Sanjay C, Jyothi C (2006) A study of surface roughness in drilling using mathematical analysis and neural networks. Int J Adv Manuf Technol 29:846–852CrossRefGoogle Scholar
  16. 16.
    Tansela IN, Ozcelikb B, Baoa WY, Chena P, Rincona D, Yanga SY, Yenilmezc A (2006) Selection of optimal cutting conditions by using GONNS. Int J mach Tool Manuf 46:26–35CrossRefGoogle Scholar
  17. 17.
    Chen Jacob C, Chen Joseph C (2005) An artificial neural networks based in process tool wear prediction system in milling operations. Int J Adv Manuf Technol 25:427–434CrossRefGoogle Scholar
  18. 18.
    Seok LK, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Engrg 194:3902–3933MATHCrossRefGoogle Scholar
  19. 19.
    Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial Intelligence through Simulated Evolution. Wiley, ChichesterMATHGoogle Scholar
  20. 20.
    Jong KD (1975) Analysis of the behavior of a class of genetic adaptive systems. Dissertation, University of MichiganGoogle Scholar
  21. 21.
    Koza JR (1990) Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Rep No STAN-CS-90-1314, Stanford University, CAGoogle Scholar
  22. 22.
    Holland JH (1975) Adaptation in natural and artificial systems. Dissertation University of Michigan PressGoogle Scholar
  23. 23.
    Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, Addison. Wesley, BostonGoogle Scholar
  24. 24.
    Glover F (1977) Heuristic for integer programming using surrogate constraints. Decision Sci 8(1):156–166CrossRefGoogle Scholar
  25. 25.
    Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Science 220(4598):671–680CrossRefMathSciNetGoogle Scholar
  26. 26.
    Geem ZW, Kim JH, Loganathan GV (2001) a new heuristic optimization algorithm: harmony search. simul 76(2):60–68Google Scholar
  27. 27.
    Kim JH, Geem ZW, Kim ES (2001) Parameter estimation of the nonlinear Muskingum model using harmony search. J Am Water Resour Assoc 37(5):1131–1138CrossRefGoogle Scholar
  28. 28.
    SuperNEC (2004) Success of the GA optimiser. In: Genetic Algorithm Optimiser User Reference Manual. SuperNEC Group. Available via DIALOG: http://www.supernec.com/manuals/sngaum.htm#_Toc21938615. Accessed July 2004

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Mohammad Reza Razfar
    • 1
  • Reza Farshbaf Zinati
    • 1
  • Mahdiar Haghshenas
    • 1
  1. 1.Department of Mechanical EngineeringAmirkabir University of TechnologyTehranIran

Personalised recommendations