Predictive modeling of deposition rate in electro-deposition of copper-tin using regression and artificial neural network

Journal of Electroanalytical Chemistry

Volumn 636, Issue 1-2, 2009, Pages 30-35

  Subramanian, K ^a   Periasamy, V M ^b   Pushpavanam, Malathy ^c   Ramasamy, K ^d  

^a ALAGAPPA CHETTIAR COLLEGE OF ENGINEERING AND TECHNOLOGY   (India)

^b B S ABDUR RAHMAN CRESCENT INSTITUTE OF SCIENCE AND TECHNOLOGY   (India)

^c CENTRAL ELECTROCHEMICAL RESEARCH INSTITUTE   (India)

^d Shanmuganathan Engineering College   (India)

Author keywords

Anova; Deposition rate; Electroplating; Neural network; Regression

Indexed keywords

BACKPROPAGATION; BRONZE; COPPER; CYANIDES; DEPOSITION; DEPOSITION RATES; FLUORESCENCE SPECTROSCOPY; PROTECTIVE COATINGS; REGRESSION ANALYSIS; TIN;

ANOVA; ARTIFICIAL NEURAL NETWORK; COATING COMPOSITIONS; COPPER-TIN ALLOY; CYANIDE BATHS; PREDICTIVE MODELING; REGRESSION; REGRESSION MODEL; X RAY FLUORESCENCE SPECTROSCOPY;

NEURAL NETWORKS;

EID: 70350786611     PISSN: 15726657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jelechem.2009.09.003     Document Type: Article

References (23)

1. Lamb V.A., and Valentine D.R. Plating 52 (1965) 1289-1311
2. Balaji R., Pushpavanam M., Yogesh Kumar K., and Subramanian K. Surf. Coat. Technol. 201 (2006) 3205-3211
3. Ramanathan K., Periasamy V.M., and Natarajan U. Indian Surf. Finish. IV (2007) 258-264
4. Ramanathan K., Periasamy V.M., and Natarajan U. Port. Electrochim. Acta 26 4 (2008) 361-368
5. Cukrowska E., Trnkova L., Kizek R., and Havel J. J. Electroanal. Chem. 503 (2001) 117-124
6. Parthiban T., Ravi R., and Kalaiselvi N. Electrochim. Acta 53 (2007) 1877-1882
7. Subramanian K., Periasamy V.M., Pushpavanam M., and Ramasamy K. Port. Electrochim. Acta 27 1 (2009) 47-55
8. K. Subramanian, V.M. Periasamy, Malathy Pushpavanam, K. Sivakumar, in: Proc. 2nd International and 23rd AIMTDR Conference, IITM-Chennai, 15-17 December, 2008, pp. 157-162.
9. K. Subramanian, V.M. Periasamy, Malathy Pushpavanam, K. Ramasamy, Abstracts of International Conference on Advanced Manufacturing and Automation, Kalasalingam University, Krishnankoil, 26-28 March, 2009, p. 96.
10. Benyounis K.Y., Olabi A.G., and Hashmi M.S.J. J. Mater. Process. Technol. 164-165 (2005) 978-985
11. Kim I.S., Son J.S., Park C.E., Lee C.W., and Yarlagadda Prasad K.D.V. J. Mater. Process. Technol. 130-131 (2002) 229-234
12. Murugan N., and Gunaraj V. J. Mater. Process. Technol. 168 (2005) 478-487
13. Choudhury S.K., Jain V.K., and Rama Rao Ch.V.V. Int. J. Machine Tools Manufact. 39 (1999) 489-504
14. Das S., Roy R., and Chattopadhyay A.B. Int. J. Machine Tools Manufact. 36 7 (1996) 789-797
15. Dornfield D.A. Neural network sensor fusion for tool condition monitoring. Ann CIRP 39 1 (1990) 1
16. Das S., Bandyopadhyay P.P., and Chattopadhyay A.B. J. Mater. Process. Technol. 63 (1997) 187-192
17. Natarajan U., Periasamy V.M., and Saravanan R. Int. J. Adv. Manufact. Technol. 31 (2007) 871-876
18. Lee J.H., Kim D.E., and Lee S.J. Mech. Syst. Signal Process. 10 3 (1996) 265-276
19. Özel T., and Karpat Yigit. Int. J. Machine Tools Manufact. (2004) 1-13
20. Montgomery D.C. Design and Analysis of Experiments. fourth ed. (1997), John Wiley and Sons, New York
21. Hayken S. Neural networks: A Comprehensive Foundation (1999), Prentice Hall, Berlin
22. Reed R.D. Neural Smithing (1999), MIT Press, Cambridge, MA
23. Hagan M., Beale M., and Demuth H. Neural Network Design (1996), PWS Publishing Company, Boston, MA