Balancing Accuracy and Efficiency: Optimizing Soft Gripper Performance through numerical Simulations for Improved Decision-Making in Manufacturing

This study examines the trade-off between accuracy and computational efficiency in simulating soft robotic grippers using Finite Element (FEM) methods comparing simplified and complex gripper geometries, interacting with regular and irregular objects and Finite volume (FVM) methods for airflow effects. Simulations showed that simplified models under pressure without object contact reduced computational time by 30%, with less than 1% deformation variance but 16.8% lower stress values. Simplified models suffice for deformation analysis, whereas complex geometries are essential for precise stress predictions. Incorporating gripping surface curvatures and surface connections to complex geometries increased reaction forces by 3.9% for regular and 6.4% for non-regular objects, thereby enhancing force transmission in interactions with regular and non-regular objects, respectively. Environmental factors such as airflow and temperature proved to have a negligible impact on deformation (0.8% and 0.6%, respectively). The simulation results lay the groundwork for future Design Space Exploration (DSE) with multi-gripper systems, enabling optimization of key variables such as actuator configurations, alignment, attachment angles, and material properties while accommodating specific handling needs like diverse object shapes, weights, and orientations.