Combining soft computing techniques and the finite element method to design and optimize complex welded products

  1. Lostado, R. 2
  2. Martinez, R.F. 1
  3. Mac Donald, B.J. 4
  4. Villanueva, P.M. 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 Universidad Pública de Navarra
    info

    Universidad Pública de Navarra

    Pamplona, España

    ROR https://ror.org/02z0cah89

  4. 4 Dublin City University
    info

    Dublin City University

    Dublín, Irlanda

    ROR https://ror.org/04a1a1e81

Revista:
Integrated Computer-Aided Engineering

ISSN: 1069-2509

Año de publicación: 2015

Volumen: 22

Número: 2

Páginas: 153-170

Tipo: Artículo

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DOI: 10.3233/ICA-150484 SCOPUS: 2-s2.0-84925743346 WoS: WOS:000351737700004 GOOGLE SCHOLAR

Otras publicaciones en: Integrated Computer-Aided Engineering

Resumen

One of the main objectives when designing welded products is to reduce strains and deformations. Strains can cause excessive angular distortion. This results in a welded product that does not meet acceptable tolerances. The geometry of the weld bead (height and width) depends on the input parameters (speed, voltage and current), and provides the welded joint with strength and quality. As welded products become increasingly complex, deformations become more difficult to predict as they depend greatly on the welding sequence. This paper shows how a combination of the Finite Element Method, Genetic Algorithms and Regression Trees may be used to design and optimize complex welded products. Initially, Artificial Neural Networks and Regression Trees that are based on heuristic methods and evolutionary algorithms were used in predicting the weld bead geometry according to the input parameters. Then, thermo-mechanical Finite Element models were created to obtain the temperature field and the angular distortion using the weld bead geometry that the best predictive models generated. Finally, optimization techniques that are based on Genetic Algorithms were used to validate these Finite Element models against experimental results, and to subsequently find the optimal welding sequence to use in the manufacture of complex welded products. © 2015 - IOS Press and the author(s). All rights reserved.