0
Research Papers

Metallurgical Phenomena Modeling in Friction Stir Welding of Aluminium Alloys: Analytical Versus Neural Network Based Approaches

[+] Author and Article Information
Livan Fratini

Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Università di Palermo, Viale delle Scienze, 90128 Palermo, Italyabaqus@dtpm.unipa.it

Gianluca Buffa

Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Università di Palermo, Viale delle Scienze, 90128 Palermo, Italy

J. Eng. Mater. Technol 130(3), 031001 (May 22, 2008) (6 pages) doi:10.1115/1.2931142 History: Received January 27, 2006; Revised January 24, 2008; Published May 22, 2008

In this paper, the metallurgical phenomena occurring in friction stir welding processes of AA6082-T6 and AA7075-T6 aluminum alloys are investigated. In particular, to predict the local values of the average grain size, either a simple analytical expression depending on a few material constants or a properly trained neural network is linked to the finite element model of the process. The utilized tools, which take as inputs the local values of strain, strain rate, and temperature, were developed starting from experimental data and numerical results.

FIGURES IN THIS ARTICLE
<>
Copyright © 2008 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 2

The nugget microstructure (AA6082-T6—715(rpm), 200mm∕min)

Grahic Jump Location
Figure 3

The investigated processes

Grahic Jump Location
Figure 4

The grain size measurement loci

Grahic Jump Location
Figure 5

The utilized neural network architecture

Grahic Jump Location
Figure 6

Comparison between the measured and the calculated average grain sizes (AA6082-T6, FSW5—1040rpm, 150mm∕min, y=1.5mm)

Grahic Jump Location
Figure 7

Comparison between the measured and the calculated average grain sizes (AA7075-T6, FSW5—1040rpm, 150mm∕min, y=1.5mm)

Grahic Jump Location
Figure 8

Comparison between the measured and the calculated average grain sizes (AA6082-T6, FSW6—715rpm, 71.5mm∕min, y=0.5mm)

Grahic Jump Location
Figure 9

Comparison between the measured and the calculated average grain sizes (AA7075-T6, FSW6—715rpm, 71.5mm∕min, y=0.5mm)

Grahic Jump Location
Figure 10

Quadratic error (Err) for the analytical model and the neural network, AA6082-T6

Grahic Jump Location
Figure 11

Quadratic error (Err) for the analytical model and the neural network, AA7075-T6

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In