Artificial Neural Network Approach to Determine Elastic Modulus of Carbon Fiber-Reinforced Laminates

Abstract

Recognized as a prominent material for engineering applications, carbon fiber-reinforced laminated composites require significant effort to characterize due to their anisotropic structure and viscoelastic nature. Dynamic mechanical analysis has been used to accelerate the testing process by transforming the measured viscoelastic properties to elastic modulus. To expand the transformation to anisotropic materials, artificial neural network approach is used to build the master relationship of the storage modulus in three in-plane directions. Using rotation transformation, the stiffness tensor can be calculated to extrapolate the frequency domain viscoelastic properties in any orientation with respect to the fiber direction. The viscoelastic properties are transformed to time domain relaxation function using the linear relationship of viscoelasticity. Stress response with a certain strain history is predicted and the elastic modulus is extracted. Compared to the experimental flexural test results, the artificial neural network-based method achieved an error of less than 7.3%. The results show that the transformation can predict the anisotropic material behavior at a wide range of temperatures and strain rates.