## 4 Conclusions  
The main purpose of this paper was twofold. First, to quantitatively evaluate the performance of LST for the detection of porosity and BVID in carbon fiber composites. Robotic LST systems have the advantage of being more adapted for online inspection  
Figure 14: Impacted samples: LST at 10 mm/s: (**a**) close-up of impact damage on sample S5 rotated 90\({}^{\circ}\) counterclockwise with respect to the rectangular area in the image at the right; and (**b**) reconstructed thermogram showing all four impacted samples approximately 0.5 s after heating. Figure 13: Specim S5. Determination of the depth at different regions of impacted specimen S5: (**a**) phase profiles of the sound area (blue) and the ROIs (red) showing the blind frequencies corresponding to the ROIs depths; phasegrams at \(f\) = (**b**) 0.02; (**c**) 0.08; (**d**) 0.1875; and (**e**) 0.5 Hz, showing selected ROIs and their estimated depths. during the production of flat and complex-shaped aerospace components. This is currently of great interest in the aerospace industry in order to reduce inspection times without compromising defect detection effectiveness. Second, to probe that porosity and impact damage can be quantified using IRT methods. For this second goal, a PT approach was selected since a defect characterization procedure exists using advanced signal processing, i.e., pulsed phase thermography (PPT). In addition, the thermal diffusivity of the material was measured as a first step toward the determination of depth by PPT. The performance of LST with an uncooled microbolometer camera was evaluated with respect to PT with a high-definition camera, which can be considered the gold standard in IRT. A set of academic plates containing artificial porosity and BVID was inspected using both approaches. The CNR was used as a quantitative parameter to objectively evaluate defect contrast. It was calculated frame by frame for all defects and all processing techniques, so the maximum values \(\text{CNR}_{\text{max}}\) were found for each approach. Table 2 summarizes the measured \(\text{CNR}_{\text{max}}\). Globally, CNR values were greater for PT than for LST, as expected given the differences in camera spatial resolution and sensitivity between setups, and also because a static configuration allows recording of thermal cooling longer than a dynamic configuration, which further improves defect contrast. Nevertheless, there were some situations where LST performed better than PT. Notably, defect 3 in sample S45, which is a single-layer artificial porosity area at \(z=3.115\) mm (Figure 10), was only detected by LST (at 25 mm/s) and not by PT. This is because with LST the sample was heated longer than with the flashes. Hence, the energy delivered to the sample was sufficient to reach this defect and produce enough defect contrast. The use of a longer pulse, using halogen lamps, for example, would most probably improve results for deeper defects when using a static configuration. The application of advanced signal processing significantly improved CNR in all cases but more significantly in PT. Spatial filters also improved CNR values but to a lesser extent. The combined effect of advanced processing and Gaussian filtering can be clearly appreciated for instance in Figure 9, which presents the case of multilayer porosity at the middle thickness of the sample that shows an improvement of \(\text{CNR}_{\text{max}}\) from 0.4 (not detected) to 7.7 (clearly detected) after processing and filtering. Further research should be carried out to investigate the limiting variance values of Gaussian filtering considering that any spatial filter modifies images to some extent. This research study has clearly demonstrated that LST (with a microbolometer camera) can be used for the detection of porosity and BVID in composite materials with performance comparable to PT (with a high-definition cooled camera), which could potentially improve inspection times in the aerospace manufacturing industry.