Issue 40

L. Zou et alii, Frattura ed Integrità Strutturale, 40 (2017) 137-148; DOI: 10.3221/IGF-ESIS.40.12 144 Figure 4 : Fatigue data scatter based on nominal stress. Figure 5 : S-N curve based on Eq. SS Range. Mean SSE 0.388 R-square 0.772 Adjusted R- square 0.7683 RMSE 0.0791 Table 3 : Goodness-of-fit statistics by using Eq.SS Range. In the Eq. SS method, the structural stress is analyzed by nodal forces approach by considering the welded toe structural stress concentration effect. The stress calculation results are insensitive to the finite element type, mesh shape and dimensions in this method, so the welded toe structural stress concentration conditions for different welded joints could be distinguished effectively. The stress parameter relevant to the fatigue lives of welds directly are defined by using the fracture mechanics and the formula for Eq.SS transformation is determined subsequently. Based on the method of stress calculation and transformation, the fatigue data of aluminum alloy welded joints are analyzed. Then the single fatigue design master S- N curve, which is necessarily important in the fatigue strength assessment and life prediction, is established as in Fig. 5. As could be seen from Fig. 4 and Fig. 5, the dispersion of the fatigue data has been reduced when Eq. structural stress is used compared with using nominal stress. Such problems as how to select S-N curves and to accurately calculate the stress existed in the nominal stress method have been overcome when the nodal force based structural stress method is used. Features Extraction Based on Neighborhood Rough Set Theory Besides the main stress factor, fatigue life of welded joints is also affected by other factors such as the geometry of the welded joints, material types, welding method, load type, Ratio, thickness of the plate et al.. While at present, the analysis of the related factors that influence the fatigue life of the welded joints is generally independent and the correlation between each other is rarely studied. We have tried successfully to establish the mathematical model of the influence of related factors on fatigue life by classical rough set theory [25, 26], where attribute discretization algorithm is used for the continuous attribute. Due to the use of discretization algorithm for continuous attributes inevitably causes the loss of information, in this work, neighborhood rough set theory is used to deal with the continuous attribute for features extraction, according to which fatigue characteristics domain is determined and S-N curve in each domain is fitted. On basis of the fatigue database established as Tab.1, the neighborhood decision table S is built up, which could be expressed as S=(U,C,D,V,f). Where U is the data set of all the aluminum alloy welded joints called the universe, A = C ∪ D is a non- empty finite set of attributes, C is a non-empty finite set of the factors which influence the fatigue life of the aluminum alloy welded joints called condition attributes, and D is the set of the fatigue life called decision attribute. Each attribute a  A can be viewed as a function that maps elements of U into a set V a . The set V a is called the value set of attribute a . In the decision table S , each row describes a solder fatigue life test sample of the aluminum alloy welded joints and each column