Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate
This paper discusses the application of machine learning techniques, notably artificial neural networks (ANN), in the fracture analysis of semi-infinite elastic plates with edge cracks. The Stress Intensity Factor (SIF) model for a semi-infinite plate with a tip crack is employed in the study, and F...
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2024-04-01
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| Series: | Fracture and Structural Integrity |
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| Online Access: | https://www.fracturae.com/index.php/fis/article/view/4752/3991 |
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| author | Saeed H. Moghtaderi Alias Jedi Ahmad Kamal Ariffin Prakash Thamburaja |
| author_facet | Saeed H. Moghtaderi Alias Jedi Ahmad Kamal Ariffin Prakash Thamburaja |
| author_sort | Saeed H. Moghtaderi |
| collection | DOAJ |
| description | This paper discusses the application of machine learning techniques, notably artificial neural networks (ANN), in the fracture analysis of semi-infinite elastic plates with edge cracks. The Stress Intensity Factor (SIF) model for a semi-infinite plate with a tip crack is employed in the study, and Finite Element Analysis (FEA) is performed via ABAQUS CAE to build a comprehensive dataset containing numerical simulations data. To improve accuracy and reliability, data preprocessing is implemented, and ANN as a valuable machine learning model is trained with various variables describing crack propagation, stress distribution, and plate structure as input parameters. The suggested method is compared to established fracture analysis methods, proving its accuracy in predicting crack behavior and stress distribution under a variety of loading circumstances. The model provides useful insights into the behavior of edge cracks in semi-infinite elastic plates, enhancing material engineering and structural mechanics. The study demonstrates the potential of combining FEA and machine learning to improve fracture analysis capabilities, and it discusses limitations and future research directions, encouraging the exploration of advanced machine learning techniques and broader fracture scenarios for future fracture mechanics innovation |
| format | Article |
| id | doaj-art-f23d9a10ab5e423787342fe79343d6b9 |
| institution | Kabale University |
| issn | 1971-8993 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj-art-f23d9a10ab5e423787342fe79343d6b92025-01-03T01:03:58ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932024-04-01186819720810.3221/IGF-ESIS.68.1310.3221/IGF-ESIS.68.13Application of machine learning in fracture analysis of edge crack semi-infinite elastic plateSaeed H. MoghtaderiAlias JediAhmad Kamal AriffinPrakash ThamburajaThis paper discusses the application of machine learning techniques, notably artificial neural networks (ANN), in the fracture analysis of semi-infinite elastic plates with edge cracks. The Stress Intensity Factor (SIF) model for a semi-infinite plate with a tip crack is employed in the study, and Finite Element Analysis (FEA) is performed via ABAQUS CAE to build a comprehensive dataset containing numerical simulations data. To improve accuracy and reliability, data preprocessing is implemented, and ANN as a valuable machine learning model is trained with various variables describing crack propagation, stress distribution, and plate structure as input parameters. The suggested method is compared to established fracture analysis methods, proving its accuracy in predicting crack behavior and stress distribution under a variety of loading circumstances. The model provides useful insights into the behavior of edge cracks in semi-infinite elastic plates, enhancing material engineering and structural mechanics. The study demonstrates the potential of combining FEA and machine learning to improve fracture analysis capabilities, and it discusses limitations and future research directions, encouraging the exploration of advanced machine learning techniques and broader fracture scenarios for future fracture mechanics innovationhttps://www.fracturae.com/index.php/fis/article/view/4752/3991mode i fracture analysismachine learningfinite element analysiselastic platestress intensity factor |
| spellingShingle | Saeed H. Moghtaderi Alias Jedi Ahmad Kamal Ariffin Prakash Thamburaja Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate Fracture and Structural Integrity mode i fracture analysis machine learning finite element analysis elastic plate stress intensity factor |
| title | Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate |
| title_full | Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate |
| title_fullStr | Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate |
| title_full_unstemmed | Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate |
| title_short | Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate |
| title_sort | application of machine learning in fracture analysis of edge crack semi infinite elastic plate |
| topic | mode i fracture analysis machine learning finite element analysis elastic plate stress intensity factor |
| url | https://www.fracturae.com/index.php/fis/article/view/4752/3991 |
| work_keys_str_mv | AT saeedhmoghtaderi applicationofmachinelearninginfractureanalysisofedgecracksemiinfiniteelasticplate AT aliasjedi applicationofmachinelearninginfractureanalysisofedgecracksemiinfiniteelasticplate AT ahmadkamalariffin applicationofmachinelearninginfractureanalysisofedgecracksemiinfiniteelasticplate AT prakashthamburaja applicationofmachinelearninginfractureanalysisofedgecracksemiinfiniteelasticplate |