Application of Artificial Neural Network to Forecast the Tensile Fatigue Life of Carbon Material

Abstract:

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Artificial neural network (ANN) is widely applied to the modeling of complex systems, which has become a common modeling method in the study of materials science. As the ideal candidates for high temperature structural materials, carbon materials are no doubt involved in fatigue loads, so the study on forecasting fatigue life is meaningful. In this paper, the electrical resistance at various fatigue cycles and level of applied stress of the materials under tensile fatigue loading has been detected, and regarded the fracture or fatigue cycles equal to 106 as fatigue life of carbon materials. On the basis of the electrical resistance value, the fatigue life has been forecasted by applied the ANN. The results indicated that the ANN could forecast the fatigue life of carbon materials well; finally, the applications of ANN in the study of material, such as properties prediction, damage prediction and failure detection were reviewed.

Info:

Periodical:

Key Engineering Materials (Volumes 385-387)

Edited by:

H.S. Lee, I.S. Yoon and M.H. Aliabadi

Pages:

533-536

DOI:

10.4028/www.scientific.net/KEM.385-387.533

Citation:

X. L. Liao et al., "Application of Artificial Neural Network to Forecast the Tensile Fatigue Life of Carbon Material", Key Engineering Materials, Vols. 385-387, pp. 533-536, 2008

Online since:

July 2008

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Price:

$35.00

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