Issue 45

L. Zou et alii, Frattura ed Integrità Strutturale, 45 (2018) 53-66; DOI: 10.3221/IGF-ESIS.45.05 64 class of welded joint fatigue test samples forms a relatively independent sample space, which is called fatigue characteristic domain. S-N curve cluster could be obtained by fitting the S-N curve in each fatigue characteristic domain. The fatigue life of welded joints is evaluated based on the fitted S-N curve cluster, which can further reduce the dispersion degree of fatigue test samples and improve the prediction accuracy of fatigue life of welded joints. It could be seen from Fig. 11, under semi-log coordination, fatigue test samples of aluminum alloy welded joints with different types of materials, under different stress ratio distributed in a relatively independent space, which is called the fatigue characteristic domain. S-N curve cluster is obtained by fitting the S-N curve in each fatigue characteristic domain, Mean 1 ~Mean 8 . From Tab.10 and Tab. 11, we could find that SSE and RMSE of Mean 7 and Mean 8 are both smaller than that of the Mean, while R-square and Adjusted R-square of Mean 7 and Mean 8 are both closer to 1 than that of the Mean. Smaller SSE and RMSE , bigger R-square and Adjusted R-square s indicate that S-N curves fitted based on the characteristic domain have better performance and higher prediction accuracy. Case study To further verify the effectiveness of the fatigue life prediction method based on the fatigue characteristics domain, under the same experiment conditions, take one 5A06+5A06 T-joint specimen as example. The three-point bending fatigue test of the welded joint is carried out. Fatigue life prediction of the T-joint by using master S-N curve is compared with the prediction value by using S-N curve Mean 8 based on the fatigue characteristic domain and the actual value obtained in the fatigue test. The number of cycles to failure of the specimen is 249363 according to Eqn. (10) and it is 516050 by using Eqn. (12). Comparison result show that fatigue life prediction value of the T-joint by using S-N curve Mean 8 is in better agreement with the experiment results than by using the master S-N curve. Actual fatigue life of the test case is shown in Tab. 12. Material type Welding method Thickness ( mm ) Ratio Loading type Joint type Equivalent structural stress range ( MPa ) Life cycles 5A06+5A06 MIG 16 0.1 3B T 113.48 775900 Table 12 : Fatigue test data of aluminum alloy T-welded joints. D ISCUSSION AND C ONCLUSION hree-point bending fatigue test of aluminum alloy T-welded joint of 5083 and 5A06 is carried out. The finite element model of T-joints is established, and the equivalent structural stress is calculated. The master S-N curve for fatigue design and the S-N curves cluster in different fatigue characteristic domains are fitted according to the experimental fatigue data and the data collected from the literatures. Goodness-of-fit statistics results indicate that the S-N curve cluster has higher prediction accuracy than the master S-N curve. The case study of fatigue life prediction of 5A06+5A06 aluminum alloy T-welded joint specimen show fatigue life prediction by using the S-N curves cluster is in better agreement with the experimental results. Neighborhood rough set theory could find the core factors which influence the fatigue life of the aluminum alloy T- welded joints from the data itself without any prior experience. Fatigue characteristics domain of aluminum alloy T- welded joints could be determined based on reduction results of the neighborhood rough set theory. The result of the case study show that the dispersion level of fatigue samples is further reduced and the fatigue life prediction accuracy is further improved. Future work will be concentrated on the further validation of the fatigue life prediction method based on the fatigue characteristics domain in the practical engineering. A CKNOWLEDGEMENTS he authors would like to thank all the reviewers for their constructive comments. This research was supported by National Science Foundation of Liaoning Province (2015020169) and Liaoning Provincial Education Department Project(JDL2017025). and the Open Project Program of Sichuan Provincial Key Lab of Process Equipment and Control(GK201815). T T