Improving Production Quality of a Hot Rolling Industrial Process via Genetic Programming Model
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @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