Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device
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. T...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/3231914 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849409804906266624 |
|---|---|
| author | Duc Nam Nguyen Thanh-Phong Dao Ngoc Le Chau Van Anh Dang |
| author_facet | Duc Nam Nguyen Thanh-Phong Dao Ngoc Le Chau Van Anh Dang |
| author_sort | Duc Nam Nguyen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7dc738e73fb94147a4f436f6552f14e5 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7dc738e73fb94147a4f436f6552f14e52025-08-20T03:35:23ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/32319143231914Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive DeviceDuc Nam Nguyen0Thanh-Phong Dao1Ngoc Le Chau2Van Anh Dang3Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, VietnamDivision of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, VietnamFaculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, VietnamFaculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, VietnamModeling 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.http://dx.doi.org/10.1155/2019/3231914 |
| spellingShingle | Duc Nam Nguyen Thanh-Phong Dao Ngoc Le Chau Van Anh Dang Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device Complexity |
| title | Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device |
| title_full | Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device |
| title_fullStr | Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device |
| title_full_unstemmed | Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device |
| title_short | Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device |
| title_sort | hybrid approach of finite element method kigring metamodel and multiobjective genetic algorithm for computational optimization of a flexure elbow joint for upper limb assistive device |
| url | http://dx.doi.org/10.1155/2019/3231914 |
| work_keys_str_mv | AT ducnamnguyen hybridapproachoffiniteelementmethodkigringmetamodelandmultiobjectivegeneticalgorithmforcomputationaloptimizationofaflexureelbowjointforupperlimbassistivedevice AT thanhphongdao hybridapproachoffiniteelementmethodkigringmetamodelandmultiobjectivegeneticalgorithmforcomputationaloptimizationofaflexureelbowjointforupperlimbassistivedevice AT ngoclechau hybridapproachoffiniteelementmethodkigringmetamodelandmultiobjectivegeneticalgorithmforcomputationaloptimizationofaflexureelbowjointforupperlimbassistivedevice AT vananhdang hybridapproachoffiniteelementmethodkigringmetamodelandmultiobjectivegeneticalgorithmforcomputationaloptimizationofaflexureelbowjointforupperlimbassistivedevice |