Issue 64

Y. Li et alii, Frattura ed Integrità Strutturale, 64 (2023) 250-265; DOI: 10.3221/IGF-ESIS.64.17

(

)

+ = + k

1 k x x

− + k β x x αε k

(9)

i

i

ij

j

i

i

where, k is the iteration times of the algorithm, α is the step, ε i is random number, which subject to uniform distribution or gaussian distribution.

C ONSTRUCTION OF S-N CURVE FITTING OPTIMIZATION MODEL ompare with the other methods, the mesh-insensitive structural stress approach benefits from accurate calculation outcomes. However, the scattering of fatigue test specimen data expressed by this method is still relatively high, which results in poor precision in predicting fatigue life. It is very important to decrease the dispersity of the fatigue test specimen data and to increase the accuracy of fatigue life prediction. Processes and steps of the model To improve the accuracy of fatigue life prediction obtained from the test, we propose an S-N fitting optimization method. Firstly, the fatigue decision system is built using the welded joint fatigue data. Then through the analysis, using IFANRSR algorithm, the evaluation model of key influencing factors is established. Next, the key factors set of obtained by the IFANRSR algorithm are taken as the basis of fatigue characteristics domain division, so that some fatigue data have similar features are dispersed in an area that is largely independent. In this way, the fatigue characteristics domain is obtained and the curve is fitted, and S-N curve fitting based on the fatigue characteristics domain is realized. An S-N fitting optimization method based on the fatigue characteristics domain is clearly described in FIG. 2: C

Figure 2: optimization of S-N curve fitting.

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