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Accelerated Aging on the Compression Properties of a Green Polyurethane Foam: Experimental and Numerical Analysis.

Enio H P Da SilvaSilvio de BarrosAndré Costa VieiraRomeu R C Da CostaMarcelo Leite Ribeiro
Published in: Polymers (2023)
The aim of this work is to evaluate the changes in compression properties of a bio-based polyurethane foam after exposure to 90 °C for different periods of time, and to propose a method to extrapolate these results and use a numerical approach to predict the compression behaviour after degradation for untested conditions at different degradation times and temperatures. Bio-based polymers are an important sustainable alternative to oil-based materials. This is explained by the foaming process and the density along the material as it was possible to see in a digital image correlation analysis. After 60 days, stiffness was approximately decreased by half in both directions. The decrease in yield stress due to thermo-oxidative degradation had a minor effect in the foaming directions, changing from 352 kPa to 220 kPa after 60 days, and the transverse property was harshly impacted changing from 530 kPa to 265 kPa. The energy absorption efficiency was slightly affected by degradation. The simulation of the compression stress-strain curves were in accordance to the experimental data and made it possible to predict the changes in mechanical properties for intermediate periods of degradation time. The plateau stress for the unaged foam transverse to the foaming direction presented experimental and numerical values of 450 kPa and 470 kPa, respectively. In addition, the plateau stresses in specimens degraded for 40 days present very similar experimental and numerical results in the same direction, at 310 kPa and 300 kPa, respectively. Therefore, this paper presents important information regarding the life-span and degradation of a green PUF. It provides insights into how compression properties vary along degradation time as function of material operation temperature, according to the Arrhenius degradation equation.
Keyphrases
  • machine learning
  • big data
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  • electronic health record