Application of Genetic Programming on Temper Mill Datasets

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

@InProceedings{Kommenda:2009:LINDI,
  author =       "Michael Kommenda and Gabriel Kronberger and 
                 Stephan Winkler and Michael Affenzeller and Stefan Wagner and 
                 Leonhard Schickmair and Benjamin Lindner",
  title =        "Application of Genetic Programming on Temper Mill
                 Datasets",
  booktitle =    "2nd International Conference on Logistics and
                 Industrial Informatics, LINDI 2009",
  year =         "2009",
  month =        sep,
  pages =        "1--5",
  abstract =     "Temper rolling is essential for the quality of steel
                 sheets. The degree of temper rolling determines the
                 mechanical properties of the steel sheet and is highly
                 influenced by the rolling force or strip tension. Since
                 mathematical models generate unsatisfactory results for
                 the calculation of these two process parameters, other
                 methods for the presetting of tempers mills must be
                 used. The parameter presetting of temper mills is of
                 prime importance because it reduces the effort of
                 tuning these parameters later. Hence, the production
                 costs are reduced by minimizing the amount of wasted
                 material that does not fulfill the quality
                 requirements.

                 Genetic programming (GP) is an evolutionary inspired
                 and population based modeling technique and has been
                 successfully applied in different contexts. In this
                 paper we present first results of advanced genetic
                 programming concepts on large datasets from a temper
                 mill in comparison to linear regression (LR), support
                 vector machines (SVMs) and previous analysis on the
                 datasets. The use of GP shows an improvement compared
                 to previous work, but is still inferior to models
                 obtained by SVMs. A major advantage of GP compared to
                 support vector machines is that the identified models
                 are mathematical formulae which can be interpreted and
                 enable knowledge generation about the temper rolling
                 process.",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 inspired based modeling technique, linear regression
                 analysis, mathematical model, mechanical properties,
                 population based modeling technique, steel sheet
                 quality, strip tension, support vector machines, temper
                 mill dataset, temper rolling, data handling, hot
                 rolling, production engineering computing, rolling
                 mills, sheet metal processing, tempering",
  DOI =          "doi:10.1109/LINDI.2009.5258766",
  notes =        "Also known as \cite{5258766}",
}

Genetic Programming entries for Michael Kommenda Gabriel Kronberger Stephan M Winkler Michael Affenzeller Stefan Wagner Leonhard Schickmair Benjamin Lindner

Citations