Dynamic mechanical and thermogravimetric properties of synthetized polyurethanes - Ornaghi Jr, Heitor Luiz; Neves, Roberta Motta; Monticeli, Francisco Maciel [UNESP]; Agnol, Lucas Dall (Springer Nature, 2022)
The ability to undergo from a deformed shape to its original shape when induced by an external stimulus brought many benefits in the polymer field. Despite the shape recovery, the structure vs property relationship has to be profoundly understood aiming to highlight the most important factors regarding the shape-memory polyurethanes (SMPUs). Based on a previous study, we show herein a complete description of the dynamic mechanical analysis for twelve different SMPUs. Also, it is presented an artificial neural network approach followed by a response surface methodology that allows modeling the dynamic mechanical curves with high reliability and low error. This principle expands the design versatility for SMP, which has broad implications in many other areas including soft robotics, flexible electronics, and medical devices.
Keywords: Artificial neural network; Biomedical applications; Dynamic mechanical properties; Polyurethane; Surface response methodology