## 4 Results and Discussion  
The first investigation presented here is of the PCTG part A, with the orientation state defined in Table 1. Using the approach outlined in the previous section for the automated raster orientation extraction, the extracted orientation state is presented for the first 10 of the 18 layers in Table 3. Two different scan results are exhibited, the first with a 10 MHz spherically focused transducer and the second with a 15 MHz spherically focused transducer, both with a nominal 38 mm focal length. Observe, in comparison with Table 1, that the orientation for each layer is consistent with a typical error of 0 degrees or 1 degree for each of the captured layers. Analysis after the 10 th layer was not performed as the reflection wave signal-to-noise ratio was insufficient for analysis, causing significant  
Figure 3: PCTG sample at the third layer of part A: (**a**) representative C-scan with (**b**) the associated 2D FFT spectrum with filtering. errors in the analysis due to signal attenuation and scatter from the previous layers. It is worth noting that the scan with the 10 MHz transducer yields the 10 th layer, whereas the results from the 15 MHz transducer could not penetrate the 10 th layer with an acceptable signal-to-noise ratio. Several numbers in Table 3 have an asterisk (*). This is used to indicate where a layer was ignored, as discussed in the methodology section, due to peaks being too close in time due to the mode shift caused near the front wall. Based on the results from Table 3, along with other in-house studies, the 10 MHz transducer was selected for all the remaining analyses. The scanning and analysis process was repeated for all three sample types, A, B, and C from Table 1, for each of the three material systems using the 10 MHz transducer for scanning. To assess the accuracy of the automated raster orientation algorithm from Section 3, the error of the nth layer is defined as  
\[Error_{n}=\left|\theta_{designed,n}-\theta_{UT\ Observed,n}\right| \tag{7}\]  
where the error will be in units of degrees. A relative error was not used in the current study as the designed orientation ranges from 0 degrees to 180 degrees. The error is plotted in Figure 4 for the type A parts of each of the three material types. Notice that for the first 9 or 10 layers, the error was 0, 1, or 2 degrees for the unfilled polymers as well as the carbon fiber-filled polymer. For the unfilled PCTG polymer and the fiber-filled PET polymer after the 10 th layer the orientation state cannot be identified due to a poor signal-to-noise ratio. Conversely, for the engineered polymer HTN, the orientation state can be identified for 17 of the 18 layers in sample type A. Notice that the error in all cases is 2 degrees or less, with the majority of the automated layer orientation extraction yielding an error of 1 degree or 0 degrees. The process was continued for both part types B and C, and the results are shown in Figure 5. As with part type A, the error was 0, 1, or 2 degrees. Similar to part type A, layers up to the 9 th or 10 th layer can be identified for all three material systems, but more layers can be identified for the HTN samples. Unlike part type A, the algorithm failed to identify the second layer in the HTN and PCTG samples for both the type B and C parts. Otherwise, the algorithm was successful in identifying the remaining layers up to the 9 th layer for the PCTG and the 14 th layer for the HTN. Unlike the type A part with HTN, the analysis of the HTN type B and C samples was successful to only the 14 th layer. It was observed that with some adjustments of the various parameters, such as \(\delta\) in Equation (5) or the relative size of \(\Delta t_{n}\) in Equation (4), various layers could be identified. The optimal setting of these parameters has been left for a future study.