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

  • Roberto Fernández-Martínez
  • Rubén Lostado-Lorza
  • Marcos Illera-Cueva
  • Rubén Escribano-García
  • Bryan J. Mac Donald
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)


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.


Design of Experiments Electrolytic tinplate materials Finite Element Machine learning 


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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roberto Fernández-Martínez
    • 1
  • Rubén Lostado-Lorza
    • 2
  • Marcos Illera-Cueva
    • 2
  • Rubén Escribano-García
    • 2
  • Bryan J. Mac Donald
    • 3
  1. 1.Department of Electrical EngineeringUniversity of Basque Country UPV/EHUBilbaoSpain
  2. 2.Department of Mechanical EngineeringUniversity of La RiojaLogroñoSpain
  3. 3.School of Mechanical and Manufacturing EngineeringDublin City UniversityDublinIreland

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