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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{HR2014,

• 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 Technology",
• 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 logic.",
• notes = "HeuristicLab http://www.inderscience.com/jhome.php?jcode=ijcat",

}

Genetic Programming entries for Alaa Sheta Hossam Faris Ertan Oznergiz

Citations