Fatigue crack propagation in Ductile Cast Irons: An Artificial Neural Networks based model

1Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.

Abstract

All the available "Paris-like" models (analytical relationships between da/dN, crack growth rates, and ΔK, stress intensity factor amplitude) are not able to take into account the possible influence of all the parameters that influence the fatigue crack propagation process. Among them, the stress ratio R (e.g., K min /K max ) is one of the most investigated and, although in the last decades the influence of R on the different propagation mechanisms has been widely investigated (e.g., crack closure effect), this parameter is often considered as an independent variable in the "Paris-like" models. A different approach can be followed using the Artificial Neural Networks that are able to consider all the possible parameters, with the condition of a satisfactory training stage. In this work, an artificial Neural Networks based model is optimized considering the influence of the stress ratio on the fatigue crack propagation in a ferritic-pearlitic Ductile Cast Iron.

Cite

CITATION STYLE

APA

D’Agostino, L., De Santis, A., Di Cocco, V., Iacoviello, D., & Iacoviello, F. (2017). Fatigue crack propagation in Ductile Cast Irons: An Artificial Neural Networks based model. In Procedia Structural Integrity (Vol. 3, pp. 291–298). Elsevier B.V. https://doi.org/10.1016/j.prostr.2017.04.048

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free