An experimental investigation on the fatigue fracture properties of polyacrylamide hydrogels - Tan, Matthew Guan Long (Nanyang Technological University, 2023)
Polyacrylamide (PAAm) hydrogel is a highly stretchable and nearly completely elastic material that is widely used in various applications, including water treatment, medicine, agriculture, oil recovery, and hydrogel devices. However, prolonged cyclic loading in these applications can lead to fatigue fracture. Despite recent interest in studying the fatigue fracture properties of hydrogels, little research has been conducted on the effects of loading rate and amplitude of stretch on the fatigue fracture properties of PAAm hydrogels. However, in practical applications of hydrogels, they are often subjected to varying loading rate and maximum displacement depending on their intended use. To address this gap, this study aims to investigate the impact of loading rate and maximum displacement on the fatigue fracture properties of PAAm hydrogels. The study will involve synthesizing PAAm hydrogel samples and subjecting them to cyclic loading using a pure shear test setup at varying loading rates of 200, 250, and 300 mm/min and maximum displacements of 20, 25, and 30 mm. A total of 90 PAAm hydrogel samples will be tested, with 10 samples for each parameter. The experimental results will be processed to obtain the fatigue life and average loading and unloading force against stretch plot for each sample. Further data analysis techniques, including correlation coefficient analysis and boxplots, will be used to identify the variables that have the most significant impact on the fatigue life of PAAm hydrogels and explore any patterns in the data. The findings of this study indicate that loading frequency, maximum displacement, and maximum stretch significantly affect the fatigue life of PAAm hydrogels. Specifically, increased maximum displacement or stretch results in decreased fatigue life, while increased loading frequency leads to increased fatigue life. These findings contribute to a better understanding of the fatigue fracture properties of PAAm hydrogels and have potential implications for the development of a machine learning models to accurately predict these properties.