This thesis presents the design and computational optimization of a two-axis nano-positioning stage. The devised stage relies on double parallelogram flexure bearings with under-constraint eliminating linkages to enable motion in the primary degrees-of-freedom. The structural parameters of the underlying flexures were optimized to provide a large-range and high bandwidth with sub-micron resolution while maintaining a compact size. A finite element model was created to establish a functional relationship between the geometry of the flexure elements and the stiffness behavior. Then, a neural network was trained from the simulation results to explore the design space with a low computational expense. The neural net was integrated with a genetic algorithm to optimize the design of the flexures for compactness and dynamic performance. The optimal solutions resulted in a reduction of stage footprint by 14% and an increase in the first natural frequency by 75% relative to a baseline design, all while preserving the same 50mm range in each axis with a factor of safety of 2. This confirms the efficacy of the proposed approach in improving stage performance through an optimization of its constituent flexures.Mechanical Engineerin
