A predictable glass forming ability expression by statistical learning and evolutionary intelligence
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- @Article{TRIPATHI:2017:Intermetallics,
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author = "Manwendra K. Tripathi and P. P. Chattopadhyay and
Subhas Ganguly",
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title = "A predictable glass forming ability expression by
statistical learning and evolutionary intelligence",
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journal = "Intermetallics",
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volume = "90",
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pages = "9--15",
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year = "2017",
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keywords = "genetic algorithms, genetic programming,
Meta-modeling, Glass forming ability (GFA), Bulk
metallic glass (BMG), Principal component analysis
(PCA), Genetic programming (GP), Combinatorial
analysis",
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ISSN = "0966-9795",
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DOI = "doi:10.1016/j.intermet.2017.06.008",
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URL = "http://www.sciencedirect.com/science/article/pii/S0966979517302650",
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abstract = "This paper demonstrates how principal component
analysis of multivariate BMG alloy data and the genetic
programming of the extracted features in the form of
principal components can be used to develop a
meta-modeling scheme for GFA expression. The proposed
GFA model can estimate the glass forming potential of
an alloy from its composition data, unlike the
characteristic temperature based glass forming ability
expressions, consisting of Tg, Txand Tl. The BMG alloys
have been described by means of generic attributes of
the constituent elements and corresponding composition
of the alloy yielding a multi-dimensional descriptor
space for a 594 BMGs compiled from literature. The PCA
model of the data base plausibly reduced the
dimensionality into a two dimension in terms of two
extracted features by first two principle components
capturing the 82percent of the data knowledge.
Successively, these principle components are used to
develop a constitutive model for glass forming ability
using genetic programming. The combinatorial analysis
of the meta-model for GFA expression is applied to the
prediction of potential compositional zone in five
different experimentally explored ternary systems. The
predicted composition zones are discussed in the
context of available experimental data in literature
and the energy of formation of the stable phases in
respective alloy systems",
- }
Genetic Programming entries for
Manwendra K Tripathi
P P Chattopadhyay
Subhas Ganguly
Citations