Research works

Below are some examples of research work published in peer-reviewed journals, reflecting our primary research focus. These selected publications are highlighted to showcase key findings, along with brief explanations.

Project 4: Future Applications of Image-Based SBFEM-BESO for Multi-Material Design

Title: Adaptive Mesh SBFE-BESO Approach for Multi-material Topology Optimization with Tunable Volume Constraints

Authors: Rut Su, Sawekchai Tangaramvong, and Chongmin Song

Our proposed SBFE-BESO framework has successfully been applied to traditional single-material design and can be further extended to multi-material configuration design. By incorporating a material interpolation scheme, it enables the creation of complex geometric designs for composite structures.

Project 3: Exploration of Image-Based SBFEM-BESO for Manufacturing Applications

Title: Isosurface-based marching cube algorithm for smooth geometric topology optimization within adaptive octree SBFE approach

Authors: Rut Su, Piyawat Boonlertnirun, Sawekchai Tangaramvong, and Chongmin Song

The SBFE-BESO approach has been further developed for applications in manufacturing. By utilizing an iso-surface function based on the marching cubes algorithm, the obtained topological results feature smooth geometric boundaries, making them directly suitable for constructing 3D printing prototypes.

Project 2: Extended Applications of Image-Based SBFEM-BESO in Elastodynamic Media

Title: Adaptive scaled boundary finite element method for two/three-dimensional structural topology optimization based on dynamic responses

Authors: Rut Su, Xiaoran Zhang, Sawekchai Tangaramvong, and Chongmin Song

Our investigation has found that static response-based topology optimization can lead to impractical designs when subjected to multiple loading conditions. To address this issue, we developed a dynamic response topology optimization approach that focuses on crucial peak points to reduce computational time. Additionally, we utilized commercial software, Fusion 360, to refine the boundaries and achieve a smoother final design.

Project 1: Integration of Image-Based SBFEM-BESO in Elastostatic Media

Title: Automatic Image-based SBFE-BESO Approach for Topology Structural Optimization

Authors: Rut Su, Sawekchai Tangaramvong, and Chongmin Song

This work introduces the integration of an advanced numerical technique, the image-based Scaled Boundary Finite Element Method (SBFEM), with Bi-directional Evolutionary Structural Optimization (BESO) to solve topology optimization problems. The proposed approach enables adaptive mesh design for linear elastic materials under static loading (elastostatic), significantly reducing computational effort.