ANN-aided evaluation of dual-phase microstructural fabric tensors for continuum plasticity representation

The evolution and orientation-dependent behavior of microstructures in dual-phase materials significantly affect the mechanical properties. How to quantify the microstructural effect in a continuum constitutive model, especially considering anisotropic elastic-plastic properties, is still a tough research topic for multiscale mechanics. In the present paper, the fabric tensors are successfully correlated with elastic-plastic mechanical properties of dual-phase materials with the help of the artificial neural network (ANN). The fabric tensors can be decomposed into isotropic and deviatoric components, which describe voluminal changes and orientationdependent properties of the microstructural material through data-driven analysis. A correlation analysis combined with gradient-based attributions revealed, furthermore, that a lower-order fabric tensor with fewer components was sufficient for the complex morphology of microstructures. For crystal symmetric materials, the second-order fabric tensors are sufficient to generate an adequate description of anisotropic dual-phase microstructures. The fabric tensors provide a bridge to connect the microstructural characteristics with phenomenological continuum plasticity for complex materials.

Automated summary

This paper investigates the role of fabric tensors in dual-phase materials and their relationship with elastic-plastic mechanical properties. The fabric tensors are successfully correlated with microstructural characteristics using an artificial neural network, providing a new approach for describing continuum plasticity of complex materials.