An Automated Design-to-Print Optimization Framework for 3D-Printed Arm Support Structures in Upper Limb Exoskeletons

Upper limb exoskeletons (ULEs) have shown significant potential in enhancing mobility and rehabilitation outcomes. However, pediatric applications pose unique challenges due to anatomical differences and the need for adaptable and comfortable components. This study develops an automated design-to-print optimization framework that combines simulation-informed design of experiments, physical testing, and artificial intelligence (AI) and machine learning (ML) to rapidly identify design and print parameters for 3D-printed exoskeleton joints. The workflow employs a two-stage design of experiments (DoE) approach: the first identifies optimal geometric parameters using finite element analysis (FEA), and the second optimizes print parameters through mechanical testing. The resulting data are used to train a multiple linear regression (MLR) model that predicts joint strength and print quality from design and manufacturing settings. Statistical analysis identified shaft diameter as the most significant design parameter and wall perimeter count as the dominant print factor influencing load capacity. The trained MLR model achieved an average prediction error below 0.10 MPa. This integrated workflow demonstrates how AI-assisted surrogate modeling can bridge virtual design and physical fabrication, enabling rapid, data-driven optimization of 3D-printed rehabilitation components.