Modeling Hot Rolling Manufacturing Process Using Soft Computing Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Faris2013a,
  author =       "Hossam Faris and Alaa Sheta and Ertan Oznergiz",
  title =        "Modeling Hot Rolling Manufacturing Process Using Soft
                 Computing Techniques",
  journal =      "International Journal of Computer Integrated
                 Manufacturing",
  year =         "2013",
  volume =       "26",
  number =       "8",
  pages =        "762--771",
  keywords =     "genetic algorithms, genetic programming, hot rolling
                 process, industrial process",
  publisher =    "Taylor & Francis",
  ISSN =         "0951-192X",
  URL =          "http://www.tandfonline.com/doi/pdf/10.1080/0951192X.2013.766937",
  DOI =          "doi:10.1080/0951192X.2013.766937",
  size =         "10 pages",
  abstract =     "Steel making industry is becoming more competitive due
                 to the high demand. In order to protect the market
                 share, automation of the manufacturing industrial
                 process is vital and represents a challenge. Empirical
                 mathematical modelling of the process was used to
                 design mill equipment, ensure productivity and service
                 quality. This modelling approach shows many problems
                 associated to complexity and time consumption.
                 Evolutionary computing techniques show significant
                 modelling capabilities on handling complex non-linear
                 systems modelling. In this research, symbolic
                 regression modelling via genetic programming is used to
                 develop relatively simple mathematical models for the
                 hot rolling industrial non-linear process. Three models
                 are proposed for the rolling force, torque and slab
                 temperature. A set of simple mathematical functions
                 which represents the dynamical relationship between the
                 input and output of these models shall be presented.
                 Moreover, the performance of the symbolic regression
                 models is compared to the known empirical models for
                 the hot rolling system. A comparison with experimental
                 data collected from the Ere[gtilde]li Iron and Steel
                 Factory in Turkey is conducted for the verification of
                 the promising model performance. Genetic programming
                 shows better performance results compared to other soft
                 computing approaches, such as neural networks and fuzzy
                 logic.",
  notes =        "http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tcim20",
}

Genetic Programming entries for Hossam Faris Alaa Sheta Ertan Oznergiz

Citations