Improved method for grinding force prediction based on neural network

International Journal of Advanced Manufacturing Technology

Volumn 39, Issue 7-8, 2008, Pages 656-668

  Amamou, Ridha ^a   Ben Fredj, Nabil ^a   Fnaiech, Farhat ^a  

^a UNIVERSITY OF TUNIS   (Tunisia)

Author keywords

Experimental design; Genetic algorithms; Grinding force components prediction; Neural networks

Indexed keywords

ALGORITHMS; BACKPROPAGATION; ELECTRIC FAULT LOCATION; EXPERIMENTS; FORECASTING; GENETIC ALGORITHMS; GRINDING (COMMINUTION); GRINDING (MACHINING); REGRESSION ANALYSIS; STATISTICS; VEGETATION;

ACCURATE PREDICTIONS; ARTIFICIAL NEURAL NETWORKS; EXPERIMENTAL DESIGN; EXPERIMENTAL DESIGNS; GENERALIZATION PERFORMANCES; GRINDING FORCE COMPONENTS PREDICTION; GRINDING FORCES; IMPROVED METHODS; INPUT LAYERS; OPTIMAL SETS; POWER MODELS; REGRESSION MODELS; SIGNIFICANT FACTORS; TESTING DATUMS; TRAINING DATUMS;

NEURAL NETWORKS;

EID: 56749174182     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-007-1264-9     Document Type: Article

References (30)

1. ZC Li B Lin YS Xu J Hu 2002 Experimental studies on grinding forces and force ratio of the unsteady-state grinding technique J of Mater Process Technol 129 76 80
2. Malkin S (1989) Grinding technology theory and applications of machining with abrasives. Ellis Horwood Ltd., Chichester
3. W König K Steffens 1982 A numerical method to describe the kinematics of grinding CIRP Ann 31 1 201 204
4. K Steffens W König 1983 Close loop simulation of grinding CIRP Ann 32 1 255 258
5. HK Tönshoff J Peters I Inasaki T Paul 1992 Modelling and simulation of grinding process CIRP Ann 41 2 677 688
6. E Brinksmeier HK Tönshoff I Inasaki J Peddinghauss 1993 Basic parameters in grinding report on a cooperative work in STC «G» CIRP Ann 42 2 795 799
7. M Thomas Y Beauchamp YA Youssef 1997 An experimental design for surface roughness and built-up edge formation in lathe dry turning Int J Qual Sci 2 3 167 180
8. YA Youssef 1994 Comparison of a full factorial experiment to fractional and Taguchi design in a lathe dry turning operation Comput Ind Eng 27 1-4 59 62
9. KM Tsai P-J Wang 2001 Semi-empirical model of surface finish on electrical discharge machining Int J Mach Tools Manuf 41 1455 1477
10. JL Lin KS Wang BH Yan YS Tarng 2000 Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics J Mater Process Technol 102 48 55
11. Y Ali LC Zhang 1997 Estimation of residual stress induced by grinding using a fuzzy logic approach J Mater Process Technol 63 875 880
12. Rao KGRK, Kuppuswamy G (1997) Fuzzy logic interface application to the electro-chemical grinding process analysis. Proc 17th AIMTDR Conf REC Warangal:329-334
13. AK Nandi DK Pratihar 2004 Design of a genetic-fuzzy system to predict surface finish and power requirement in grinding Fuzzy Sets Syst 148 3 487 504
14. R Saravanan M Sachithanandam 2001 Genetic algorithm (GA) for multivariable surface grinding process optimisation using a multi-objective function model Int J Adv Manuf Technol 17 330 338
15. AV Gopal PV Rao 2003 The optimisation of the grinding of silicon carbide with diamond wheels using genetic algorithms Int J Adv Manuf Technol 22 475 480
16. R Saravanan P Asokan M Sachidanandam 2002 A multi-objective genetic algorithm (GA) approach fro optimization of surface grinding operations Int J Mach Tools Manuf 42 1327 1334
17. MS Shunmugam SVB Reddy TT Narendran 2000 Selection of optimal conditions in multi-pass face-milling using a genetic algorithm Int J Mach Tools Manuf 40 401 414
18. YH Tsai JC Chen S-J Lou 1999 An in-process surface recognition system based on neural networks in end milling cutting operations Int J Mach Tools Manuf 39 583 605
19. Zouaghi N, Ichida Y, Ben Fredj N, Kimura N (1996) Grinding mode identification of silicon carbide by using neural network. In: Proceeding of the third international conference on progress of cutting and grinding Vol. III (JSPE, Osaka,) 342-347
20. M Sakakura I Inasaki 1991 A neural network approach to the decision-making process for grinding operation CIRP Ann 41 1 353 356
21. DH Kim DJ Kim B Kim 1999 The application of neural networks and statistical methods to process design in metal forming Int J Adv Manuf Technol 15 886 894
22. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford UK
23. Montgomery DC (1992) Design and analysis of experiments, 3rd edn. John Wiley & son, New York
24. N Ben Fredj R Amamou 2006 Ground surface roughness prediction based upon experimental design and neural network models Int J Adv Manuf Technol 31 24 36
25. F Cus J Balic 2003 Optimization of cutting process by GA approach Robot Comput Integr Manuf 19 113 121
26. Haykin S (1999) Neural networks a comprehensive foundation. Prentice Hall Inc., Upper Saddle River New Jersy
27. JS Heo Y Koo SS Choi 2004 Grinding characteristics of conventional and ELID methods in difficult-to-cut and hardened brittle material J Mat Process Technol 155-156 1196 1200
28. Andrew C, Howes TD, Pearce TRA (1985) Creep feed grinding. Hotlt, Rinehart and Winston Ltd. Eastbourne East Sussex
29. Tangy JC (1985) Rectification connaissances de base et données pratiques. Centre Technique des Industries Mécaniques
30. R Snoeys J Peters 1974 The significance of the chip thickness in grinding CIRP Ann 23 2 227 237