Prediction of surface roughness in turning operations by computer vision using neural network trained by differential evolution algorithm

International Journal of Advanced Manufacturing Technology

Volumn 51, Issue 9-12, 2010, Pages 965-971

  Yang, S H ^a   Natarajan, U ^a   Sekar, M ^a   Palani, S ^b  

^a Kyungpook National University   (South Korea)

^b MOUNT ZION COLLEGE OF ENGINEERING AND TECHNOLOGY   (India)

Author keywords

ANN; Backpropagation; DEA; Turning

Indexed keywords

ANN; ARTIFICIAL NEURAL NETWORK; CONVERGENCE SPEED; CUTTING SPEED; DEA; DEPTH OF CUT; DIFFERENTIAL EVOLUTION ALGORITHMS; ERROR PERCENTAGE; FEED-RATES; FUNCTIONAL ASPECTS; GRAY LEVELS; INPUT PARAMETER; OUTPUT PARAMETERS; PRODUCT QUALITY; SURFACE IMAGE; TURNING OPERATIONS; WORK PIECES;

BACKPROPAGATION; COMPUTER VISION; EVOLUTIONARY ALGORITHMS; METAL ANALYSIS; NEURAL NETWORKS; SURFACE PROPERTIES; TURNING;

SURFACE ROUGHNESS;

EID: 78650171110     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-010-2668-5     Document Type: Article

References (29)

1. B-Y Lee Y-S Tarng 2001 Surface roughness inspection by computer vision in turning operations Int J Adv Manuf Technol 41 1251 1263
2. SY Ho, et al. 2002 Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system Int J Machine Tools Manuf 42 1441 1446 10.1016/S0890-6955(02)00078-0
3. M-B Kiran, et al. 1998 Evaluation of surface roughness by vision system Int J Machine Tools Manuf 38 5-6 685 690 10.1016/S0890-6955(97)00118-1
4. B Dhanasekaran, et al. 2008 Evaluation of surface roughness based on monochromatic speckle correlation using image processing Precision Eng 32 196 206 10.1016/j.precisioneng.2007.08.005 (Pubitemid 351615438)
5. I Yamaguchi, et al. 2004 Measurement of surface roughness by speckle correlation Soc Photo-Optical Instrum Eng 43 11 2753 61
6. U Persson 1993 Measurement of surface roughness on rough machined surface using speckle correlation and image analysis Wear 160 221 225 10.1016/0043-1648(93)90424-K (Pubitemid 23609022)
7. U Persson 1992 Real time measurement of surface roughness on ground surfaces using speckle contrast technique Opt Laser Eng 17 61 67 10.1016/0143-8166(92)90012-V 1151254 (Pubitemid 23593935)
8. HH Shahabi MM Ratnam 2009 Non-contact roughness measurement of turned parts using machine vision Int J Adv Manuf Technol 10.1007/s001 70-009-2101-0
9. HH Shahabi MM Ratnam 2008 Assessment of flank wear and nose radius wear from workpiece roughness profile in turning operation using machine vision Int J Adv Manuf Technol 10.1007/s001 70-008-1688-x
10. HH Shahabi MM Ratnam 2008 In-cycle monitoring of tool nose wear and surface roughness of turned parts using machine vision Int J Adv Manuf Technol 40 1148 1157 10.1007/s001 70-008-1430-8 10.1007/s00170-008-1430-8
11. H Zhongxiang, et al. 2008 Evaluation of three dimensional surface roughness parameters based on digital image processing Int J Adv Manuf Technol 40 342 348 10.1007/s001 70-007-1357-5 10.1007/s00170-007-1357-5
12. Eberhart R-C, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimisation. The 7th Annual Conference on Evolutionary Programming, San Diego, USA
13. Rajasekaran S, Vijayalakshmi Pai GA (1996) Genetic algorithm based weight determination for back-propagation networks. Proceedings of Fourth International Conference on Advanced Computing, pp 73-79
14. Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proceedings of IEEE International conference on neural networks IV, pp 1942-1948
15. J-X Du, et al. 2007 Shape reconstruction based on neural networks trained by differential evolution algorithm Neurocomputing 70 896 903
16. GD Magoulas VP Plagianakos MN Vrahatis 2004 Neural network-based colonoscopic diagnosis using on-line learning and differential evolution Applied Soft Computing 4 1 369 379 10.1016/j.asoc.2004.01.005 (Pubitemid 38781712)
17. L dos Santos Coelho, et al. 2010 Model-free adaptive control design using evolutionary-neural compensator Expert Syst Appl 37 1 499 508 10.1016/j.eswa.2009.05.042
18. Nikunj Chauhan V Ravi D Karthik Chandra 2009 Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks Expert Syst Appl 36 4 7659 7665 10.1016/j.eswa.2008.09.019
19. L dos Santos Coelho FA Guerra 2008 B-spline neural network design using improved differential evolution for identification of an experimental nonlinear process Applied Soft Computing 4 8 1513 1522 10.1016/j.asoc.2007.10.015
20. A Basturk E Gunay 2009 Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm Expert Syst Appl 36 2 2645 2650 10.1016/j.eswa.2008.01.082
21. J Ilonen, et al. 2003 Differential evolution training algorithm for feed-forward neural networks Neural Process Lett 17 93 105 10.1023/A: 1022995128597
22. Zelinka I, Lampinen J (1999) An evolutionary learning algorithms for neural networks. Fifth International Conference on Soft Computing: MENDEL'99:410-414
23. Storn R, Price K (2005) Differential evolution-a practical approach to global optimization. Springer, Berlin
24. R Storn K Price 1997 Differential evolution-a simple evolution strategy for fast optimization Dr. Dobb's J 22 4 18 24 1479553
25. Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA, USA
26. D-G Mayer, et al. 2005 Differential evolution-an easy and efficient evolutionary algorithm for model optimization Agric Syst 83 315 328 10.1016/j.agsy.2004.05.002 (Pubitemid 40118184)
27. Coello CA et al (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, Heidelberg
28. H-A Abbass R Sarker 2002 The Pareto differential evolution algorithm Int J Artif Intell Tools 11 No.4 531 552 10.1142/S0218213002001039
29. Babu B-V, Rakesh Angira (2002) A differential evolution approach for global optimization of MNLP problems. Proceedings of 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'02), Singapore, paper no. 1033, vol. 2, pp 880-884, November 18-22