A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance

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@Article{Garg:2015:SEC,
  author =       "Akhil1 Garg and V. Vijayaraghavan and 
                 Jasmine Siu Lee Lam and Pravin M Singru and Liang Gao",
  title =        "A molecular simulation based computational
                 intelligence study of a nano-machining process with
                 implications on its environmental performance",
  journal =      "Swarm and Evolutionary Computation",
  volume =       "21",
  pages =        "54--63",
  year =         "2015",
  ISSN =         "2210-6502",
  DOI =          "doi:10.1016/j.swevo.2015.01.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2210650215000115",
  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.",
  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