Review of machine learning applications for defect detection in composite materials
Machine learning (ML) techniques have shown promising applications in a broad range of topics in engineering, composite materials behavior analysis, and manufacturing. This paper reviews successful ML implementations for defect and damage identification and progression in composites. The focus is on...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827024000768 |
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| _version_ | 1850061138268520448 |
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| author | Vahid Daghigh Hamid Daghigh Thomas E. Lacy, Jr. Mohammad Naraghi |
| author_facet | Vahid Daghigh Hamid Daghigh Thomas E. Lacy, Jr. Mohammad Naraghi |
| author_sort | Vahid Daghigh |
| collection | DOAJ |
| description | Machine learning (ML) techniques have shown promising applications in a broad range of topics in engineering, composite materials behavior analysis, and manufacturing. This paper reviews successful ML implementations for defect and damage identification and progression in composites. The focus is on predicting composites' responses under specific loads and environments and optimizing setting and imperfection sensitivity. Discussions and recommendations toward promising ML implementation practices for fruitful interpretable results in the composites’ analysis are provided. |
| format | Article |
| id | doaj-art-b41a93e83f9c4e53b77ac94d494995e1 |
| institution | DOAJ |
| issn | 2666-8270 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-b41a93e83f9c4e53b77ac94d494995e12025-08-20T02:50:20ZengElsevierMachine Learning with Applications2666-82702024-12-011810060010.1016/j.mlwa.2024.100600Review of machine learning applications for defect detection in composite materialsVahid Daghigh0Hamid Daghigh1Thomas E. Lacy, Jr.2Mohammad Naraghi3Department of Aerospace Engineering, Texas A&M University, College Station, TX, USA; Corresponding author.School of Engineering, The University of British Columbia, BC, CanadaDepartment of Mechanical Engineering, Texas A&M University, College Station, TX, USADepartment of Aerospace Engineering, Texas A&M University, College Station, TX, USAMachine learning (ML) techniques have shown promising applications in a broad range of topics in engineering, composite materials behavior analysis, and manufacturing. This paper reviews successful ML implementations for defect and damage identification and progression in composites. The focus is on predicting composites' responses under specific loads and environments and optimizing setting and imperfection sensitivity. Discussions and recommendations toward promising ML implementation practices for fruitful interpretable results in the composites’ analysis are provided.http://www.sciencedirect.com/science/article/pii/S2666827024000768Machine learningComposite materialsDefectDamageDeep learning |
| spellingShingle | Vahid Daghigh Hamid Daghigh Thomas E. Lacy, Jr. Mohammad Naraghi Review of machine learning applications for defect detection in composite materials Machine Learning with Applications Machine learning Composite materials Defect Damage Deep learning |
| title | Review of machine learning applications for defect detection in composite materials |
| title_full | Review of machine learning applications for defect detection in composite materials |
| title_fullStr | Review of machine learning applications for defect detection in composite materials |
| title_full_unstemmed | Review of machine learning applications for defect detection in composite materials |
| title_short | Review of machine learning applications for defect detection in composite materials |
| title_sort | review of machine learning applications for defect detection in composite materials |
| topic | Machine learning Composite materials Defect Damage Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666827024000768 |
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