An elastic mixed-modes I and II fracture criterion using an artificial neural network database

Zarrabi, K. and Tsang, K. H. (2007). An elastic mixed-modes I and II fracture criterion using an artificial neural network database. In: Martin Veidt, Faris Albermani, Bill Daniel, John Griffiths, Doug Hargreaves, Ross McAree, Paul Meehan and Andy Tan, Proceedings of the 5th Australasian Congress on Applied Mechanics (ACAM 2007). 5th Australasian Congress on Applied Mechanics (ACAM 2007), Brisbane, Australia, (139-145). 10-12 December, 2007.

 
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Author(s) Zarrabi, K.
Tsang, K. H.
Title of paper An elastic mixed-modes I and II fracture criterion using an artificial neural network database
Conference name 5th Australasian Congress on Applied Mechanics (ACAM 2007)
Conference location Brisbane, Australia
Conference dates 10-12 December, 2007
Proceedings title Proceedings of the 5th Australasian Congress on Applied Mechanics (ACAM 2007)
Editor(s) Martin Veidt
Faris Albermani
Bill Daniel
John Griffiths
Doug Hargreaves
Ross McAree
Paul Meehan
Andy Tan
Place published Brisbane
Publisher Engineers Australia
Publication date 2007
Year available 2008
Volume number 1
ISBN 0 8582 5862 5
Start page 139
End page 145
Total pages 7
Collection year 2007
Language eng
Abstract/Summary Many thin structural elements such as various parts of fuselage, tubular supports in oil/gas off-shore platforms, etc may fail under the mixed-modes I and II fracture. Although there are a number of criteria for the mixed-modes I and II fracture, none of them are universally accepted and applied to practical problems. This is mainly due to lack of knowledge about the fracture toughness under various mode-mixity and rather complexity of the pertinent relationships. This paper proposes a new and relatively simple criterion for the mixed-modes I and II fracture that uses an artificial neural network database to obtain the pertinent fracture toughness. The application of the criterion is demonstrated by applying it to an example.
Subjects 290501 Mechanical Engineering
Keyword(s) artificial neural network
mixed-modes I-II fracture
 
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