Knowledge Bank

Topology optimization is a computational method widely used in structural design to distribute material within a defined design space to maximize performance objectives such as stiffness, weight reduction, or energy efficiency. Though it has proven to be a powerful tool for structural design, its widespread adoption is still hampered as it is computationally intensive, requiring significant processing power and time, particularly for large-scale, high-resolution problems. Additionally, the optimized designs often result in complex, organic shapes that, while theoretically optimal, are difficult to manufacture using conventional methods and fail to align with industry preferences for simpler geometries. This research proposes a real-time structural analysis framework that performs live geometric primitive extraction concurrently with topology optimization. Rather than treating primitive detection as a post-processing step, this approach continuously analyzes the evolving topology during optimization to identify and classify structural primitives, including junctions (T-junctions, X-junctions, Y-junctions, L-junctions), beam segments (main beams, connectors, secondary members), and connection endpoints. The framework employs computer vision techniques, including binary thresholding, morphological skeletonization, junction clustering through neighbor analysis, and contour-based connection tracing. By integrating this analysis directly into the optimization loop, the system provides real-time visualization of structural evolution, immediate quantification of design complexity through junction counts and connectivity metrics, and establishes a foundation for future manufacturing-aware optimization where primitive-based constraints could guide designs toward manufacturability.