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

International Journal of Advanced Manufacturing Technology

Volumn 52, Issue 5-8, 2011, Pages 487-495

  Razfar, Mohammad Reza ^a   Farshbaf Zinati, Reza ^a   Haghshenas, Mahdiar ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Artificial neural network; Cutting parameters; Harmony search algorithm; Optimization; Surface roughness

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CUTTING PARAMETERS; EXPERIMENTAL DATA; EXPERIMENTAL MEASUREMENTS; FACE MILLING; GLOBAL MINIMA; HARMONY SEARCH ALGORITHMS; HS ALGORITHM; NEW APPROACHES; OPTIMAL CUTTING PARAMETERS; OPTIMIZATION PROBLEMS; PREDICTIVE MODELS; STATISTICAL EXPERIMENTAL DESIGN;

LEARNING ALGORITHMS; METAL ANALYSIS; MILLING (MACHINING); OPTIMIZATION; PARAMETER ESTIMATION; REGRESSION ANALYSIS; STAINLESS STEEL; SURFACE PROPERTIES; SURFACE ROUGHNESS;

NEURAL NETWORKS;

EID: 79751524167     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-010-2757-5     Document Type: Article

References (28)

1. B Huang JC Chen 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 1049.65048 10.1007/s001700300039
2. A Kohli US Dixit 2005 A neural network based methodology for the prediction of surface roughness in a turning process Int J Adv Manuf Technol 25 118 129 10.1007/s00170-003-1810-z (Pubitemid 40034736)
3. Dagnal MA (1986) Exploring surface texture. Rank Taylor Hobson, Leicester
4. Box GEP, Draper NR (1987) Empirical model-building and response surface. Wiley, New York
5. LHS Luong TA Spedding 1995 Neural-network system for predicting machining behavior J Mater Process Technol 52 585 591 10.1016/0924-0136(94)01626-C
6. KL Petri RE Billo B Bidanda 1998 A neural network process model for abrasive flow machining operations J Manuf Sci 17 1 52 64 10.1016/S0278-6125(98) 80009-5 (Pubitemid 128447046)
7. HC Zang SH Huang 1995 Application of neural network in manufacturing-a state of art survey Int J Prod Res 33 3 705 728 10.1080/00207549508930175
8. Kalpakjian S, Schmid SR (2001) Manufacturing Engineering and Technology. Prentice Hall, New Jersey
9. Lou S (1997) Development of four in process surface recognition systems to predict surface roughness in end milling. University of Iowa State, Dissertation
10. C Godfrey Onwubolu 2006 Performance based optimization of multi pass face milling operations using Tribes Int J Mach Tool Manuf 46 717 727 10.1016/j.ijmachtools.2005.07.041
11. YJ Dong C Young-Gu K Hong-Gil A Hsiao 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 4 453 464 10.1016/0890-6955(95)00074-7 (Pubitemid 126374952)
12. SECO Milling 1 (2007) Catalogue & Technical Guide. SECO Tools AB, 737 82 Fagersta, Sweden
13. Keppel G, Zedeck S (1989) Data analysis for research designs: analysis of variance and multiple research/correlation approaches. Freeman, New York
14. Rosse P (1996) Taguchi techniques for quality engineering, loss funktion, orthogonal experiments, parameter and tolerance design. McGraw-Hill, Toronto
15. C Sanjay C Jyothi 2006 A study of surface roughness in drilling using mathematical analysis and neural networks Int J Adv Manuf Technol 29 846 852 10.1007/s00170-005-2538-8 (Pubitemid 44195209)
16. IN Tansela B Ozcelikb WY Baoa P Chena D Rincona SY Yanga A Yenilmezc 2006 Selection of optimal cutting conditions by using GONNS Int J mach Tool Manuf 46 26 35 10.1016/j.ijmachtools.2005.04.012 (Pubitemid 41497897)
17. C Chen Jacob C Chen Joseph 2005 An artificial neural networks based in process tool wear prediction system in milling operations Int J Adv Manuf Technol 25 427 434 10.1007/s00170-003-1848-y (Pubitemid 40833851)
18. LK Seok ZW Geem 2005 A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice Comput Methods Appl Mech Engrg 194 3902 3933 1096.74042 10.1016/j.cma.2004.09.007 (Pubitemid 40815237)
19. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial Intelligence through Simulated Evolution. Wiley, Chichester
20. Jong KD (1975) Analysis of the behavior of a class of genetic adaptive systems. Dissertation, University of Michigan
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, CA
22. Holland JH (1975) Adaptation in natural and artificial systems. Dissertation University of Michigan Press
23. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, Addison. Wesley, Boston
24. F Glover 1977 Heuristic for integer programming using surrogate constraints Decision Sci 8 1 156 166 10.1111/j.1540-5915.1977.tb01074.x
25. S Kirkpatrick C Gelatt M Vecchi 1983 Optimization by simulated annealing Science 220 4598 671 680 10.1126/science.220.4598.671 702485
26. Geem ZW, Kim JH, Loganathan GV (2001) a new heuristic optimization algorithm: harmony search. simul 76(2):60-68 (Pubitemid 32543074)
27. JH Kim ZW Geem ES Kim 2001 Parameter estimation of the nonlinear Muskingum model using harmony search J Am Water Resour Assoc 37 5 1131 1138 10.1111/j.1752-1688.2001.tb03627.x (Pubitemid 34009650)
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