Experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel
The polyacrylamide (PAAm) hydrogel, a transparent and nearly elastic material, has applications in diverse fields such as biomedical, agricultural, and water treatment. While extensive research has explored its many mechanical properties, a critical gap exists in understanding the impact of de...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/172821 |
| _version_ | 1811678750254825472 |
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| author | Tia, Yi Ken |
| author2 | Li Hua |
| author_facet | Li Hua Tia, Yi Ken |
| author_sort | Tia, Yi Ken |
| collection | NTU |
| description | The polyacrylamide (PAAm) hydrogel, a transparent and nearly elastic material, has applications
in diverse fields such as biomedical, agricultural, and water treatment. While extensive research
has explored its many mechanical properties, a critical gap exists in understanding the impact of
dehydration on PAAm hydrogel fatigue fracture. This is crucial due to PAAm hydrogel’s
susceptibility to dehydration given its rich-water structure. Therefore, this project aims to
investigate the fatigue fracture of PAAm hydrogel under dehydration, focusing specifically on
fatigue life. Machine learning (ML) models will be employed to predict the fatigue life, addressing
the limitations of existing mathematical models.
The study begins with a pure shear test of PAAm hydrogel under dehydration, gathering 90 raw
data samples with variations in loading rates (200/250/300 mm/min) and maximum displacement
(20/25/30 mm). A supplementary experiment was conducted to compare fatigue fracture behaviour
under both dehydration and constant moisture conditions using 10 samples. The 90 raw data
samples were processed using MATLAB to generate graphs for fatigue life determination and
hysteresis loss analysis. Feature extraction was also performed using MATLAB, followed by data
cleaning and statistical analysis using Jupyter Notebook. Subsequently, augmented data was
generated from the original data and integrated with it to create a new dataset through the data
augmentation process. Both the original and new datasets were separately fitted with 5 ML models
to predict fatigue life under dehydration.
The study revealed that dehydration significantly alters fatigue fracture behaviour, leading to an
abrupt and extensive fracture at fatigue life and a transition to an opaque state. The fatigue life
generally tends to increase with loading frequency and decrease with maximum stretch. Hysteresis
loss was also found to be negligible during fatigue. Moreover, the KNN model was identified as
the most suitable ML model, achieving a test score of 0.68 for the original dataset and 0.90 for the
new dataset.
These findings contribute to a better understanding and highlight the adverse effects of dehydration
on the fatigue fracture of PAAm hydrogel. They also demonstrate the potential of ML models in
predicting the mechanical properties of hydrogels for future studies. |
| first_indexed | 2024-10-01T02:58:13Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/172821 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:58:13Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1728212023-12-30T16:49:59Z Experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel Tia, Yi Ken Li Hua School of Mechanical and Aerospace Engineering LiHua@ntu.edu.sg Engineering::Mechanical engineering Engineering::Computer science and engineering Engineering::Materials The polyacrylamide (PAAm) hydrogel, a transparent and nearly elastic material, has applications in diverse fields such as biomedical, agricultural, and water treatment. While extensive research has explored its many mechanical properties, a critical gap exists in understanding the impact of dehydration on PAAm hydrogel fatigue fracture. This is crucial due to PAAm hydrogel’s susceptibility to dehydration given its rich-water structure. Therefore, this project aims to investigate the fatigue fracture of PAAm hydrogel under dehydration, focusing specifically on fatigue life. Machine learning (ML) models will be employed to predict the fatigue life, addressing the limitations of existing mathematical models. The study begins with a pure shear test of PAAm hydrogel under dehydration, gathering 90 raw data samples with variations in loading rates (200/250/300 mm/min) and maximum displacement (20/25/30 mm). A supplementary experiment was conducted to compare fatigue fracture behaviour under both dehydration and constant moisture conditions using 10 samples. The 90 raw data samples were processed using MATLAB to generate graphs for fatigue life determination and hysteresis loss analysis. Feature extraction was also performed using MATLAB, followed by data cleaning and statistical analysis using Jupyter Notebook. Subsequently, augmented data was generated from the original data and integrated with it to create a new dataset through the data augmentation process. Both the original and new datasets were separately fitted with 5 ML models to predict fatigue life under dehydration. The study revealed that dehydration significantly alters fatigue fracture behaviour, leading to an abrupt and extensive fracture at fatigue life and a transition to an opaque state. The fatigue life generally tends to increase with loading frequency and decrease with maximum stretch. Hysteresis loss was also found to be negligible during fatigue. Moreover, the KNN model was identified as the most suitable ML model, achieving a test score of 0.68 for the original dataset and 0.90 for the new dataset. These findings contribute to a better understanding and highlight the adverse effects of dehydration on the fatigue fracture of PAAm hydrogel. They also demonstrate the potential of ML models in predicting the mechanical properties of hydrogels for future studies. Bachelor of Engineering (Mechanical Engineering) 2023-12-26T06:03:41Z 2023-12-26T06:03:41Z 2023 Final Year Project (FYP) Tia, Y. K. (2023). Experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172821 https://hdl.handle.net/10356/172821 en B409 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Mechanical engineering Engineering::Computer science and engineering Engineering::Materials Tia, Yi Ken Experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel |
| title | Experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel |
| title_full | Experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel |
| title_fullStr | Experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel |
| title_full_unstemmed | Experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel |
| title_short | Experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel |
| title_sort | experimental analysis and machine learning based predictions of fatigue fracture in dehydrated polyacrylamide hydrogel |
| topic | Engineering::Mechanical engineering Engineering::Computer science and engineering Engineering::Materials |
| url | https://hdl.handle.net/10356/172821 |
| work_keys_str_mv | AT tiayiken experimentalanalysisandmachinelearningbasedpredictionsoffatiguefractureindehydratedpolyacrylamidehydrogel |