Automatic Image-Based SBFE-BESO Approach for Topology Structural Optimization

This paper presents automatic image-based topology optimization with the highest mechanical stiffness. A socalled bi-evolutionary structural optimization is encoded within the scaled boundary finite element (SBFE) framework, incorporating digital image-based quadtree (2D) and octree (3D) decomposing mesh constructions. The information on both geometry and material distribution is transparently transferred using the standard tessellation language (STL) format of digital images, which can be directly modelled for stress analyses. The polytope SBFE method allows for a computationally advantageous model construction with hanging nodes. A convolution filter scheme is applied for colour intensity adjustment between solid and void regions, leading to multi-levels in the quadtree/octree hierarchy. Combined with the convolution filter method, the matrix precomputation technique, within the polytope SBFE decomposition framework enables the automatic adaptive (digital image-based) model construction from the limited number of master cells with balanced quadtree/octree meshes. Hence, the highly efficient analysis-ready design scheme for 2D and 3D structures is carried out. The proposed approach significantly reduces the number of degrees of freedom required, giving thus the computationally efficient efforts that are seen to enhance the high-resolution optimal topology of the practical-scale structures.