Review of empirical modelling techniques for modelling of turning process

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@Article{Garg:2013:IJMIC,
  author =       "Akhil Garg and Yogesh Bhalerao and Kang Tai",
  title =        "Review of empirical modelling techniques for modelling
                 of turning process",
  journal =      "International Journal of Modelling, Identification and
                 Control, Vol. 20, No. 2, 2013",
  year =         "2013",
  month =        aug # "~31",
  volume =       "20",
  number =       "2",
  pages =        "121--129",
  keywords =     "genetic algorithms, genetic programming, empirical
                 modelling, turning, artificial neural networks, ANNs,
                 review, regression analysis, fuzzy logic, support
                 vector machines, SVM",
  ISSN =         "1746-6180",
  publisher =    "Inderscience Publishers",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  language =     "eng",
  URL =          "http://www.inderscience.com/link.php?id=56184",
  DOI =          "DOI:10.1504/IJMIC.2013.056184",
  abstract =     "The most widely and well known machining process used
                 is turning. The turning process possesses higher
                 complexity and uncertainty and therefore several
                 empirical modelling techniques such as artificial
                 neural networks, regression analysis, fuzzy logic and
                 support vector machines have been used for predicting
                 the performance of the process. This paper reviews the
                 applications of empirical modelling techniques in
                 modelling of turning process and unearths the vital
                 issues related to it.",
}

Genetic Programming entries for Akhil Garg Yogesh Bhalerao Kang Tai

Citations