FEM-Based Optimization of the Mechanical Properties of a Soft Robotic Neck

Finite element modeling (FEM) provides a power- ful and flexible approach for accurately simulating soft robots and their dynamics. This work presents a simulation-based framework for identifying the mechanical properties of a 3D- printed, cable-driven soft robotic neck made of thermoplastic polyurethane (TPU) with a Shore hardness of 82A. A Bayesian optimization method is employed during FEM simulations in the open-source SOFA framework to estimate material param- eters by minimizing the discrepancy between experimental data and simulation results. Cyclic traction tests were conducted on a custom test bench to obtain force and displacement data from one of the three actuation cables of the neck. Based on these observations, a hyperelastic constitutive model was selected, and its parameters optimized. The simulated response remained consistent with experimental measurements, indicating that the model captures the essential mechanical behavior of the system. This approach opens a practical alternative to direct material characterization and lays the foundation for future extensions involving viscoplastic models adaptive force–based control.