Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network

Journal of Materials Processing Technology

Volumn 190, Issue 1-3, 2007, Pages 199-203

  Aykut, Ş ^a   Golcu M ^a   Semiz, S ^a   Ergur H S ^b  

^a PAMUKKALE UNIVERSITY   (Turkey)

^b ESKISEHIR OSMANGAZI UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Cutting forces; Face milling; Stellite 6

Indexed keywords

ALGORITHMS; FEEDFORWARD CONTROL; MACHINABILITY; MILLING (MACHINING); NEURAL NETWORKS;

CUTTING FORCES; FACE MILLING; SCALED CONJUGATE GRADIENT (SCG); STELLITE 6;

CUTTING;

EID: 34248164412     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmatprotec.2007.02.045     Document Type: Article

References (30)

1. Agarwal S.C., and Ocken H. The microstructure and galling wear of a laser-melted cobalt-base hardfacing alloy. Wear 140 (1990) 223-233
2. Crook P. Properties and Selection: Non-ferrous Alloys and Special-Purpose Materials, 2 vol. 10 (1993), Metals Handbook, ASM Int., USA p. 446
3. Murray J.D., and Mc Alister A.J. Bull. Alloy Phase Diagrams 5 (1984) 90
4. S. Aykut, The investigation of effects of machinability on chip removal parameters for face milling of cobalt-based superalloy steels, Marmara University Institute of Science & Technology, Ph.D. Thesis, 2005.
5. Kuzucu V., Ceylan M., Celik H., and Aksoy I. Microstructure and phase analyses of Stellite plus 6 wt.% Mo alloy. J. Maler. Process. Technol. 69 (1997) 257-263
6. Mohamed K.E., Gad M.M.A., Nassef A.E., and El-Sayed A.W. Localized behaviour of powder metallurgy processed cobalt-based alloy Stellite 6 in chloride environments. Z. Metallkd. 90 (1999) 195-201
7. Diniz A.E., and Filho J.C. Influence of the relative positions of tool and workpiece on tool life, tool wear and surface in the face milling process. Wear 232 (1999) 67-75
8. Alti{dotless}ntaş Y. Manufacturing Automation (2000), Cambridge University Press, USA pp. 5-6
9. Bezce C.E., and Elbestawi M.A. A chip formation based analytic force model for oblique cutting. Int. J. Mach. Tools Manuf. 42 (2002) 529-538
10. Philipson R.H., and Ravindram A. Microstructure, an application of mathematical programming to metal cutting. Math. Program Stud. 11 (1979) 116-134
11. Hayes G.M., and Davis R.P. A discrete variable approach to machine parameter optimization. IIE Trans. 11 (1979) 155-159
12. Baǧci{dotless} E., and Aykut S. A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6). Int. J. Adv. Manuf. Technol. 29 (2006) 940-947
13. Cus F., and Balic J. Selection of cutting conditions and tool flow in flexible manufacturing system. Int. J. Manuf. Sci. Technol. 2 (2000) 101-106
14. Phillips D.T., and Beightler C.S. Optimization in tool engineering using geometric programming. AIIE Trans. 2 (1970) 355-360
15. Paulo Davim J., and Antonio C.A.C. Optimisation of cutting conditions in machining of aluminium matrix composites using a numerical and experimental model. J. Mater. Process. Technol. 112 (2001) 78-82
16. Milfelner M., and Cus F. System for simulation of cutting process. International Scientific Conference on the Occasion of the 50th Anniversary of Founding the Faculty of Mechanical Engineering Ostrava (2000) 349-352
17. Wong S.V., and Hamouda A.M.S. Machinability data representation with artificial neural network. J. Mater. Process. Technol. 138 (2003) 538-544
18. Zuperl U., and Cus F. Optimization of cutting conditions during cutting by using neural networks. Rob. Comput. Integr. Manuf. 19 (2003) 189-199
19. El-Mounayri H., Kishawy H., and Briceno J. Optimization of CNC ball end milling: a neural network-based model. J. Mater. Process. Technol. 166 (2005) 50-62
20. Ezugwua E.O., Fadarea D.A., Bonneya J., Da Silvaa R.B., and Salesa W.F. Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. Int. J. Mach. Tools Manuf. 45 (2005) 1375-1385
21. Tosun N., and Özler L. A study of tool life in hot machining using artificial neural networks and regression analysis method. J. Mater. Process. Technol. 124 (2002) 99-104
22. James LI C., and Tzeng T.C. Multimilling-insert wear assessment using non-linear virtual sensor, time-frequency distribution and neural networks. Mech. Syst. Signal Process. 14 6 (2000) 945-957
23. Balic J., and Korosec M. Intelligent tool path generation for milling of free surfaces using neural networks. Int. J. Mach. Tools Manuf. 42 (2002) 1171-1179
24. ®ed back-propagation algorithm in tool condition monitoring for faster convergence. J. Mater. Process. Technol. 98 (2000) 299-309
25. Dimla D.E., Lister P.M., and Leightont N.J. Neural network solutions to the tool condition monitoring problem in metal cutting-a critical review of methods. Int. J. Mach. Tools Manuf. 9 (1997) 1219-1241
26. ISO 8688-1 Tool Life Testing in Milling, Part I, Face Milling, first ed., 1989.
27. Matlab Version 6.5, Online Manual.
28. Bechtler H., Browne M.W., Bansal P.K., and Kecman V. New approach to dynamic modeling of vapour-compression liquid chillers: artificial neural networks. Appl. Energy 21 (2001) 941-953
29. Haykin S. Neural Networks: A Compherensive Foundation (1994), Macmillan, New York
30. Sha W. Comment on "Modeling of tribological properties of alumina fiber reinforced zinc-aluminum composites using artificial neural network" by K. Genel et al. [Mater. Sci. Eng. A 363 (2003) 203]. Mater. Sci. Eng. A 372 (2004) 334-335