Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms
In the last four decades, bonded composite repair has proven to be an effective method for addressing crack damage propagation. On the other hand, machine learning (ML) has made it possible to employ a variety of approaches for mechanical and aerospace problems and such significant approach is the r...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Gruppo Italiano Frattura
2024-04-01
|
| Series: | Fracture and Structural Integrity |
| Subjects: | |
| Online Access: | https://www.fracturae.com/index.php/fis/article/view/4838/4005 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850055021501087744 |
|---|---|
| author | Abdul Aabid Md Abdul Raheman Meftah Hrairi Muneer Baig |
| author_facet | Abdul Aabid Md Abdul Raheman Meftah Hrairi Muneer Baig |
| author_sort | Abdul Aabid |
| collection | DOAJ |
| description | In the last four decades, bonded composite repair has proven to be an effective method for addressing crack damage propagation. On the other hand, machine learning (ML) has made it possible to employ a variety of approaches for mechanical and aerospace problems and such significant approach is the repair mechanism and hence ML algorithms used to enhance in the present work. The current work investigates the effect of the single-sided composite patch bonded on a thin plate under plane stress conditions. An analytical model was formulated for a single-sided composite patch repair using linear elastic fracture mechanics and Rose's analytical modelling. From the analytical model, the stress intensity factors (SIF) were calculated by varying all possible parameters of the model. Next, ML algorithms were selected, and comparative studies were conducted for the best possible performance and to identify the parametric effects on optimum SIF. Also, the analytical model is validated with existing work, and it shows good agreement with less than 10% error. This study is particularly important for designing the single-sided composite patch repair method based on analytical modelling. Also, it is important to compare ML algorithms with analytical solutions in regression applications |
| format | Article |
| id | doaj-art-fa0bf54211b840dbb24fd4f84d4ee16b |
| institution | DOAJ |
| issn | 1971-8993 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj-art-fa0bf54211b840dbb24fd4f84d4ee16b2025-08-20T02:52:05ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932024-04-01186831032410.3221/IGF-ESIS.68.2110.3221/IGF-ESIS.68.21Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithmsAbdul AabidMd Abdul RahemanMeftah HrairiMuneer BaigIn the last four decades, bonded composite repair has proven to be an effective method for addressing crack damage propagation. On the other hand, machine learning (ML) has made it possible to employ a variety of approaches for mechanical and aerospace problems and such significant approach is the repair mechanism and hence ML algorithms used to enhance in the present work. The current work investigates the effect of the single-sided composite patch bonded on a thin plate under plane stress conditions. An analytical model was formulated for a single-sided composite patch repair using linear elastic fracture mechanics and Rose's analytical modelling. From the analytical model, the stress intensity factors (SIF) were calculated by varying all possible parameters of the model. Next, ML algorithms were selected, and comparative studies were conducted for the best possible performance and to identify the parametric effects on optimum SIF. Also, the analytical model is validated with existing work, and it shows good agreement with less than 10% error. This study is particularly important for designing the single-sided composite patch repair method based on analytical modelling. Also, it is important to compare ML algorithms with analytical solutions in regression applicationshttps://www.fracturae.com/index.php/fis/article/view/4838/4005bonded composite repaircracksreinforced patchanalytical modelmachine learning |
| spellingShingle | Abdul Aabid Md Abdul Raheman Meftah Hrairi Muneer Baig Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms Fracture and Structural Integrity bonded composite repair cracks reinforced patch analytical model machine learning |
| title | Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms |
| title_full | Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms |
| title_fullStr | Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms |
| title_full_unstemmed | Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms |
| title_short | Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms |
| title_sort | improving the performance of damage repair in thin walled structures with analytical data and machine learning algorithms |
| topic | bonded composite repair cracks reinforced patch analytical model machine learning |
| url | https://www.fracturae.com/index.php/fis/article/view/4838/4005 |
| work_keys_str_mv | AT abdulaabid improvingtheperformanceofdamagerepairinthinwalledstructureswithanalyticaldataandmachinelearningalgorithms AT mdabdulraheman improvingtheperformanceofdamagerepairinthinwalledstructureswithanalyticaldataandmachinelearningalgorithms AT meftahhrairi improvingtheperformanceofdamagerepairinthinwalledstructureswithanalyticaldataandmachinelearningalgorithms AT muneerbaig improvingtheperformanceofdamagerepairinthinwalledstructureswithanalyticaldataandmachinelearningalgorithms |