A finite element based data analytics approach for modeling turning process of Inconel 718 alloys

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@Article{Vijayaraghavan:2016:JCP,
  author =       "V. Vijayaraghavan and A. Garg and Liang Gao and 
                 R. Vijayaraghavan and Guoxing Lu",
  title =        "A finite element based data analytics approach for
                 modeling turning process of Inconel 718 alloys",
  journal =      "Journal of Cleaner Production",
  year =         "2016",
  volume =       "137",
  pages =        "1619--1627",
  month =        "20 " # nov,
  keywords =     "genetic algorithms, genetic programming, Finite
                 element analysis, Machining, Turning, Inconel 718",
  ISSN =         "0959-6526",
  DOI =          "doi:10.1016/j.jclepro.2016.04.010",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0959652616302694",
  abstract =     "Turning is a primary metal cutting process deployed
                 extensively for producing components to required shape
                 and dimensions. A commonly used material is Inconel
                 718, which exhibits an inferior economic feasibility in
                 terms of turning due to its poor machinability
                 characteristics. A combined finite element based data
                 analytics model is introduced in this work. Finite
                 element modelling was used to predict the cutting force
                 while Genetic Programming was used to obtain the
                 mathematical relation between the process variables and
                 the cutting force. The weighted parameter analysis was
                 conducted on the mathematical model which revealed that
                 depth of cut and cutting angle exerts significant
                 influence on the cutting force. As turning process is
                 generally specified by a given depth of cut which
                 dictates the material removal rate, optimization of
                 tool cutting angle can result in enhanced power
                 savings. It is anticipated that the findings obtained
                 from this study can result in greater power savings in
                 turning process of hard-to-machine materials which can
                 lead to a sustainable manufacturing process.",
}

Genetic Programming entries for Venkatesh Vijayaraghavan Akhil Garg Liang Gao R Vijayaraghavan Guoxing Lu

Citations