Neural network constitutive modelling for non-linear characterization of anisotropic materials

摘要

This paper presents a new technique of neural network constitutive modelling for non-linear characterization of anisotropic materials. The proposed technique, based on a recently developed energy-based characterization framework, derives the variations of the external work applied to and the strain energy induced in a specimen. The error between the variations of the energies is subsequently applied to correct the neural network properties by using a modified backpropagation algorithm. Unlike the conventional techniques for neural network constitutive modelling, the proposed technique develops models by quantifying the deformation of the specimen on a continuum basis. This allows the neural network constitutive models to be constructed from a single load test of one specimen. Numerical examples first examine the effect of specimen geometries and loading conditions. The effect of noise in the experimental measurements is subsequently investigated while having the applicability for non-linear constitutive behaviour shown thereafter. The application for anisotropic materials is finally demonstrated by modelling a unidirectional lamina based on the measurements of a biaxial load test on a balanced laminate.