# Monitoring of Atmospheric Corrosion of Aircraft Alloy AA2024 by Acoustic Emission Measurements
## 3 Results and Discussion
### Comparison of AE, Bubble Activity, and Atmospheric Corrosion
However, a closer look at the corresponding videos revealed that tiny bubble activity already started again at hour 27.5. Therefore, the dry phase at the end of the experiment was also analyzed. Figure 6c shows the dry specimen surface at hour 77.7 at an RH of 27% and Figure 6d shows the surface at the end (18.1 h later) of the experiment, i.e., the same frame shown in Figure 5e. No differences were found between Figure 6c,d. Moreover, no bubble activity was observed in the videos recorded during the corresponding time period. Thus, it is concluded that corrosion did not progress during the two dry phases of the experiment. Figure 5: Relative humidity (RH) and temperature \(T\) during the atmospheric corrosion experiment, and exemplary images (**a–e**) of the deposited droplet and the corroded surface. The areas marked in blue represent the night times from 8 pm to 7 am. Figure 6: No progress in the corrosion during the defined dry phase, i.e., no difference can be seen between the corroded specimen surfaces (**a**) at hour 23.5 and (**b**) at hour 27.5. There was also no progress in the corrosion after the final evaporation of the droplet. (**c**) The specimen surface at hour 77.7 and (**d**) at hour 95.8. To address the initial question of whether atmospheric corrosion can be measured by the AE and further, how it is related to the RH, the measured AE, video, and RH data were processed and analyzed to determine if correlations existed. Commonly, AE signals are analyzed by features such as the AE hit count, hit energy, peak amplitude, root mean square voltage, etc. [32]. However, for this fundamental investigation of whether atmospheric corrosion can be detected by the AE, the cumulative AE hit energy was found to be sufficient (see results below). An amplitude threshold of 28 dB with a reference voltage \(V_{\text{ref}}=1\,\mathrm{\SIUnitSymbolMicro V}\)[32] was used for the extraction of single hits from the continuous AE data stream. The threshold was chosen to be 3 dB above the noise level (proved to suppress small noise outliers), which was approximately constant at 25 dB for the entire AE data. Further, for all the detected hits, a 5 ms time signal was used for further analysis, i.e., a predefined hit duration was applied. The 5 ms duration was enough to fully record most AE events that triggered a hit. Although the experiment was conducted under laboratory conditions, electrical interference could not be completely avoided. However, such interference typically generated spike-like AE signals with high amplitudes but only very few (often just single) counts (number of positive threshold crossings) [32; 41]. These interferences were filtered out by not considering AE hits with fewer than five counts. For the monitoring of the corrosion propagation, the videos were visually inspected (results are presented in Figures 5 and 6). It was observed that the corrosion of aluminum and its constant production of H\({}_{2}\) also formed bubbles when there was only a small amount of electrolyte present on the aluminum surface. These bubbles are reported to be one of the major sources of AE in immersion-like tests [26; 27; 28]; thus, the observed small H\({}_{2}\) bubbles were believed to also be a major AE source in the present atmospheric corrosion experiment. To investigate this question, the recorded videos were processed using an algorithm implemented in Python 3.8 that automatically detects sudden changes such as moving, merging, or bursting bubbles. In the first step, background subtraction using the OpenCV package was conducted to highlight sudden changes in the videos. In the second step, event detection by evaluating the size of these changes using the imutils and OpenCV Python packages was performed. In cases when the detected events exceeded a certain threshold size, the events were considered to be relevant. For comparison with the AE data processing, a cumulative count of the detected events over time was generated. Figure 7 shows some exemplary results of a processed video recorded in step 1.