Data augmentation strategies for machine learning in polymer composites
Machine learning has become ubiquitous in recent years across various sectors. This report presents prevalent machine learning techniques and their potential applications in the field of polymer composites. However, a sufficiently large dataset is requisite for learning algorithms to make accurate p...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/159161 |
| _version_ | 1811693890373156864 |
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| author | Ho, Agnes Lin Xuan |
| author2 | Sunil Chandrakant Joshi |
| author_facet | Sunil Chandrakant Joshi Ho, Agnes Lin Xuan |
| author_sort | Ho, Agnes Lin Xuan |
| collection | NTU |
| description | Machine learning has become ubiquitous in recent years across various sectors. This report presents prevalent machine learning techniques and their potential applications in the field of polymer composites. However, a sufficiently large dataset is requisite for learning algorithms to make accurate predictions. Existing data augmentation strategies meant to boost the amount of data available so that they can meet the requirements for machine learning were then discussed, which led to implementation of the Knowledge-Based Data Boosting technique for this research project. Software development of the technique was conducted accordingly and applied to case studies associated with polymer composites. After four iterations of the augmentation process, the first case study achieved a boosting factor of approximately 10:1 for two datasets. The second attained a boosting factor of nearly 9:1, with further potential to multiply the number of data points for all datasets if supplementary rounds of augmentations are performed. Data collection of experimental results can be a time-consuming and costly endeavour. Through its generation of an additional set of realistic data points, application of the KBDB data augmentation strategy will consequently enable researchers to utilise machine learning algorithms with existing datasets even when they do not have ample data. |
| first_indexed | 2024-10-01T06:58:52Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/159161 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:58:52Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1591612023-03-04T20:12:16Z Data augmentation strategies for machine learning in polymer composites Ho, Agnes Lin Xuan Sunil Chandrakant Joshi School of Mechanical and Aerospace Engineering MSCJoshi@ntu.edu.sg Engineering::Mechanical engineering Machine learning has become ubiquitous in recent years across various sectors. This report presents prevalent machine learning techniques and their potential applications in the field of polymer composites. However, a sufficiently large dataset is requisite for learning algorithms to make accurate predictions. Existing data augmentation strategies meant to boost the amount of data available so that they can meet the requirements for machine learning were then discussed, which led to implementation of the Knowledge-Based Data Boosting technique for this research project. Software development of the technique was conducted accordingly and applied to case studies associated with polymer composites. After four iterations of the augmentation process, the first case study achieved a boosting factor of approximately 10:1 for two datasets. The second attained a boosting factor of nearly 9:1, with further potential to multiply the number of data points for all datasets if supplementary rounds of augmentations are performed. Data collection of experimental results can be a time-consuming and costly endeavour. Through its generation of an additional set of realistic data points, application of the KBDB data augmentation strategy will consequently enable researchers to utilise machine learning algorithms with existing datasets even when they do not have ample data. Bachelor of Engineering (Mechanical Engineering) 2022-06-10T12:40:58Z 2022-06-10T12:40:58Z 2022 Final Year Project (FYP) Ho, A. L. X. (2022). Data augmentation strategies for machine learning in polymer composites. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159161 https://hdl.handle.net/10356/159161 en B201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Mechanical engineering Ho, Agnes Lin Xuan Data augmentation strategies for machine learning in polymer composites |
| title | Data augmentation strategies for machine learning in polymer composites |
| title_full | Data augmentation strategies for machine learning in polymer composites |
| title_fullStr | Data augmentation strategies for machine learning in polymer composites |
| title_full_unstemmed | Data augmentation strategies for machine learning in polymer composites |
| title_short | Data augmentation strategies for machine learning in polymer composites |
| title_sort | data augmentation strategies for machine learning in polymer composites |
| topic | Engineering::Mechanical engineering |
| url | https://hdl.handle.net/10356/159161 |
| work_keys_str_mv | AT hoagneslinxuan dataaugmentationstrategiesformachinelearninginpolymercomposites |