Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process

Expert Systems with Applications

Volumn 39, Issue 3, 2012, Pages 2397-2407

  Mukherjee, Indrajit ^a   Routroy, Srikanta ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

^b BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE   (India)

Author keywords

Back propagation neural network; Gradient descent algorithm; Levenberg Marquardt algorithm; Multiple response; Quasi Newton algorithm

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; GRADIENT DESCENT ALGORITHMS; LEVENBERG-MARQUARDT ALGORITHM; MULTIPLE RESPONSE; QUASI-NEWTON ALGORITHM;

BACKPROPAGATION ALGORITHMS; GRINDING (COMMINUTION); GRINDING (MACHINING); MATHEMATICAL MODELS; NEURAL NETWORKS;

PROCESS CONTROL;

EID: 80255123452     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.08.087     Document Type: Article

References (68)

1. Ampazis, N.; & Perantonis, S. J. (2000). Levenberg-Marquardt algorithm with adaptive momentum for the efficient training of feedforward networks. In Proceedings of IEEE & INNS international joint conference on neural networks, IJCNN 2000, paper no. NN0401, Como, Italy, (CD proceedings) (pp. 126-131).
2. I.A. Basheer, and M. Hajmeer Artificial neural networks: Fundamentals, computing, design, and application Journal of Microbiological Methods 43 2000 3 31
3. D.M. Bates, and D.G. Watts Nonlinear regression analysis and its applications 1988 John Wiley and Sons New York
4. M.S. Bazaraa, H.D. Sherali, and C.M. Shetty Nonlinear programming 2nd ed. Theory and algorithms 2004 Wiley India
5. G.E.P. Box, and N.R. Draper Empirical model-building and response surface 1987 John Willey and Sons New York
6. C. Brezinski A classification of quasi-Newton methods Numerical Algorithms 33 2003 123 135
7. H. Chen, J. Lin, Y. Yang, and C. Tsai Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach Expert Systems with Applications 37 2010 7147 7153
8. Y.T. Chen, and S.R.T. Kumara Fuzzy logic and neural network for design of process parameters: A grinding process application International Journal of Production Research 36 2 1998 395 415 (Pubitemid 128565057)
9. T.J. Choi, N. Subrahmanya, H. Li, and C.Y. Shin Generalized practical models of cylindrical plunge grinding processes International Journal of Machine Tools and Manufacture 48 2008 61 72 (Pubitemid 47576556)
10. D.W. Coit, B.T. Jackson, and A.E. Smith Static neural network process models: Considerations and case studies International Journal of Production Research 36 11 1998 2953 2967 (Pubitemid 128568669)
11. F. Cus, U. Zuperl, and M. Milfelner Dynamic neural network approach for tool cutting force modelling of end milling operations International Journal of General Systems 35 5 2006 603 618 (Pubitemid 44728628)
12. O.A. Dombayci, and M. Golcu Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey Renewable Energy 34 4 2009 1158 1161
13. H. El-Hofy Advanced machining processes: Nontraditional and hybrid machining processes 2005 MacGraw-Hill New York
14. F.T. Farago Abrasive methods engineering 1976 Industrial Press Inc NY
15. C. Feng, X. Wang, and Z. Yu Neural networks modelling of honing surface roughness parameter defined by ISO 13565 SIAM Journal of Manufacturing Systems 21 8 2002 1 35
16. H. Fishcher R.I. King, R.S. Hahn, Honing in handbook of modern grinding technology 1986 Chapman and Hall New York 301 336
17. L. Fu Neural network in computer intelligence 2003 Tata McGraw Hill India
18. J.J. Govindhasamy, S.F. MacLoone, G.W. Irwin, J.J. French, and R.P. Doyle Neural modelling, control and optimization of an industrial grinding process Control Engineering Practice 13 2005 1243 1258 (Pubitemid 40870023)
19. R.E. Haber, and A. Alique Intelligent process supervision for predicting tool wear in machining processes Mechatronics 13 2003 825 849
