Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures
This work addresses automating image-based modeling of intricate microstructures for materials science, but it appears incremental as it builds on existing SVM and meshfree methods.
The authors tackled automating discretization and approximation for digital modeling of composite microstructures from Micro-CT images by using SVM classification for discretization and proposing an Interface-Modified Reproducing Kernel Particle Method (IM-RKPM) to handle weak discontinuities without duplicated degrees of freedom, validating effectiveness on polymer-ceramic composites.
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.