Parametric Personalization and Design Optimization of 3D Printed 2-Degrees of Freedom Ankle-Foot Orthosis
thesis
posted on 2024-12-01, 00:00authored byAditya Rahul Patel
This thesis investigates the integration of parametric personalization and additive manufacturing (AM) strategies to develop a high-performance, user-adaptive Ankle-Foot Orthosis (AFO). The central research question explores how these methodologies can simplify end-effector personalization, enhance manufacturability, and improve mechanical performance. The hypothesis posits that combining parametric personalization with AM strategies, specifically planarization and geometry consolidation, will expedite the development of a high-performance, user-adaptive AFO, effectively meeting the research objectives. To address this hypothesis, three primary objectives were established: simplifying the design process by eliminating manual redesign based on user anthropometry; improving manufacturability by reducing material usage and production time through AM techniques; and enhancing mechanical performance by increasing torque capacity while optimizing weight for better user comfort and assistance. The study successfully validated all three objectives. A Python-based add-in was developed, enabling quick and easy parametric personalization and reducing redesign time from over 16 hours to approximately 12 seconds, thereby allowing clinicians to customize AFOs without engineering support. Geometry consolidation and planarization techniques significantly enhanced manufacturability, reducing total production time by 52% and material usage by 43%, and resulting in approximately 50% lower manufacturing costs. Additionally, mechanical performance enhancements included doubling the peak torque capacity from 40 Nm to 80 Nm and reducing device weight by 29%, thereby tripling the torque capacity-to-device weight ratio and enhancing user comfort and compliance. These advancements demonstrate that integrating parametric personalization with AM strategies effectively expedites the development of high-performance, user-adaptive AFOs, validating the initial hypothesis and contributing valuable insights to the field of gait rehabilitation technology.