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A practical method for statistical analysis of strain-life fatigue data
A method is presented that facilitates the development of accurate statistical strain-life curves given experimental data from strain controlled uni-axial fatigue tests. The method establishes a series of selection criteria that ensure that the data used in the statistical analysis are significant and truly representative of material behavior. The method goes on to establish a procedure for the statistical analysis that ensures that each domain of material behavior is accurately represented. The statistical analysis incorporates a method based on the approximate Owen tolerance limit to account for the nature of scatter fatigue data.
A practical method for statistical analysis of strain-life fatigue data
A method is presented that facilitates the development of accurate statistical strain-life curves given experimental data from strain controlled uni-axial fatigue tests. The method establishes a series of selection criteria that ensure that the data used in the statistical analysis are significant and truly representative of material behavior. The method goes on to establish a procedure for the statistical analysis that ensures that each domain of material behavior is accurately represented. The statistical analysis incorporates a method based on the approximate Owen tolerance limit to account for the nature of scatter fatigue data.
A practical method for statistical analysis of strain-life fatigue data
Williams, C.R. (Autor:in) / Lee, Y.L. (Autor:in) / Rilly, J.T. (Autor:in)
International Journal of Fatigue ; 25 ; 427-436
2003
10 Seiten, 11 Quellen
Aufsatz (Zeitschrift)
Englisch
A practical method for statistical analysis of strain-life fatigue data
| British Library Online Contents | 2003
Characterizing statistical strain-life fatigue parameters
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Characterising statistical strain-life fatigue parameters
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A general regression model for statistical analysis of strain–life fatigue data
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Statistical evaluation of strain-life fatigue crack initiation predictions
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