# Multiscale Damage Evolution Analysis of Aluminum Alloy Based on Defect Visualization
## 3 Acquisition and Analysis of Defect Characteristics Information
### Extraction and Analysis of Defect Characteristic Information  
The characteristic information of defect (including void defect position, volume size, etc.) was obtained through the label analysis module for each cycle stage 3D model. Table 2 shows characteristic statistics of damage defects in different cycles.  
Figure 8: The 3D visualization of internal void defects for specimen labeled 8-3 from different perspectives.  
Figure 7: TIF diagram display. (**a**) TIF diagram after positioning display processing, (**b**) TIF diagram after noise filtering, (**c**) TIF diagram after threshold segmentation processing.  
The variation of the defect characterization with the different loading stages is illustrated in Figure 9. Figure 9a shows that the number of voids increased first to the peak and then decreased rapidly. Figure 9b-d is plotted for the variation of the maximum void, maximum damage surface, and porosity with the number of cycles. It could be seen that the rate of damage growth was not obvious at the beginning loading stage; however, at the later stage of loading, the damage growth rate increased sharply with the growth and association of defects.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Load Sequence** & **Stage** & **2** & **4** & **6** & **8** & **10** \\ \hline \multirow{2}{*}{Total number of voids (n)} & Group1 & 35,106 & 215,398 & 125,703 & 35,684 & 20,982 \\  & Group2 & 36,061 & 233,821 & 104,895 & 30,569 & 16,456 \\  & Group3 & 34,854 & 190,636 & 76,695 & 24,534 & 18,672 \\ \hline \multirow{2}{*}{The volume of maximum void (\(\upmu\)m\({}^{3}\))} & Group1 & 972 & 1810 & 1970 & 3210 & 5671 \\  & Group2 & 1190 & 2161 & 2275 & 3841 & 5319 \\  & Group3 & 1021 & 2013 & 2144 & 3511 & 4782 \\ \hline \multirow{2}{*}{Maximum damage surface area (\(\upmu\)m\({}^{2}\))} & Group1 & 0.0022 & 0.0392 & 0.0345 & 0.0596 & 0.24 \\  & Group2 & 0.0151 & 0.0275 & 0.0226 & 0.0459 & 0.197 \\  & Group3 & 0.0073 & 0.0157 & 0.0553 & 0.0412 & 0.211 \\ \hline \multirow{2}{*}{Porosity (\%)} & Group1 & 0.0004 & 0.0046 & 0.0031 & 0.0287 & 0.2421 \\  & Group2 & 0.0009 & 0.0025 & 0.0673 & 0.0562 & 0.3866 \\  & Group3 & 0.0006 & 0.0051 & 0.0242 & 0.0901 & 0.5293 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic statistics of damage defects in a different cycle.  
Figure 9: Variation of the voids damage characterization with the number of cycles N. (**a**) Variation of the total number of voids with the number of cycles N. (**b**) Variation of the volume of maximum void with the number of cycles N. (**c**) Variation of the maximum damage surface area with the number of cycles N. (**d**) Variation of the Porosity with the number of cycles N.  
According to the variation of these damage characterizations (the number of hole defects, maximum hole, maximum damage surface, and porosity) with respect to the number of cycles, it could be concluded that the damage behavior mainly manifested that the initiation of void defects led to a slower growth of damage in the early loading stage. However, the growth rate of damage increased rapidly in the subsequent loading stages with the growth of micro-voids and the connection between adjacent voids.  
The fatigue damage evolution process could be effectively characterized by the above damage parameters. Although some damage characterizations had volatility in the individual cycle stages, the overall tendency was consistent with the damage evolution law.