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 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.

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 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 4: Development of an Adaptive Quadtree Mesh SBFEM for Infill Porous Structures

Title: To be Proposed

Authors: Sompong Rungsawang, Rut Su, Junqi Zhang, Pengchen Liu, and Sawekchai Tangaramvong

The SBFE approach has been developed into another optimization algorithm so-called Proportional Topology Optimization and further applied the application to design the configuration of infill porous structures (named as IPTO). In this project, we are not only solved for the classical benchmark example, but we can also achieve the adaptive mesh infill layout for the complex geometry such as the femur-shaped structure, which can be pathed into the field of biomechanical engineering.

Project 5: 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 6: Applications of SBFE-BESO for Composite Design with different mechanical properties

Title: To be Proposed

Authors: Rut Su, Sawekchai Tangaramvong, and Chongmin Song

Our proposed SBFE–BESO framework can be readily extended to composite configuration design. By assigning distinct material properties to the tension and compression regions, it enables the generation of complex geometries tailored for composite structures.

Project 7: Exploration of Image-Based SBFEM-MMPTO for Manufacturing Applications

Title: To be Proposed

Authors: Rut Su, Sompong Rungsawang, Sawekchai Tangaramvong, and Chongmin Song

Building upon the recent development, the technique has been advanced into an SBFE–MMPTO framework to efficiently address multi-material design problems, ensuring clear separation of different material regions within a structure. This capability makes the proposed approach particularly promising for applications in additive manufacturing, where precise control of material distribution is essential.