20. M.T. Hagan, H.B. Demuth, and M. Beale Neural network design 2002 Thomson Learning India
21. D. Howard, and M. Beale Neural network toolbox: For use with Matlab; user's Guide, version 3 1998 The Mathworks Inc USA
22. R.K. Jain, V.K. Jain, and P.K. Kalra Modelling of abrasive flow machining process: A neural network approach Wear 231 1999 242 248 (Pubitemid 29466709)
23. P. Krajnik, J. Kopac, and A. Sluga Design of grinding factors based on response surface methodology Journal of Materials Processing Technology 2005 162 163 629-636
24. R.I. King, and R.S. Hahn Handbook of modern grinding technology 1986 Chapman and Hall New York
25. S. Kumar, and S.K. Choudhury Prediction of wear and surface roughness in electro-discharge diamond grinding Journal of Materials Processing Technology 191 2007 206 209 (Pubitemid 46873992)
26. J. Kwak Application of Taguchi and response surface methodologies for geometric error in surface grinding process International Journal of Machine Tools and Manufacturing 45 2005 327 334
27. L.H.S. Luong, and T.A. Spedding A neural-network system for predicting machining behaviour Journal of Materials Processing Technology 52 2002 585 591
28. K. Madsen, H.B. Nielsen, and O. Tingleff Methods for non-linear least squares problems 2nd ed. 2004 Technical University of Denmark
29. T.M.A. Maksoud, and M.R. Atia Review of intelligent grinding and dressing operations Machine Science and Technology 8 2 2004 263 276
30. S. Malkin Thermal analysis of grinding Annals of CIRP 56 2 2007 760 782 (Pubitemid 350122468)
31. S. Malkin Grinding of metals: Theory and application Journal of Applied Metalwork 3 1984 95 109
32. Markos, S.; Viharos, Zs. J.; & Monostori, L. (1998). Quality-oriented, comprehensive modelling of machining processes. In 6th ISMQC IMEKO symposium on metrology for quality control in production (pp. 67-74).
33. D.W. Marquardt An algorithm for least-squares estimation of nonlinear parameters Journal Society Industrial Applied Mathematics 11 2 1963 431 441
34. A. Mitra Fundamentals of quality control and improvement 2nd ed. 2001 Pearson Education Asia, Inc India
35. D.C. Montgomery Introduction to statistical quality control 2nd ed. 1991 John Wiley and Sons New York
36. D.C. Montgomery Design and analysis of experiment 5th ed. 2001 Wiley and sons Inc New York
37. D.C. Montgomery, and E.A. Peck Introduction to linear regression analysis 2nd ed. 1992 John Wiley and Sons, Inc New York
38. P.N. Moulik, H.T.Y. Yang, and S. Chandrasekhar Simulation of thermal stresses due to grinding, international Journal of Mechanical Society 43 2001 831 851
39. Mukherjee, I. (2007). Modelling and optimization of abrasive metal cutting processes. Ph.D. Thesis, Department of Industrial Engineering and Management. Indian Institute of Technology, Kharagpur, India.
40. I. Mukherjee, and P.K. Ray A review of optimization techniques in metal cutting processes International Journal of Computers and Industrial Engineering 50 2006 15 34 (Pubitemid 43816785)
41. I. Mukherjee, and P.K. Ray A systematic solution methodology for inferential multivariate modelling of industrial grinding process International Journal of Materials Processing Technology 196 2008 379 392 (Pubitemid 350192208)
42. I. Mukherjee, and P.K. Ray Technology management of grinding process using quality control methods: A multivariate regression and neural network application Innovation and Technology Management Advanced Research Series 2008 Macmillan India 238 246
43. I. Mukherjee, and P.K. Ray A modified tabu search strategy for multiple-response grinding process optimization International Journal of Intelligent Systems Technologies and Applications 4 1/2 2008 97 122
