The use of 3D printing in robotics enables the fabrication of lightweight, customized, and geometrically complex structures, such as lattices and compliant mechanisms. While these flexible printed components expand design possibilities, they also introduce challenges in accurately predicting dynamic behavior. Traditional rigid-body models often neglect structural deformations and vibrations, which can critically influence performance, stability, and control.

This work presents initial advances toward a computational framework for the dynamics of flexible multibody 3D-printed robotic structures. A two-link mechanism is adopted as a case study, modeled in MATLAB Simscape Multibody, where both rigid and flexible assumptions are compared. Parametric analyses are performed to investigate the influence of geometric properties, mass distribution, and structural stiffness on system dynamics, highlighting the trade-offs between lightweight design and vibration sensitivity.

Beyond conventional finite-element and multibody approaches, the framework aims to incorporate AI-driven surrogate models and reduced-order techniques to accelerate simulation, enabling real-time predictive tools for design and control. This integration opens the door to optimization studies, where the distribution of mass, topology of the printed structure, and material selection can be tailored to achieve enhanced dynamic performance.

The long-term objective is to establish reliable, computationally efficient methods for the modeling, optimization, and control of 3D-printed flexible robotic mechanisms, contributing to safer, smarter, and more efficient next-generation robotic systems.