This work presents an approach for automating the discretization and
approximation procedures in constructing digital representations of composites
from Micro-CT images featuring intricate microstructures. The proposed method
is guided by the Support Vector Machine (SVM) classification, offering an
effective approach for discretizing microstructural images. An SVM soft margin
training process is introduced as a classification of heterogeneous material
points, and image segmentation is accomplished by identifying support vectors
through a local regularized optimization problem. In addition, an
Interface-Modified Reproducing Kernel Particle Method (IM-RKPM) is proposed for
appropriate approximations of weak discontinuities across material interfaces.
The proposed method modifies the smooth kernel functions with a regularized
heavy-side function concerning the material interfaces to alleviate Gibb's
oscillations. This IM-RKPM is formulated without introducing duplicated degrees
of freedom associated with the interface nodes commonly needed in the
conventional treatments of weak discontinuities in the meshfree methods.
Moreover, IM-RKPM can be implemented with various domain integration
techniques, such as Stabilized Conforming Nodal Integration (SCNI). The
extension of the proposed method to 3-dimension is straightforward, and the
effectiveness of the proposed method is validated through the image-based
modeling of polymer-ceramic composite microstructures.

该研究提出了一种基于支持向量机（SVM）分类的方法，以自动化复合材料数字表示构建中的离散化和近似过程，该方法还使用 IM-RKPM 算法进行适当的近似处理，并通过对聚合物 - 陶瓷复合材料微观结构建模进行验证。