Una metodología para el análisis fenomenológico de procesos de daño acumulado. Aplicación a fenómenos de fractura y fatiga

Abstract

Sample functions, i.e., stochastic process realizations are used to define cumulative damage phenomena which end into an observable terminal state or failure. The complexity inherent to such phenomena justifies the use of phenomenological models associated with the evolution of a physical magnitude feasible to be monitored. Sample functions representing the damage evolution may be identified, once normalized to the interval [0,1], with cumulative distribution functions (cdfs), generally, of the generalized extreme value (GEV) family. Though usually, according to the specific problem handled, only a fraction of the whole damage evolution, is available from the test record, the phenomenological models proposed allow the whole damage process to be recovered providing down- and upwards extrapolations of the whole damage process beyond the scope of the experimental program as a fundamental tool for failure prediction in the practical design. The proposed methodology is presented and its utility and generality confirmed by its successive application to representative well-known problems in fracture and fatigue characterization. The excellent fittings, the physical interpretation of the model parameters and the good expectations to achieve a complete probabilistic analysis of these phenomena justify the interest of the proposed phenomenological approach with possible applications to other cumulative damage processes.