Prediction of temperature-frequency-dependent mechanical properties of composites based on thermoplastic liquid resin reinforced with carbon fibers using artificial neural networks

Abstract

A considerable interest has been generated in recent years in the use of thermoplastic polymers as matrices in the manufacture of advanced composites that require high reliability during long-term operations. In this research, a new Elium® acrylic matrix developed by Arkema was studied to evaluate the accelerated test methodology based on time-temperature superposition principle of Carbon Fiber/Elium® 150 composites. The results show that the high frequencies increase the glass transition (Tg) to higher values because the free volume is favored by polymer chains movement. In addition, artificial neural network has been used to model the temperature-frequency dependence of dynamic mechanical over the wide range of temperatures and frequencies due to its complex non-linear behavior. It has been observed that low frequencies result in low damping due to the lower internal friction, while high frequencies provide greater stiffness to the chains, resulting in a high damping. The long-term life prediction using master curves confirms that this new material can be considered to acoustic or vibrational damping purposes, considering its use in temperatures above Tg.