Genetic algorithm based optimization for multi-physical properties of HSLA steel through hybridization of neural network and desirability function

Computational Materials Science

Volumn 45, Issue 1, 2009, Pages 104-110

  Das, P ^a   Mukherjee, S ^a   Ganguly, S ^b   Bhattacharyay, B K ^b   Datta, S ^b  

^a INDIAN STATISTICAL INSTITUTE   (India)

^b INDIAN INSTITUTE OF ENGINEERING SCIENCE AND TECHNOLOGY   (India)

Author keywords

Composite desirability; Desirability function; Genetic algorithm; High strength low alloy steel; Neural network; Strengthening mechanism

Indexed keywords

ALGORITHMS; ALLOYS; CARBON FIBER REINFORCED PLASTICS; FEEDFORWARD NEURAL NETWORKS; FUNCTIONS; GALLIUM ALLOYS; GENETIC ALGORITHMS; MECHANICAL PROPERTIES; MECHANISMS; METALLURGY; OPTIMIZATION; PROBABILITY DENSITY FUNCTION; STEEL; STEEL METALLURGY; STRENGTH OF MATERIALS; STRENGTHENING (METAL); TENSILE STRENGTH; YIELD STRESS;

COMPOSITE DESIRABILITY; DESIRABILITY FUNCTION; GENETIC ALGORITHM; HIGH STRENGTH LOW ALLOY STEEL; NEURAL NETWORK; STRENGTHENING MECHANISM;

NEURAL NETWORKS;

EID: 59749086490     PISSN: 09270256     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.commatsci.2008.03.050     Document Type: Article

References (24)

1. Das P., and Datta S. International Journal of Production Research 45 3 (2007) 699-717
2. Datta S., Sil J., and Banerjee M.K. ISIJ International 39 10 (1999) 986-990
3. Datta S., and Banerjee M.K. ISIJ International 44 5 (2004) 846-851
4. Datta S., and Banerjee M.K. ISIJ International 45 1 (2005) 121-126
5. Datta S., and Banerjee M.K. Materials Science and Engineering A 420 (2006) 254-264
6. Datta S., and Banerjee M.K. Scandinavian Journal of Metallurgy 33 (2004) 310-315
7. Datta S., Pettersson F., Ganguly S., Saxén H., and Chakraborti N. ISIJ International 47 8 (2007) 1193-2001
8. S.K. Ghosh, S. Ganguly, P.P. Chattopadhyay, S. Datta, Effect of Copper and Microalloying (Ti, B) Addition on Tensile Properties of HSLA Steels Predicted by ANN Technique, accepted in Ironmaking and Steelmaking, in press.
9. Chakraborti N. International Materials Reviews 49 (2004) 246-260
10. Jackiewicz J. Materials and Manufacturing Processes 20 (2005) 523-542
11. Mahfouf M., Jamei M., and Linkens D.A. Materials and Manufacturing Processes 20 (2005) 553-556
12. Egorov-Yegorov I.N., and Dulikravich G.S. Materials and Manufacturing Processes 20 (2005) 569-590
13. Ganguly S., Datta S., and Chakraborti N. Materials and Manufacturing Processes 22 (2007) 650-658
14. Haykin S. Neural Networks: A Comprehensive Foundation (1994), McMillan, New York
15. Nigrin A. Neural Networks for Pattern Recognition (1993), The MIT Press, Cambridge, MA
16. Zurada J.M. Introduction to Artificial Neural Systems (1992), PWS Publishing Company, Boston
17. Harrington Jr. E.C. Industrial Quality Control April (1965) 494-498
18. P.E. Gatza, R.C. McMillan, The use of experimental design and computerized data analysis in Elastomer Development Studies, Division of Rubber Chemistry, American Chemical Society fall meeting, Paper No. 6, Cincinnati, Ohio, October 1972, pp. 3--6.
19. Derringer G.C., and Suich R. Journal of Quality Technology 12 4 (1980) 214-219
20. Derringer G.C. Quality Progress June (1994) 51-58
21. Del Castillo E., Montgomery D.C., and McCarville D. Journal of Quality Technology 28 3 (1996) 337-345
22. Das P. Quality Engineering 11 3 (1999) 365-368
23. Melanie M. Journal of Quality Technology 28 3 (1998) 337-345
24. Gen M., and Cheng R. Genetic Algorithms and Engineering Optimization (2000), Wiley-IEEE