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

@article{Das2009GeneticAB,
  title={Genetic algorithm based optimization for multi-physical properties of HSLA steel through hybridization of neural network and desirability function},
  author={P. Das and Sandip Mukherjee and S. Ganguly and B. K. Bhattacharyay and S. Datta},
  journal={Computational Materials Science},
  year={2009},
  volume={45},
  pages={104-110}
}
A genetic algorithm (GA) based optimization of the composite desirability of the tensile properties of thermomechanically processed high strength low alloy (HSLA) steel plates is proposed. The empirical relationship between each of the mechanical properties of steel, viz. tensile strength, yield strength and elongation is developed with the composition and rolling process parameters using feedforward neural network models. A composite desirability scale is then used through individual… Expand
26 Citations
Multiresponse Optimization of Mechanical Properties and Formability of Hot Rolled Microalloyed Steels
  • 4
...
1
2
3
...

References

SHOWING 1-10 OF 22 REFERENCES
...
1
2
3
...