Support Vector Machine Guided Reproducing Kernel Particle Method for
  Image-Based Modeling of Microstructures
Yanran Wang Jonghyuk Baek Yichun Tang Jing Du Mike Hillman J. S. Chen
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.