Short-fiber-reinforced composites (SFRC) are high-performance engineering
materials for lightweight structural applications in the automotive and
electronics industries. Typically, SFRC structures are manufactured by
injection molding, which induces heterogeneous microstructures, and the
resulting nonlinear anisotropic behaviors are challenging to predict by
conventional micromechanical analyses. In this work, we present a machine
learning-based multiscale method by integrating injection molding-induced
microstructures, material homogenization, and Deep Material Network (DMN) in
the finite element simulation software LS-DYNA for structural analysis of SFRC.
DMN is a physics-embedded machine learning model that learns the microscale
material morphologies hidden in representative volume elements of composites
through offline training. By coupling DMN with finite elements, we have
developed a highly accurate and efficient data-driven approach, which predicts
nonlinear behaviors of composite materials and structures at a computational
speed orders-of-magnitude faster than the high-fidelity direct numerical
simulation. To model industrial-scale SFRC products, transfer learning is
utilized to generate a unified DMN database, which effectively captures the
effects of injection molding-induced fiber orientations and volume fractions on
the overall composite properties. Numerical examples are presented to
demonstrate the promising performance of this LS-DYNA machine learning-based
multiscale method for SFRC modeling.

本研究提出了一种基于机器学习的多尺度方法，将注射成型引起的微观结构，材料均质化和深度材料网络（DMN）与有限元模拟软件 LS-DYNA 相结合，用于短纤维增强复合材料的结构分析。通过将 DMN 与有限元耦合，我们开发了一种高度准确和高效的数据驱动方法，该方法以比高保真度直接数值模拟快若干个数量级的计算速度预测复合材料和结构的非线性行为。