44. M. Nalbant, H. Gokkaya, I. Toktas, and G. Sur 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 Robotics and Computer-Integrated Manufacturing 25 1 2009 211 223
45. S.S. Oren On quasi-Newton and pseudo-Newton algorithms Journal of Optimization Theory and Applications 20 2 1976 155 170
46. K.L. Petri, R.E. Billo, and B. Bidanda A neural network process model for abrasive flow machining operations Journal of Manufacturing Systems 17 1 1998 52 64 (Pubitemid 128447046)
47. R.L. Rardin Optimization in operations research 2003 Pearson Education India
48. A.C. Rencher Method of multivariate analysis 1995 John Wiley and Sons, Inc USA
49. A.C. Rencher Multivariate statistical inference and applications 1998 John Wiley and Sons, Inc New York
50. D.E. Rumelhart, G.E. Hilton, and R.J. Williams Learning representations by back-propagating errors Nature 323 9 1986 533 536 (Pubitemid 16025374)
51. B. Robitaille, B. Marcos, M. Veillejte, and G. Payre Modified quasi-Newton methods for training neural networks Computers and Chemical Engineering 20 9 1996 1133 1140
52. B.W. Rowe, L.I. Yan, I. lnasaki, and S. Malkin Applications of artificial intelligence in grinding Annals of CIRP 43 2 1994 521 531
53. G. Sathyanarayanan, I.J. Lin, and M. Chen Neural network modelling and multiobjective optimization of creep feed grinding of superalloys International Journal of Production Research 30 10 1992 2421 2438 (Pubitemid 23557594)
54. M. Sedighi, and D. Afshari Creep feed grinding optimization by an integrated GA-NN system Journal of Intelligent Manufacturing 21 6 2010 657 663
55. S. Shaji, and V. Radhakrisnan Analysis of process parameters in surface grinding with graphite as lubricant based on Taguchi method Journal of Material Processing Technology 2003 1 9
56. D.F. Shanno, and P.C. Kettler Optimal conditioning of quasi-newton methods Mathematics of Computation 24 111 1970 657 664
57. Y.C. Shin, and P. Vishnupad Neuro-fuzzy control of complex manufacturing processes International Journal of Production Research 34 12 1996 3291 3309 (Pubitemid 126530371)
58. P.H. Sorensen, M. Norgaard, O. Ravn, and N.K. Poulsen Implementation of neural network based non-linear predictive control Neurocomputing 28 1999 37 51 (Pubitemid 29456840)
59. T. Tawakoli, M. Rabiey, and M. Lewis Neural Network prediction of dry grinding by CBN grinding wheel using special conditioning International Journal of Materials and Product Technology 35 1/2 2009 118 133
60. J.S. Torrecilla, L. Otero, and P.D. Sanz Optimization of an artificial neural network for thermal/pressure food processing: Evaluation of training algorithms Computers and Electronics in Agriculture 56 2007 101 110 (Pubitemid 46436171)
61. K. Tsai, and P. Wang Predictions of surface finish in electrical discharge machine based upon neural network models International Journal of Machine Tools and Manufacture 41 2001 1385 1403
62. M. Urbaniak Evaluation system for the cutting properties of grinding wheels Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 218 11 2004 1491 1498
63. A. Vafaeesefat Optimum creep feed grinding process conditions for rene 80 supper alloy using neural network International Journal of Precision Engineering and Manufacturing 10 3 2009 5 11
64. P.D. Wasserman Advanced methods in neural computing 1993 Van Nostrand Reinhold New York
65. B. Widrow, and M.A. Lehr 30 Years of adaptive neural networks: Perceptron, Madaline, and back propagation Proceedings of IEEE 78 9 1990 1415 1442
66. C. Xu, and J. Zhang A survey of quasi-Newton equations and quasi-newton methods for optimization Annals of Operations Research 103 2001 213 234 (Pubitemid 33634409)
67. L.A. Zadeh Fuzzy sets Information and Control 8 1965 338 353
68. H.C. Zhang, and S.H. Huang Application of neural network in manufacturing - a state of art survey International Journal of Production Research 33 3 1995 705 728