TY - JOUR
T1 - Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms
AU - Bhattacharya, Baidurya
AU - Dinesh Kumar, G. R.
AU - Agarwal, Akash
AU - Erkoç, Şakir
AU - Singh, Arunima
AU - Chakraborti, Nirupam
N1 - Funding Information:
Financial and logistic support from TATA Steel is thankfully acknowledged. One of the authors (SE) would like to thank TUBITAK (The Scientific and Technological Research Council of Turkey) for partial support through the project TUBITAK-TBAG-107T142.
Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009/10
Y1 - 2009/10
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Fe-Zn system
KW - Genetic algorithms
KW - Hot-dip galvanizing
KW - Molecular dynamics
KW - Multi-objective optimization
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U2 - 10.1016/j.commatsci.2009.04.023
DO - 10.1016/j.commatsci.2009.04.023
M3 - Article
AN - SCOPUS:70249094618
SN - 0927-0256
VL - 46
SP - 821
EP - 827
JO - Computational Materials Science
JF - Computational Materials Science
IS - 4
ER -