Improving Production Quality of a Hot Rolling Industrial Process via Genetic Programming Model

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Alaa F. Sheta and Hossam Faris and Ertan Oznergiz",
  title =        "Improving Production Quality of a Hot Rolling
                 Industrial Process via Genetic Programming Model",
  journal =      "International Journal of Computer Applications in
  year =         "2014",
  number =       "3/4",
  volume =       "49",
  pages =        "239--250",
  month =        "6 " # jun,
  note =         "Special Issue on: Computational Optimisation and
                 Engineering Applications",
  keywords =     "genetic algorithms, genetic programming, Production
                 Quality, Hot Rolling, Manufacturing Process, Neural
                 Networks, Fuzzy Logic",
  ISSN =         "1741-5047",
  owner =        "Hossam",
  publisher =    "Inderscience",
  timestamp =    "2014.04.15",
  DOI =          "doi:10.1504/IJCAT.2014.062360",
  size =         "12 pages",
  abstract =     "Satisfying the customers' need for manufacturing
                 plants and the demand for high-quality products becomes
                 more challenging nowadays. Manufacturers need to retain
                 advanced attributes of their products by applying
                 high-quality automation process. In this paper, a
                 genetic programming (GP) approach is applied in order
                 to develop three mathematical models for the force,
                 torque and slab temperature in the hot-rolling
                 industrial process. A frequency-based analysis using GP
                 is performed to provide an insight into the process
                 significant factors. The performance of the GP
                 developed models is evaluated with respect to the known
                 soft computing models explored in the literature.
                 Experimental data were collected from the Eregli Iron
                 and Steel Factory in Turkey and used to test the
                 performance of the GP models. Genetic programming shows
                 better performance modelling capabilities compared with
                 models-based artificial neural networks and fuzzy
  notes =        "HeuristicLab

Genetic Programming entries for Alaa Sheta Hossam Faris Ertan Oznergiz