Predictions for hydrogel fracture experiment using data processing
Hydrogels, intricate three-dimensional networks of hydrophilic polymers, are vital in biomedical engineering and regenerative medicine. Predicting their fracture behaviour remains challenging due to complex viscoelastic properties. While molecular modelling methods like High-Resolution Transmissi...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176519 |
| _version_ | 1826125797638799360 |
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| author | Siak, Yen Kar |
| author2 | Li Hua |
| author_facet | Li Hua Siak, Yen Kar |
| author_sort | Siak, Yen Kar |
| collection | NTU |
| description | Hydrogels, intricate three-dimensional networks of hydrophilic polymers, are vital in
biomedical engineering and regenerative medicine. Predicting their fracture behaviour
remains challenging due to complex viscoelastic properties. While molecular
modelling methods like High-Resolution Transmission Electron Microscopy
(HRTEM) are conventional, they are computationally intensive. This study explores
Convolutional Long Short-term Memory (Conv-LSTM) deep learning techniques for
accurate hydrogel fracture prediction. Conv-LSTM, adept at handling spatiotemporal
data, shows promise in modelling fracture mechanisms across materials.
In this report, the research develops and optimizes a Conv-LSTM-based predictive
model for enhanced hydrogel fracture prediction accuracy. Methodology involves data
collection from tensile testing, preprocessing, model selection, training, and validation.
Evaluation metrics include Mean Squared Error (MSE) and Structural Similarity Index
(SSIM). Results demonstrate Conv-LSTM's high precision in capturing hydrogel
fracture patterns, offering insights into fracture dynamics. Computational efficiency is
assessed through GPU and CPU training comparisons.
Future research will focus on expanding the dataset, exploring multi-scale prediction
approaches, estimating prediction uncertainty, adapting the model to related domains,
and optimizing for real-time prediction. Addressing these areas will advance predictive
modelling for hydrogel fracture, benefiting biomedical engineering and materials
science. |
| first_indexed | 2024-10-01T06:42:14Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176519 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:42:14Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1765192024-05-18T16:53:13Z Predictions for hydrogel fracture experiment using data processing Siak, Yen Kar Li Hua School of Mechanical and Aerospace Engineering LiHua@ntu.edu.sg Engineering Hydrogels, intricate three-dimensional networks of hydrophilic polymers, are vital in biomedical engineering and regenerative medicine. Predicting their fracture behaviour remains challenging due to complex viscoelastic properties. While molecular modelling methods like High-Resolution Transmission Electron Microscopy (HRTEM) are conventional, they are computationally intensive. This study explores Convolutional Long Short-term Memory (Conv-LSTM) deep learning techniques for accurate hydrogel fracture prediction. Conv-LSTM, adept at handling spatiotemporal data, shows promise in modelling fracture mechanisms across materials. In this report, the research develops and optimizes a Conv-LSTM-based predictive model for enhanced hydrogel fracture prediction accuracy. Methodology involves data collection from tensile testing, preprocessing, model selection, training, and validation. Evaluation metrics include Mean Squared Error (MSE) and Structural Similarity Index (SSIM). Results demonstrate Conv-LSTM's high precision in capturing hydrogel fracture patterns, offering insights into fracture dynamics. Computational efficiency is assessed through GPU and CPU training comparisons. Future research will focus on expanding the dataset, exploring multi-scale prediction approaches, estimating prediction uncertainty, adapting the model to related domains, and optimizing for real-time prediction. Addressing these areas will advance predictive modelling for hydrogel fracture, benefiting biomedical engineering and materials science. Bachelor's degree 2024-05-16T07:40:59Z 2024-05-16T07:40:59Z 2024 Final Year Project (FYP) Siak, Y. K. (2024). Predictions for hydrogel fracture experiment using data processing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176519 https://hdl.handle.net/10356/176519 en B138 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Siak, Yen Kar Predictions for hydrogel fracture experiment using data processing |
| title | Predictions for hydrogel fracture experiment using data processing |
| title_full | Predictions for hydrogel fracture experiment using data processing |
| title_fullStr | Predictions for hydrogel fracture experiment using data processing |
| title_full_unstemmed | Predictions for hydrogel fracture experiment using data processing |
| title_short | Predictions for hydrogel fracture experiment using data processing |
| title_sort | predictions for hydrogel fracture experiment using data processing |
| topic | Engineering |
| url | https://hdl.handle.net/10356/176519 |
| work_keys_str_mv | AT siakyenkar predictionsforhydrogelfractureexperimentusingdataprocessing |