Abstract
Modeling for robotic joints is actually complex and may lead to wrong Pareto-optimal solutions. Hence, this paper develops a new hybrid approach for multiobjective optimization design of a flexure elbow joint. The joint is designed for the upper-limb assistive device for physically disable people. The optimization problem considers three design variables and two objective functions. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), Kigring metamodel, and multiobjective genetic algorithm (MOGA) is developed. The CDD is used to establish the number of numerical experiments. The FEM is developed to retrieve the strain energy and the reaction torque of joint. And then, the Kigring metamodel is used as a black-box to find the pseudoobjective functions. Based on pseudoobjective functions, the MOGA is applied to find the optimal solutions. Traditionally, an evolutionary optimization algorithm can only find one Pareto front. However, the proposed approach can generate 6 Pareto-optimal solutions, as near optimal candidates, which provides a good decision-maker. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results found that the optimal strain energy is about 0.0033 mJ and the optimal torque is approximately 588.94 Nm. Analysis of variance is performed to identify the significant contribution of design variables. The sensitivity analysis is then carried out to determine the effect degree of each parameter on the responses. The predictions are in a good agreement with validations. It confirms that the proposed hybrid optimization approach has an effectiveness to solve for complex optimization problems.
Highlights
Along with a modern society, human people have been facing a fast increase in stroke or accidence
To improve overall static performances, including the strain energy and reaction torque, a hybrid optimization approach was developed. This approach was an integration of FEM, RSM, Kigring metamodel method, and MOGA
The sensitivity analysis through the RSM was conducted to determine influence of each factor. It found that the parameter t3 has a largest contribution to the strain energy with 50.78% (Fvalue of 22.23), followed by parameter t2 with contribution of 13.16% (F-value of 13.67), and parameter t1 has a smallest contribution of 8.84%
Summary
Along with a modern society, human people have been facing a fast increase in stroke or accidence. To support the disabled people, robotic systems are designed and commercialized to assist the upper limb. Robotic devices for upper-limb rehabilitation were proposed for shoulder exercises [1]. A whole arm wearable robotic exoskeleton is used for rehabilitation and to assist upper limb [4]. The reason is because the devices must take a motor to generate a moment, a gear pairs to transfer motions, a coil spring to store and release an elastic energy, and submechanical elements Their mechanical elements are assembled based on kinematic joints, and this results in undesired clearances. If the established mathematical equations are wrong, the predicted results are unaccurate For these reasons, this study introduces a new data-driven multiobjective optimization technique for optimizing the performances of the proposed joint to decrease the modeling errors. A validation is performed to evaluate predicted results and efficiency of the proposed approach
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
