A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Garg:2015:SEC,
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author = "Akhil1 Garg and V. Vijayaraghavan and
Jasmine Siu Lee Lam and Pravin M Singru and Liang Gao",
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title = "A molecular simulation based computational
intelligence study of a nano-machining process with
implications on its environmental performance",
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journal = "Swarm and Evolutionary Computation",
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volume = "21",
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pages = "54--63",
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year = "2015",
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ISSN = "2210-6502",
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DOI = "doi:10.1016/j.swevo.2015.01.001",
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URL = "http://www.sciencedirect.com/science/article/pii/S2210650215000115",
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abstract = "Determining the optimum input parameter settings
(temperature, rotational velocity and feed rate) in
optimising the properties (strength and time) of the
nano-drilling process can result in an improvement in
its environmental performance. This is because the
rotational velocity is an essential component of power
consumption during drilling and therefore by
determining its appropriate value required in
optimisation of properties, the trial-and-error
approach that normally results in loss of power and
waste of resources can be avoided. However, an
effective optimisation of properties requires the
formulation of the generalised and an explicit
mathematical model. In the present work, the
nano-drilling process of Boron Nitride Nanosheet (BNN)
panels is studied using an explicit model formulated by
a molecular dynamics (MD) based computational
intelligence (CI) approach. The approach consists of
nano scale modelling using MD simulation which is
further fed into the paradigm of a CI cluster
comprising genetic programming, which was specifically
designed to formulate the explicit relationship of
nano-machining properties of BNN panel with respect to
process temperature, feed and rotational velocity of
drill bit. Performance of the proposed model is
evaluated against the actual results. We find that our
proposed integrated CI model is able to model the
nano-drilling process of BNN panel very well, which can
be used to complement the analytical solution developed
by MD simulation. Additionally, we also conducted
sensitivity and parametric analysis and found that the
temperature has the least influence, whereas the
velocity has the highest influence on the properties of
nano-drilling process of BNN panel.",
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keywords = "genetic algorithms, genetic programming, Computational
intelligence, Nano-drilling, Boron nitride sheets,
Materials nano-machining",
- }
Genetic Programming entries for
Akhil Garg
Venkatesh Vijayaraghavan
Jasmine Siu Lee Lam
Pravin M Singru
Liang Gao
Citations