Performance optimization of shape memory epoxy polymers based on machine learning

In this study, we proposed a machine learning (ML) method to optimize the comprehensive performance of shape memory epoxy polymers (SMEPs) based on experimental data as samples. Firstly, a series of SMEPs specimens were prepared, and their properties were evaluated respectively by testing four indexes including the glass transition temperature ( T g ), bending strength, strain fixation rate ( R f ), and strain recovery rate ( R r ). Subsequently, ML used these experimental data as samples for feature learning to investigate the influence of each component on these properties. The results indicated that methyltetrahydrophthalic anhydride was more favorable to T g and bending strength than methylhexahydrophthalic anhydride (MHHPA) as a curing agent. However, a certain amount of MHHPA must be included in the system to guarantee a higher R f and R r . Moreover, the right amount of bisphenol A cyanate ester in the system improved the comprehensive properties of SMEPs, especially the shape memory effect. Finally, a SMEPs system with superior properties was acquired through the optimization of four indexes of T g , bending strength, R f and R r . Therefore, this study shows that ML methods can also be used to investigate SMEPs that require more specific excellent performance.