## 2 Materials and Methods
### Image and Signal Processing
This aspect is crucial as, in practical applications, the identification of small cracks is more important than complete ruptures. Additionally, the released energy is proportional to the area of the break. Obviously, the thinner the sample (as in this case), the lower the signal. Despite that, signal was clearly captured proving the concept. This measurement concept was applied to the fatigue tests by placing two microphones as close as possible to the whole sandwich structure (refer to Figure 2), aiming to minimize signal attenuation. The signal processing procedure was as explained in the next points. It must be highlighted that due to the prolonged duration of fatigue tests and the high-frequency acquisition rate of the microphone, acoustic signals are recorded in separate files, each containing 60 s of recording in binary format. 1. The files containing acoustic recording were read and concatenated. 2. Spectrograms were generated to assess the spectral response of the signals. 3. Pre-processing: a high-pass filter was applied to reduce the low-frequency noise not related to the heat exchanger's behavior. A Butterworth high-pass filter was employed with a cutoff frequency of 0.05 (in normalized frequency, equivalent to a 50 kHz cutoff frequency). 4. The root mean square of the signal was computed to determine the energy of the acoustic events. From these values, high-energy events were identified and distinguished from the low ones. 5. A threshold value was established to discard the acoustic events that would not be considered for analysis. This was set at 1000 ticks. 6. The selected event's acoustic signals were retrieved from the original recording. The total length of the signal, 4 milliseconds, was established based on the observation of several acoustic events and in particular those with the longest duration. The 4 ms were divided as follows: 1 ms before the peak value and 3 after. 7. From these individual acoustic signals, 6 main features were extracted which are listed below. These 6 features per acoustic event formed the data set employed for data analysis. 1. Maximum signal amplitude; 2. Rise time (time interval between maximum value and the instant where signals exceed threshold value); 3. Total signal duration; 4. Energy assessed as the root mean square of the signal; 5. Count as the number of zero crossings within the signal segment; 6. Peak frequency of the signal using the Fast Fourier Transform (FFT). 8. The dataset was normalized to apply a Principal Component Analysis (PCA). This revealed the distribution of the data in terms of variability and the principal components that explain this variability. This also eliminated the effects of redundant information. 9. With the dataset, the Isolation Forest algorithm [19; 20] was applied to distinguish acoustic events of different natures. The basic assumption was that most of the event's sources may not stem from cracking events. The suggested strategy considered that events with different behavior, i.e., outliers, were related to cracking events. This was backed by the evidence showing that there were many more events than fins in the structure, suggesting that scratching between loose fins may have been an acoustic source. Given these assumptions, the choice of the Isolation Forest algorithm was straightforward. This machine learning algorithm is particularly effective in identifying outliers in data, because unlike many other anomaly detection methods, Isolation Forest does not rely on any distance or density measure to detect anomalies. Instead, it isolates anomalies by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected features. This process was repeated recursively, and as a result, anomalies were isolated closer to the root of the tree, since they have attribute values that are more distinct than normal points.