PSI - Issue 33

Mohammad Azadi et al. / Procedia Structural Integrity 33 (2021) 181–188 Azadi & Aroo / Structural Integrity Procedia 00 (2021) 000 – 000

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Fig. 5. The curve of the stress versus the fatigue lifetime for the heat-treated nano-composite.

The main objective of this article was the sensitivity analysis of inputs on outputs. The obtained results from the regression analysis on experimental data could be found in Tables 2 and 3, respectively for normal and logarithmic scales. For this order, the coefficient of determination (R 2 ) was calculated as 42.78 and 86.98%, respectively. This means that the logarithmic scale of the fatigue lifetime had a meaningful trend, compared to the normal scale. It should be noted that the P-value of the regression analysis was less than 0.05 and therefore, the sensitivity analysis was meaningful for both scales. Based on the P-value in the normal scale of the fatigue lifetime, the stress and the pre-corrosion were effective parameters, since they had the P-value lower than 0.05. However, the effect of both reinforcements was not significant on the fatigue lifetime of studied materials. Considering the F-value, the effect of the stress was higher than the pre corrosion influence of the fatigue performance of the material. Besides, according to the P-value in the normal scale of the fatigue lifetime, all input parameters were effective on the fatigue performance of the material. The F-value indicated that the most effective parameter was the stress, the pre-corrosion, the heat treatment and lastly, nano particles. For the graphical result, these sensitivities are drawn in Figs. 6 and 7, based on the pareto diagram.

Table 2. The sensitivity analysis for the fatigue lifetime. Parameter

F-Value P-Value

Regression

12.71 37.34 11.32

0.000 0.000 0.001 0.184 0.158

Stress

Pre-corrosion

Nano-particles reinforcement 1.81 Heat treatment reinforcement 2.04

Table 3. The sensitivity analysis for the logarithmic fatigue lifetime. Parameter

F-Value P-Value

Regression

113.07 216.82

0.000 0.000 0.000 0.000 0.000

Stress

Pre-corrosion

72.76

Nano-particles reinforcement 22.39 Heat treatment reinforcement 62.61