Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary Study
The reliability of aircraft control surfaces, constructed from thermoplastic materials, can be affected by impacts from airborne particles. Recognizing the exact position of such impacts is essential for correctly estimating the resulting damage. This research intended to address the issue by introd...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/11/6573 |
| _version_ | 1827739752765325312 |
|---|---|
| author | Li Ai Sydney Flowers Tanner Mesaric Bryson Henderson Sydney Houck Paul Ziehl |
| author_facet | Li Ai Sydney Flowers Tanner Mesaric Bryson Henderson Sydney Houck Paul Ziehl |
| author_sort | Li Ai |
| collection | DOAJ |
| description | The reliability of aircraft control surfaces, constructed from thermoplastic materials, can be affected by impacts from airborne particles. Recognizing the exact position of such impacts is essential for correctly estimating the resulting damage. This research intended to address the issue by introducing an innovative structural health monitoring solution capable of autonomously detecting and localizing impacts using acoustic emission monitoring. The objective of this research is to investigate the application of AE for the localization of impacts on aircraft elevators using machine learning techniques, specifically regression algorithms. To achieve this goal, two algorithms, linear regression, and random forest, were employed for predicting the impact locations based on AE signals. The performance of each algorithm was validated on a thermoplastic composite aircraft elevator. Results indicated that both linear regression and random forest models show high accuracy in predicting the impact locations. The random forest model, with an R<sup>2</sup> value of 0.98616 and an RMSE of 0.6778, outperformed the linear regression model, which exhibited an R<sup>2</sup> value of 0.9361 and an RMSE of 1.4614. |
| first_indexed | 2024-03-11T03:11:33Z |
| format | Article |
| id | doaj.art-7f05be57b4c44fbba5c571dafa683992 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:11:33Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7f05be57b4c44fbba5c571dafa6839922023-11-18T07:33:59ZengMDPI AGApplied Sciences2076-34172023-05-011311657310.3390/app13116573Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary StudyLi Ai0Sydney Flowers1Tanner Mesaric2Bryson Henderson3Sydney Houck4Paul Ziehl5Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29201, USADepartment of Integrated Information Technology, University of South Carolina, Columbia, SC 29201, USADepartment of Integrated Information Technology, University of South Carolina, Columbia, SC 29201, USADepartment of Mechanical Engineering, University of South Carolina, Columbia, SC 29201, USADepartment of Mechanical Engineering, University of South Carolina, Columbia, SC 29201, USADepartment of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29201, USAThe reliability of aircraft control surfaces, constructed from thermoplastic materials, can be affected by impacts from airborne particles. Recognizing the exact position of such impacts is essential for correctly estimating the resulting damage. This research intended to address the issue by introducing an innovative structural health monitoring solution capable of autonomously detecting and localizing impacts using acoustic emission monitoring. The objective of this research is to investigate the application of AE for the localization of impacts on aircraft elevators using machine learning techniques, specifically regression algorithms. To achieve this goal, two algorithms, linear regression, and random forest, were employed for predicting the impact locations based on AE signals. The performance of each algorithm was validated on a thermoplastic composite aircraft elevator. Results indicated that both linear regression and random forest models show high accuracy in predicting the impact locations. The random forest model, with an R<sup>2</sup> value of 0.98616 and an RMSE of 0.6778, outperformed the linear regression model, which exhibited an R<sup>2</sup> value of 0.9361 and an RMSE of 1.4614.https://www.mdpi.com/2076-3417/13/11/6573thermoplastic compositeimpactsacoustic emissionstructural health monitoring |
| spellingShingle | Li Ai Sydney Flowers Tanner Mesaric Bryson Henderson Sydney Houck Paul Ziehl Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary Study Applied Sciences thermoplastic composite impacts acoustic emission structural health monitoring |
| title | Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary Study |
| title_full | Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary Study |
| title_fullStr | Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary Study |
| title_full_unstemmed | Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary Study |
| title_short | Acoustic Emission-Based Detection of Impacts on Thermoplastic Aircraft Control Surfaces: A Preliminary Study |
| title_sort | acoustic emission based detection of impacts on thermoplastic aircraft control surfaces a preliminary study |
| topic | thermoplastic composite impacts acoustic emission structural health monitoring |
| url | https://www.mdpi.com/2076-3417/13/11/6573 |
| work_keys_str_mv | AT liai acousticemissionbaseddetectionofimpactsonthermoplasticaircraftcontrolsurfacesapreliminarystudy AT sydneyflowers acousticemissionbaseddetectionofimpactsonthermoplasticaircraftcontrolsurfacesapreliminarystudy AT tannermesaric acousticemissionbaseddetectionofimpactsonthermoplasticaircraftcontrolsurfacesapreliminarystudy AT brysonhenderson acousticemissionbaseddetectionofimpactsonthermoplasticaircraftcontrolsurfacesapreliminarystudy AT sydneyhouck acousticemissionbaseddetectionofimpactsonthermoplasticaircraftcontrolsurfacesapreliminarystudy AT paulziehl acousticemissionbaseddetectionofimpactsonthermoplasticaircraftcontrolsurfacesapreliminarystudy |