Materials knowledge system for nonlinear composites

TL;DR

This paper introduces a novel Materials Knowledge System framework that predicts the nonlinear stress-strain behavior of composites by integrating micromechanical data, statistical theories, and efficient computational modeling, reducing simulation costs.

Contribution

The paper develops a new data-driven, microstructure-sensitive modeling approach for nonlinear composites, combining statistical continuum theories with micromechanical data calibration.

Findings

Model accurately predicts finite element results

Significant reduction in computational cost

Applicable to composites with various strain hardening laws

Abstract

In this contribution, we present a new Materials Knowledge System framework for microstructure-sensitive predictions of effective stress--strain responses in composite materials. The model is developed for composites with a wide range of combinations of strain hardening laws and topologies of the constituents. The theoretical foundation of the model is inspired by statistical continuum theories, leveraged by mean-field approximation of self-consistent models, and calibrated to data obtained from micromechanical finite element simulations. The model also relies on newly formulated data-driven linkages between micromechanical responses (phase-average strain rates and effective strength) and microstructure as well as strength contrast of the constituents. The paper describes in detail the theoretical development of the model, its implementation into an efficient computational plasticity framework, calibration of the linkages, and demonstration of the model predictions on two-phase composites with isotropic constituents exhibiting linear and power-law strain hardening laws. It is shown that the model reproduces finite element results reasonably well with significant savings of the computational cost.