Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms

Bhattacharya B., Kumar G. R. D., Agarwal A., ERKOÇ Ş., Singh A., Chakraborti N.

COMPUTATIONAL MATERIALS SCIENCE, vol.46, no.4, pp.821-827, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 4
  • Publication Date: 2009
  • Doi Number: 10.1016/j.commatsci.2009.04.023
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.821-827
  • Keywords: Fe-Zn system, Hot-dip galvanizing, Molecular dynamics, Multi-objective optimization, Artificial neural networks, Genetic algorithms, MATERIALS SCIENCE, MATERIALS DESIGN, IDENTIFICATION, OPTIMIZATION, SIMULATIONS, NETWORKS, EXCHANGE, FRACTURE
  • Middle East Technical University Affiliated: No


Failure behavior of Zn coated Fe is simulated through molecular dynamics (MD) and the energy absorbed at the onset of failure along with the corresponding strain of the Zn lattice are computed for different levels of applied shear rate. temperature and thickness. Data-driven models are constructed by feeding the MD results to an evolutionary neural network. The outputs of these neural networks are utilized to carry out a multi-objective optimization through genetic algorithms, where the best possible tradeoffs between two conflicting requirements, minimum deformation and maximum energy absorption at the onset of failure, are determined by constructing a Pareto frontier. (C) 2009 Elsevier B.V. All rights reserved.