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A computational estimation of cyclic material properties using artificial neural networks

 
: Tomasella, A.; Dsoki, C. el; Hanselka, H.; Kaufmann, H.

:
Postprint (PDF; )

Procedia Engineering 10 (2011), S.439-445
ISSN: 1877-7058
International Conference on the Mechanical Behavior of Materials (ICM) <11, 2011, Como>
Englisch
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer LBF ()

Abstract
The structural durability design of components requires the knowledge of cyclic material properties. These parameters are strongly dependent on environmental conditions and manufacturing processes, and require many experimental tests to be correctly determined. Considering time and costs, it is not possible to include in the tests all the variables that influence the material behaviour. For this reason, the computational method of the Artificial Neural Network (ANN) can be implemented to support these investigations. This method allows an estimation of the cyclic material properties starting from the static parameters deducted through tensile tests. The results permit a very good approximation of cyclic material properties using just a few specimens in tests, so that the experimental effort can be deeply reduced. The ANN has been implemented in the software called Artificial Neural Strain Life Curves (ANSLC), and has been tested on a large database of steels. In this pa per the method of the ANN and the program ANSLC will be presented.

: http://publica.fraunhofer.de/dokumente/N-189205.html