Lightweight Knee Orthosis for Athletic Rehabilitation: Achieving 40% Weight Reduction with Topology Optimization in Generative AI Design

The increasing demand for lightweight and cost-efficient orthopedic support in sports rehabilitation has accelerated the adoption of AI-driven design methodologies. This proposed work demonstrates the novelty of applying topology optimization within the Functional Generative Design (FGD) module of the 3DExperience platform to develop a structurally optimized knee orthosis. Under mixed mass constraints, the initially optimized thigh and shin brace designs achieved 51-gram and 62-gram weight reductions respectively while maintaining mechanical integrity under a 5000N physiological load. Finite element analysis revealed a 23 MPa reduction in Von Mises stress compared to the 50% mass design, indicating improved stress distribution. The final prototype braces, chosen with mass constraints of 45% and 50% for the thigh and shin respectively, 3D-printed using Polylactic Acid (PLA), were tested on a user’s leg and showed good anatomical fit, flexibility, and comfort, while achieving a combined final weight reduction of 122-grams (40%) compared to the original model. These improvements enhance wearer mobility while reducing material use and production cost.