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Performance optimization of shape memory epoxy polymers based on machine learning
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DOI：10.1002/pat.5595.png)
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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.
Keyword:
feature learning
machine learning
performance optimization
shape memory epoxy polymers
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