Issue 45

L. Zou et alii, Frattura ed Integrità Strutturale, 45 (2018) 53-66; DOI: 10.3221/IGF-ESIS.45.05 63 lg 3.144 0.2018 lg S N  = − (10) S-N curves cluster based on fatigue characteristic domain Neighborhood fatigue decision system is constructed according to the established fatigue database of aluminum alloy welded joints. Feature reduction of this neighborhood fatigue decision system is accomplished by using the forward greedy algorithm. The reduction result we get in the experiment is {Material type, Ratio, Equivalent structural stress range}. Accordingly, fatigue characteristic domain is divided based on the reduction result and we get the 8 fatigue characteristic domains from S 1 to S 8 . In domain S 1 , material type is 5083H11 and ratio is 0.1. In domain S 2 , material type is 5083H11 and ratio is 0.5. In domain S 3 , material type is AlMg4MnCr and ratio is 0.1. In domain S 4 , material type is AlMgSi1(6082) and ratio is 0. In domain S 5 , material type is NP5/6 and ratio is 0. In domain S 6 , material type is HP30 and ratio is 0. In domain S 7 , material type is 5A06+5083 and ratio is 0.1. In domain S 8 , material type is 5A06 and ratio is 0.1. S- N curves are fitted in each fatigue characteristic domain, and the S-N curves cluster we get in the experiment are shown in Fig. 11. Where Mean 7 and Mean 8 corresponds to the 5A06+5083 and the 5A06+5A06 T-joints respectively. Goodness-of- fit statistics of Mean 7 and Mean 8 are shown in Tab. 11. Figure 11 : S-N curve cluster based on the fatigue characteristic domain. Mean 7 Mean 8 SSE 0.0012 0.0029 R-square 0.9202 0.9555 Adjusted R-square 0.9002 0.9406 RMSE 0.0173 0.0311 Table 11 : Goodness-of-fit statistics of Mean 7 -Mean 8 . The S-N curve equation of Mean 7 and Mean 8 are shown as the following (11) and (12). (11) (12) From the process of the determination of the fatigue characteristic domains we could see that neighborhood rough set reduction result is the foundation of determination of fatigue characteristic domains. By using neighborhood rough set theory, we don’t depend on any prior knowledge to achieve the classification of the welded joint fatigue samples. Each lg 2.426 0.0907lg S N  = − lg 3.302 0.2183lg S N  = −