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Lignocellulosic fibers are widely applied as reinforcement in polymer composites due to their properties. The thermal degradation behavior governs the maximum temperature at which the fiber can be applied without significant mass loss. It is possible to determine this temperature using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined by using Arrhenius-based equations, and more detailed characteristics of the material are obtained. However, every curve obtained at a distinct heating rate demands material, cost and time. Methods to predict thermogravimetric curves can be very useful in the materials science field, and in this sense, mathematical approaches are powerful tools, if well employed. For this reason, in the present study, thermogravimetric curves from curaua fiber were obtained at four different heating rates (5, 10, 20 and 40 °C·min−1) and Vyazovkin kinetic parameters were obtained using free available software. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM) aiming to obtain curves at any heating rate between the minimum and maximum experimental heating rates. Finally, Vyazovkin kinetic parameters were tested again, with the new predicted curves at the heating rates of 7, 15, 30 and 50 °C·min−1. Similar values of the kinetic parameters were obtained compared to the experimental ones. In conclusion, due to the capability to learn from the own data, ANN combined with SRM seems to be an excellent alternative to predict TG curves that do not test experimentally, opening the range of applications.

Considering that structural composites are typically composed of off-axis plies, i.e. quasi-isotropic stacking sequence, their strength and stiffness are time-dependent due to the viscoelastic character of polymer matrices. This work consists of determining creep, recovery, and stress relaxation of carbon fiber-reinforced polymer (CFRP) composites. Long-term experimental analyses are conducted via dynamic mechanical analysis under several temperatures and stress levels. From the experimental observations, the changes in the relaxation mechanisms are predicted using Fancey's latch model. The rate of relaxation at different temperatures is also covered. Since at certain strain levels the viscoelastic behavior cannot be properly determined, the stress-relaxation is determined using the time-temperature superposition (TTS) principle, considering nine temperatures at three strain levels in order to cover the three main regions of the composite system (glassy, glass transition and rubbery regions). The models and experiments herein presented can be extended to any polymeric system.