## 5 Conclusions  
This study investigated the use of AE monitoring for impact localization on aircraft elevators, employing two regression machine learning models--linear regression and random forest--to analyze AE signals and predict impact locations. The study demonstrated the efficacy of both models, with the random forest model outperforming the linear regression model in terms of accuracy. The key findings and conclusions drawn are as follows:  
1. AE monitoring has the potential to be an effective tool for detecting and localizing impacts on aircraft elevators, offering a real-time and noninvasive monitoring solution for improved aircraft safety and maintenance.
2. Both linear regression and random forest regression models demonstrated high accuracy in predicting impact locations, proving the effectiveness of regression algorithms in analyzing AE signals for impact localization.
3. The Pearson correlation analysis demonstrated a strong correlation between amplitude and impact location, suggesting that amplitude may be a critical factor in predicting the impact location using linear regression.
4. The random forest model demonstrated better performance compared to the linear regression model, with a higher R\({}^{2}\) value (0.98616) and a lower RMSE (0.6778), indicating its potential for practical applications in the aviation industry.  
The limitation of the current study is the difficulty with access to the labeled AE signals for existing aircraft components. Future studies may address this issue by employing transfer learning or involving simulation AE data in the training of machine learning models. Future research could also explore other machine learning models and feature extraction methods to further enhance the accuracy of impact localization on aircraft elevators using AE monitoring, as well as investigate the scalability of these techniques to other parts of the aircraft. By incorporating additional sensor data, such as strain and temperature measurements, future research could also further improve the accuracy and reliability of the impact localization system, providing a more holistic understanding of structural health.  
Conceptualization, L.A. and P.Z.; methodology, L.A., S.F. and T.M.; software, L.A., S.F. and T.M.; formal analysis, L.A., S.F. and T.M.; data curation, L.A., B.H. and S.H.; writing--original draft preparation, L.A., S.F. and T.M.; writing--review and editing, P.Z.; supervision, L.A. and P.Z.; project administration, P.Z.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.  
This research was conducted at the University of South Carolina and was partially funded through the NASA University Leadership Initiative Cooperative Agreement entitled Innovative  
Figure 9: The comparison of actual impact locations and predicted impact locations.  
Manufacturing, Operation, and Certification of Advanced Structures for Civil Vertical Lift Vehicles led by Georgia Tech, Agreement No. 80 NSSC21M0113. The APC was waived. The NASA technical monitor is [PERSON]. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government.  
Not applicable.  
The raw/processed data required to reproduce these findings will be made available on request.  
The authors declare no conflict of interest.