# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 6 Summary and Future Trends  
Damage diagnostics are performed by detecting the origin of the damage, identifying the type of damage, and locating the damage. The damage identification process is characterized by nondeterministic features such as noise, missing data, etc. Many AE characteristics are strongly influenced by the detection system: for example, amplitude; energy; rise time; duration; and counts return values that significantly depend on the distance between the sensor and source, on the type of sensor, on the geometry of the material, etc. These parameters must, therefore, be adequately considered to obtain AE-based monitoring that is reliable and robust. Methodologies based on the exploitation of the AE waveform significantly reduce the effect of the threshold level. The analytical tools associated with the great performances of modern computers allow the processing of large amounts of data (Big Data), returning detailed spectral analyzes, and provide us with groupings of AE parameters that were previously unattainable. This evolution dictated by technological development opens new scenarios in the search for correlations between acoustic events and damage mechanisms. The monitoring of the health of materials and structures through the analysis of AE strongly depends on the quality of the sensors: the technological evolution we are experiencing obviously also affects this type of devices, which are becoming more and more performing. Traditionally, AE technology has exploited piezoelectric sensors, characterized by significant dimensions and with limits of use in harsh environments (high pressures and high temperatures). Furthermore, this type of sensor is subject to corrosion and has been shown to be sensitive to electromagnetic interference. Piezoelectric wafer active sensors (PWASs) represent a low-cost solution that can be inserted within composite materials or between layers of overlapping joints. The resistance to high temperatures of these sensors depends on the PWAS material and on the frequency; in fact, the antiresonance and resonance frequencies have a linear relationship with temperature [106]. Fiber optic sensors are inexpensive and not very sensitive to electromagnetic interference. Furthermore, they show high reliability even in harsh environments with low operating costs, can be easily interrogated at long distances, and offer high spatial coverage [107]. Microelectromechanical systems (MEMS) sensors have proven effective in detecting and controlling on a microscale and generating effects on a macroscale. These are miniaturized sensors used as resonators to amplify the signal-to-noise ratio, with a resonant silicon microstructure and a thin piezoelectric layer, mounted on a ceramic container. Such sensors are characterized by significantly smaller dimensions and weight and by a sensitivity comparable with that of conventional AE sensors [108]. ML-based techniques represent an alternative approach to traditional methodologies for identifying damage through AE. Although it requires significant resources for algorithm training, automatic monitoring returns optimization processes that are shorter than conventional optimization techniques based on manual procedures. These technologies show significant sensitivity to changes in high-frequency transmitted acoustic waves due to the emergence of a defect [109]. The efforts of researchers concentrated on the characterization of the AE signal features to obtain models capable of discriminating between the types of defects: the influence of process parameters and operating conditions on the characteristics of the associated AE signals is evident. The performance of a machine-learning-based model strongly depends on the quality of the data used as input in the training phase.