In recent years, experimental tests exploring the gigacycle fatigue properties of materials suggest the introduction of modifications in well known statistical fatigue life models. Usual fatigue life models, characterized by failures due to a single failure mode and by the presence of the fatigue limit, have been integrated by models accounting for two failure modes and the possible presence of a fatigue limit. A unified statistical model which can take into account any number of failure modes and the possible presence of a fatigue limit has already been defined. The paper presents a robust method to estimate the parameters involved in the probabilistic model, by applying the Maximum Likelihood Estimation (MLE) method. The robustness of the method, implemented in a MATLAB® environment, is demonstrated by means of several simulated test cases. As an example, the method is applied to experimental data taken from the literature.
STATISTICAL MODEL FOR GIGACYCLE S-N FATIGUE CURVES: PARAMETER ESTIMATION / Paolino, Davide Salvatore; Chiandussi, Giorgio; Rossetto, Massimo; Hobson, L.. - STAMPA. - (2012). (Intervento presentato al convegno International Conference on Fatigue Damage of Structural Materials IX tenutosi a Hyannis, MA, USA nel 16-21 September 2012).
STATISTICAL MODEL FOR GIGACYCLE S-N FATIGUE CURVES: PARAMETER ESTIMATION
PAOLINO, Davide Salvatore;CHIANDUSSI, Giorgio;ROSSETTO, Massimo;
2012
Abstract
In recent years, experimental tests exploring the gigacycle fatigue properties of materials suggest the introduction of modifications in well known statistical fatigue life models. Usual fatigue life models, characterized by failures due to a single failure mode and by the presence of the fatigue limit, have been integrated by models accounting for two failure modes and the possible presence of a fatigue limit. A unified statistical model which can take into account any number of failure modes and the possible presence of a fatigue limit has already been defined. The paper presents a robust method to estimate the parameters involved in the probabilistic model, by applying the Maximum Likelihood Estimation (MLE) method. The robustness of the method, implemented in a MATLAB® environment, is demonstrated by means of several simulated test cases. As an example, the method is applied to experimental data taken from the literature.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2502705
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