Optimal development for a 3D-printed gripper for biomedical and micromanipulation applications by non-parametric regression-based metaheuristic technique

Abstract

With the advancement of bioengineering and robotic engineering, medical robots have been increasing concern about manipulating the microobject or cells. Although the rigid robots have a stable operation, they inherit many limitations such as the complex assembly process of joints-coupled rigid links and expensive costs. Especially, clearances between kinematic joints cause vibrations that damage microobject. To cope with such problems, a flexure-based polylactic acid (PLA) gripper is developed to realize precise motion in medical robots. The proposed gripper is 3D printed by fused deposition modeling technique with advantages of monolithic structure, jointless and cheap cost. Prior to the gripper fabrication, the optimization development of the gripper is conducted employing a combination of the non-parametric regression (NPR) and multi-objective genetic algorithm (MOGA). Numerical data samples are collected by the finite element method. The modeling results were well formulated utilizing the NPR method with the R2 value greater than 0.9. The Pareto-optimum design results identified that the gripper can provide a high displacement of 2 mm and a small stress of 41 MPa via MOGA. Additionally, the proposed flexure-based compliant PLA gripper can work with a safety factor higher than 1.6. The experiment tests on the prototype of the gripper are close to the estimated values.