A PYTHON-BASED FRAMEWORK FOR CAD-INTEGRATED 3D TOPOLOGY OPTIMIZATION

Abstract

Topology optimization (TO) is a powerful and widely used design methodology in structural engineering and product design. While numerous academic software tools exist for 2D and 3D TO, they often focus on relatively simple geometries, support only a single TO method, and lack user-friendly graphical interfaces. Furthermore, these tools typically do not integrate with commercial CAD platforms, making it difficult to import complex models or export optimized designs for further develop-ment. These limitations hinder the broader adoption of TO tools in both educational settings and research focused on advanced TO algorithm development. This thesis presents a Python-based framework that addresses these gaps by provid-ing a front-end graphical interface for defining and solving topology optimization problems, including those involving complex 3D geometries. The framework sup-ports multiple TO algorithms and solvers, enhancing its flexibility for a wide range of applications. Additionally, we introduce a novel post processing tool that pre-serves essential design features while refining the optimized geometry. The system is integrated with SolidWorks, enabling seamless import and export of design ge-ometries, thereby facilitating a smooth workflow between topology optimization and downstream CAD modeling. Finally, we use the framework to benchmark several 3D thermal and structural TO problems.