Prediction of constant amplitude fatigue crack growth life of 2024 T3 Al alloy with R-ratio effect by GP

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Mohanty:2015:ASC,
  author =       "J. R. Mohanty and T. K. Mahanta and A. Mohanty and 
                 D. N. Thatoi",
  title =        "Prediction of constant amplitude fatigue crack growth
                 life of 2024 {T3 Al} alloy with R-ratio effect by
                 {GP}",
  journal =      "Applied Soft Computing",
  volume =       "26",
  pages =        "428--434",
  year =         "2015",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 neural network, Fatigue crack growth life, Fatigue
                 crack growth rate, Load ratio",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2014.10.024",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494614005353",
  abstract =     "The objective of this study is to develop a genetic
                 programming (GP) based model to predict constant
                 amplitude fatigue crack propagation life of 2024 T3
                 aluminium alloys under load ratio effect based on
                 experimental data and to compare the results with
                 earlier proposed ANN model. It is proved that genetic
                 programming can effectively interpret fatigue crack
                 growth rate data and can efficiently model fatigue life
                 of the material system under investigation in
                 comparison to ANN model.",
}

Genetic Programming entries for J R Mohanty T K Mahanta A Mohanty D N Thatoi

Citations