Using a neural network for predicting the average grain size in friction stir welding processes

  • Authors:

  • Livan Fratini;Gianluca Buffa;Dina Palmeri

  • Affiliations:

  • Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Universití di Palermo, Viale delle Scienze, 90128 Palermo, Italy;Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Universití di Palermo, Viale delle Scienze, 90128 Palermo, Italy;Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Universití di Palermo, Viale delle Scienze, 90128 Palermo, Italy

  • Venue:
  • Computers and Structures
  • Year:
  • 2009

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Abstract

In the paper the microstructural phenomena in terms of average grain size occurring in friction stir welding (FSW) processes are focused. A neural network was linked to a finite element model (FEM) of the process to predict the average grain size values. The utilized net was trained starting from experimental data and numerical results of butt joints and then tested on further butt, lap and T-joints. The obtained results show the capability of the AI technique in conjunction with the FE tool to predict the final microstructure in the FSW joints.