Design and Optimization of an Adaptive Robotic Gripper using Finite Element Analysis and Generative Design

Various industries use robotic grippers. These grippers come in varied types and functions and protect workers' health and improve industries' growth. However, specialized grippers cannot adapt to complex materials; thus, they become a liability to these sectors in the long run. Adaptive grippers, a type of gripper, are not limited to specific object shapes. These tools can grasp objects of different shapes and sizes, thus showing a remarkable potential in various applications. Therefore, this study focused on designing a rigid body, two-finger adaptive robotic gripper and optimizing its mass without compromising its strength while maintaining a safety factor level of 2. The loading conditions for different types of grip sizes were formulated. The researchers gathered data from the related literature, integrated these inputs to design the gripper, simulated and optimized the gripper design using generative design, then validated the results by finite element analysis. Results show that the total mass of the gripper was reduced by 79% while maintaining a safety factor level of 2. These findings then indicate that the gripper has been successfully optimized and hint at the potential for better and lighter technologies to be developed in the future. © 2023 CAD Solutions, LLC.