Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design
This paper presents a comprehensive study on the design optimization of a robotic gripper, focusing on both the gripper modeling and the optimization of its parallel mechanism structure. This study integrates the Black-winged Kite Algorithm (BKA), Finite Element Analysis (FEA), Backpropagation Neura...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publications
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/117944/ http://psasir.upm.edu.my/id/eprint/117944/1/117944.pdf |
| _version_ | 1848867386852114432 |
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| author | Haohao, Ma As’arry, Azizan Yanwei, Feng Lulu, Cheng Delgoshaei, Aidin Ismail, Mohd Idris Shah Ramli, Hafiz Rashidi |
| author_facet | Haohao, Ma As’arry, Azizan Yanwei, Feng Lulu, Cheng Delgoshaei, Aidin Ismail, Mohd Idris Shah Ramli, Hafiz Rashidi |
| author_sort | Haohao, Ma |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents a comprehensive study on the design optimization of a robotic gripper, focusing on both the gripper modeling and the optimization of its parallel mechanism structure. This study integrates the Black-winged Kite Algorithm (BKA), Finite Element Analysis (FEA), Backpropagation Neural Network (BPNN), and response surface optimization techniques. The Good Point Set (GPS), nonlinear convergence factor, and adaptive t-distribution method improve BKA, which enhances exploration and exploitation performance, convergence speed, and solution quality. Subsequently, the parallel mechanism structure is designed to minimize the total mass, total deformation, and maximum equivalent stress. The central composite design (CCD) method was used to design the FEA experiment and establish the BKA-BPNN regression prediction model. The RMSE of this model’s training set and test set are 0.001615 and 0.0029328. A response surface optimization model is constructed to determine the best design solution. The optimized design achieves a 33.12% reduction in maximum equivalent stress, a 1.47% decrease in total mass, and a 0.16% reduction in maximum total deformation. This study provides valuable insights into the design optimization process for robotic grippers, showcasing the effectiveness of the proposed methodologies in enhancing performance while reducing mass and improving structural integrity. |
| first_indexed | 2025-11-15T14:35:41Z |
| format | Article |
| id | upm-117944 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:35:41Z |
| publishDate | 2024 |
| publisher | SAGE Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1179442025-06-17T04:25:48Z http://psasir.upm.edu.my/id/eprint/117944/ Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design Haohao, Ma As’arry, Azizan Yanwei, Feng Lulu, Cheng Delgoshaei, Aidin Ismail, Mohd Idris Shah Ramli, Hafiz Rashidi This paper presents a comprehensive study on the design optimization of a robotic gripper, focusing on both the gripper modeling and the optimization of its parallel mechanism structure. This study integrates the Black-winged Kite Algorithm (BKA), Finite Element Analysis (FEA), Backpropagation Neural Network (BPNN), and response surface optimization techniques. The Good Point Set (GPS), nonlinear convergence factor, and adaptive t-distribution method improve BKA, which enhances exploration and exploitation performance, convergence speed, and solution quality. Subsequently, the parallel mechanism structure is designed to minimize the total mass, total deformation, and maximum equivalent stress. The central composite design (CCD) method was used to design the FEA experiment and establish the BKA-BPNN regression prediction model. The RMSE of this model’s training set and test set are 0.001615 and 0.0029328. A response surface optimization model is constructed to determine the best design solution. The optimized design achieves a 33.12% reduction in maximum equivalent stress, a 1.47% decrease in total mass, and a 0.16% reduction in maximum total deformation. This study provides valuable insights into the design optimization process for robotic grippers, showcasing the effectiveness of the proposed methodologies in enhancing performance while reducing mass and improving structural integrity. SAGE Publications 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/117944/1/117944.pdf Haohao, Ma and As’arry, Azizan and Yanwei, Feng and Lulu, Cheng and Delgoshaei, Aidin and Ismail, Mohd Idris Shah and Ramli, Hafiz Rashidi (2024) Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design. Advances in Mechanical Engineering, 16 (10). pp. 1-16. ISSN 1687-8132; eISSN: 1687-8140 https://journals.sagepub.com/doi/10.1177/16878132241288402 10.1177/16878132241288402 |
| spellingShingle | Haohao, Ma As’arry, Azizan Yanwei, Feng Lulu, Cheng Delgoshaei, Aidin Ismail, Mohd Idris Shah Ramli, Hafiz Rashidi Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design |
| title | Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design |
| title_full | Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design |
| title_fullStr | Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design |
| title_full_unstemmed | Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design |
| title_short | Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design |
| title_sort | improved black-winged kite algorithm and finite element analysis for robot parallel gripper design |
| url | http://psasir.upm.edu.my/id/eprint/117944/ http://psasir.upm.edu.my/id/eprint/117944/ http://psasir.upm.edu.my/id/eprint/117944/ http://psasir.upm.edu.my/id/eprint/117944/1/117944.pdf |