# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 5 Actual Damage Detection and Quantification of a Twin-Screw Compressor Body
### Damage Visualization and Quantification  
In this section, we discuss the detection, visualization and quantification of the damage on the connection transverse plane of a twin-screw compressor body using the experimental setup presented in Section 3. In this damage detection process, two scanning modes with different damage detection accuracies were used in the experiment to identify the damage with different dimensions. In mode one, the scanning area of the laser pulse was set to 10 \(\times\) 10 mm\({}^{2}\), and the distance between the two adjacent points was 0.2 mm, yielding 2500 scanning points. The damage was quickly located in this wide range scanning mode. The scanning area of the laser pulse was set to 5 \(\times\) 5 mm\({}^{2}\) in mode two. The distance between the two adjacent points was 0.1 mm, yielding 2500 scanning points. More information on the tiny damage was acquired by using this scanning mode. A schematic of the second scanning mode was shown in Figure 13a. In this experiment, the baseline data was acquired from the same connection transverse plane on the same twin-screw compressor body. The only difference is that the scanned area used for acquiring the baseline data was examined carefully with the digital microscope to make sure there is no obvious defect in the area. In this way, this area can be treated as the'reference undamaged structure' in the experiment. The embedding parameters should be chosen before computing the NNPE values. The embedding parameters of ten randomly selected ultrasonic responses recorded by a transducer placed at a fixed point were calculated by the AMI and [PERSON]'s function. The calculation results are provided in Figures 14 and 15, respectively. The results calculated by [PERSON]'s function for the response, whose minimum embedding dimension was the largest compared with those of the others, are shown in Figure 15b. From Figures 14 and 15, when the time delay \(T\) reached 4, the calculation results for all the responses by using the AMI function were close to the minimum value, and the values of E\({}_{1}\) and E\({}_{2}\) did not change after \(m\) exceeded 23. Hence, \(T\) = 4 and \(m\) = 24 were selected in this experiment. Similarly, the time delay \(T\) and embedding dimension \(m\) in scanning mode two were calculated to be 3 and 19, respectively. Next, all responses with the same embedding parameters were used to reconstruct the attractors. Based on the attractors reconstructed from both the undamaged and damaged sections, the 2500 NNPE values were computed and are summarized in Figure 16. Here, the detection result by linear feature root mean square was also calculated as shown in Figure 17 in addition to the NNPE values. In Figure 16a, the values of the feature NNPE around the sand inclusion are the largest compared with others in the scanning area. Thus, the sand inclusion can be detected and visualized clearly. Furthermore, the shape and direction of the resulting fatigue crack can be vaguely distinguished from the picture too. But, the picture of the fatigue crack is almost submerged in the environmental noise due to the low detection accuracy. In Figure 16b, the shape and direction information of the fatigue crack can be identified clearly, which means that in scanning mode two the damage detection accuracy has been greatly improved as a result of changes in the visualization  
Figure 13: The images of the damage in two different scanning modes: (**a**) a global view of the damage; (**b**) enlarged local fatigue crack. parameters. In Figure 17, the sand inclusion has been detected and visualized successfully. However, the detection of the resulting fatigue crack has failed since there is no obvious information about the fatigue crack in Figure 17. This detection result is not surprised since the nonlinear ultrasonic features are usually more sensitive to small cracks than the linear ones as mentioned above.