Comparison Analysis of Regression Models Based on Experimental and Fem Simulation Datasets used to Characterize Electrolytic Tinplate Materials

  1. Fernández-Martínez, R. 1
  2. Lostado-Lorza, R. 2
  3. Illera-Cueva, M. 2
  4. Escribano-García, R. 2
  5. Mac Donald, B.J. 3
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  3. 3 Dublin City University
    info

    Dublin City University

    Dublín, Irlanda

    ROR https://ror.org/04a1a1e81

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Libro:
Advances in Intelligent Systems and Computing

ISBN: 9783319079943

Año de publicación: 2014

Volumen: 299

Páginas: 279-288

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-319-07995-0_28 SCOPUS: 2-s2.0-84927743115 WoS: WOS:000343754200028 GOOGLE SCHOLAR

Resumen

Currently, processes to characterize materials are mainly based on two methodologies: a good design of experiments and models based on finite element simulations. In this paper, in order to obtain advantages and disadvantages of both techniques, a prediction of mechanical properties of electrolytic tinplate is made from the data obtained in both methodologies. The predictions, and therefore, the comparative analysis are performed using various machine learning techniques: linear regression, artificial neural networks, support vector machines and regression trees. Data from both methodologies are used to develop models that subsequently are tested with their own method data and with data obtained from mechanical tests. The obtained results show that models based on design of experiments are more accurate, but the models based on finite element simulations better define the problem space. © Springer International Publishing Switzerland 2014.