Issue 40

L. Zou et alii, Frattura ed Integrità Strutturale, 40 (2017) 137-148; DOI: 10.3221/IGF-ESIS.40.12 142 of material types including 5083H11, AlMg4MnCr,AlMgSi1,NP5/6 and HP30, four kinds of plate thicknesses including 10 mm ,2.5 mm ,3 mm and 4. 8mm , three kinds of Ratio including 0, 0.1 and 0.5, two kinds of load types including 4B and T, three types of joint types including TJ:p, LJ_DS:p, and SJ_DS:p. Limited to the space, only part of the experiment data is shown as below in Tab.1. It only includes fatigue data of crack initiation from weld toe, excludes that from weld and base metal. Material type Welding method Thickness ( mm ) Ratio Load type Joint type Nominal stress ( MPa ) Eq.structural stress range ( MPa ) Life Cycles 5083H11 MIG 10 0.1 4B TJ:p 120 161 62700 5083H11 MIG 10 0.5 4B TJ:p 90 121 213750 AlMg4MnCr GMAW 2.5 0.1 T LJ_SS:p 45 174 31260 AlMg4MnCr GMAW 2.5 0.1 T LJ_SS:p 35 135 52040 AlMgSi1 TIG 3 0 T LJ_DS:p 53 160 85920 AlMgSi1 TIG 3 0 T LJ_DS:p 32 97 323460 NP5/6 Manual Arc 4.8 0 T SJ_DS:p 46 116 188000 NP5/6 Manual Arc 4.8 0 T SJ_DS:p 31 77 1250000 HP30 Manual Arc 4.8 0 T SJ_DS:p 62 155 188000 …… Table1 : Part fatigue data of the aluminum alloy welded joints. Fitting of S-N Curves According to the three fatigue stress-life relations mentioned in the three types of fatigue stress-life relations section, S-N curve fitting results using the nodal force based structural stress are obtained in Fig. 3. Comparison of goodness-of-fit statistics including SSE , R-square , Adjusted R-square and RMSE is shown in Tab. 2. Where, sum of squares due to error measures the total deviation of the response values from the fit to the response values. It is also called the summed square of residuals and is usually labeled as SSE . 2 1 ( ) , n i i i i SSE y y       (17) R-square is the square of the correlation between the response values and the predicted response values. It is also called the square of the multiple correlation coefficients and the coefficient of multiple determinations. R-square is defined as the ratio of the sum of squares of the regression ( SSR ) and the total sum of squares ( SST ). SSR is defined as 2 1 ( ) , n i i i SSR y y       (18) SST is also called the sum of squares about the mean, and is defined as 2 1 ( ) , n i i i SST y y      (19) Where, SST = SSR + SSE . Given these definitions, R-square is expressed as R-square = 1 , SSR SSE SST SST   (20) R-square can take on any value between 0 and 1, with a value closer to 1 indicating that a greater proportion of variance is accounted for by the model. The adjusted R-square statistic is generally the best indicator of the fit quality when you compare two models that are nested, that is, a series of models each of which adds additional coefficients to the previous model.