A modular pipeline for improving the constitutive modelling of composite materials is proposed.The method is leveraged here for the development of subject-specific spatially-varying brain white matter mechanical properties. For this application, white matter microstructural information is extracted from diffusion magnetic resonance imaging (dMRI) scans, and used to generate hundreds of representative volume elements (RVEs) with randomly distributed fibre properties. By automatically running finite element analyses on these RVEs, stress-strain curves corresponding to multiple RVE-specific loading cases are produced. A mesoscopic constitutive model homogenising the RVEs’ behaviour is then calibrated for each RVE, producing a library of calibrated parameters against each set of RVE microstructural characteristics. Finally, a machine learning layer is implemented to predict the constitutive model parameters directly from any new microstructure. The results show that the methodology can predict calibrated mesoscopic material properties with high accuracy. More generally, the overall framework allows for the efficient simulation of the spatially-varying mechanical behaviour of composite materials when experimentally measured location-specific fibre geometrical characteristics are provided.

提出了一种用于改善复合材料本构模型的模块化管道。在此，该方法被用于开发特定于对象的空间变化的脑白质力学性能。对于此应用程序，白质微结构信息是从扩散磁共振成像（dMRI）扫描中提取的，并用于生成数百个具有随机分布的纤维特性的代表性体积元素（RVE）。通过在这些RVE上自动运行有限元分析，可以生成对应于多个RVE特定载荷工况的应力-应变曲线。然后针对每个RVE校准使RVE行为均质的介观本构模型，从而针对每个RVE微观结构特征生成校准参数库。最后，实现了机器学习层，以直接从任何新的微观结构预测本构模型参数。结果表明，该方法可以高精度地预测校准的介观材料的性能。更一般地，当提供了实验测量的位置特定的纤维几何特性时，整个框架允许有效地模拟复合材料的空间变化的机械性能。