Multi-objective optimization of current-assisted splitting spinning of small module tooth-shaped part based on the combination of BP neural network and NSGA-II algorithm
Small module tooth-shaped parts (SMTSPs) with characteristics of hollow, thin wall-thickness made of difficult-to-deformed metals, are one of the most precision transmission components, which are traditionally manufactured by tooth hobbing or tooth shaping. Current-assisted splitting spinning (CASS)...
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EDP Sciences
2024-01-01
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| Series: | Manufacturing Review |
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| Online Access: | https://mfr.edp-open.org/articles/mfreview/full_html/2024/01/mfreview240057/mfreview240057.html |
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| _version_ | 1849738629005443072 |
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| author | Zhou Haoyang Xia Qinxiang Xiao Gangfeng Chen Can |
| author_facet | Zhou Haoyang Xia Qinxiang Xiao Gangfeng Chen Can |
| author_sort | Zhou Haoyang |
| collection | DOAJ |
| description | Small module tooth-shaped parts (SMTSPs) with characteristics of hollow, thin wall-thickness made of difficult-to-deformed metals, are one of the most precision transmission components, which are traditionally manufactured by tooth hobbing or tooth shaping. Current-assisted splitting spinning (CASS) has been introduced as a method to achieve integrated manufacturing of SMTSPs. A coupled electrical-thermal-mechanical finite element analysis (FEA) model was established based on the ABAQUS software, the deformation characteristics of the small module tooth and the mechanism of tooth filling under current-assisted splitting spinning were investigated. A BP neural network (BPNN) was used to establish the mapping relationship between process parameters of CASS and forming quality evaluation metrics, and the Non-dominated Sorting Genetic Algorithm (NSGA-II) multi-objective genetic optimization algorithm was employed to optimize the forming process parameters. The results show that the material at the tooth tip along the radial direction is in the state of tensile stress along radial and compressive stresses along tangential and axial directions, which promotes the radial flowing of the material and is beneficial the tooth filling of SMTSPs; the tooth saturation increases obviously under pulse current comparing without pulse current; the BPNN combined with the NSGA-II algorithm can reliably optimize the process parameters of the CASS, improving the forming quality of SMTSPs; experiments verified the feasibility of the process and the accuracy of the predictive model based on the optimization results. |
| format | Article |
| id | doaj-art-587f013cc3b54bf1901357e7b616a2e8 |
| institution | DOAJ |
| issn | 2265-4224 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Manufacturing Review |
| spelling | doaj-art-587f013cc3b54bf1901357e7b616a2e82025-08-20T03:06:30ZengEDP SciencesManufacturing Review2265-42242024-01-01112410.1051/mfreview/2024023mfreview240057Multi-objective optimization of current-assisted splitting spinning of small module tooth-shaped part based on the combination of BP neural network and NSGA-II algorithmZhou Haoyang0Xia Qinxiang1Xiao Gangfeng2Chen Can3School of Mechanical and Automotive Engineering, South China University of TechnologySchool of Mechanical and Automotive Engineering, South China University of TechnologySchool of Mechanical and Automotive Engineering, South China University of TechnologySchool of Mechanical and Automotive Engineering, South China University of TechnologySmall module tooth-shaped parts (SMTSPs) with characteristics of hollow, thin wall-thickness made of difficult-to-deformed metals, are one of the most precision transmission components, which are traditionally manufactured by tooth hobbing or tooth shaping. Current-assisted splitting spinning (CASS) has been introduced as a method to achieve integrated manufacturing of SMTSPs. A coupled electrical-thermal-mechanical finite element analysis (FEA) model was established based on the ABAQUS software, the deformation characteristics of the small module tooth and the mechanism of tooth filling under current-assisted splitting spinning were investigated. A BP neural network (BPNN) was used to establish the mapping relationship between process parameters of CASS and forming quality evaluation metrics, and the Non-dominated Sorting Genetic Algorithm (NSGA-II) multi-objective genetic optimization algorithm was employed to optimize the forming process parameters. The results show that the material at the tooth tip along the radial direction is in the state of tensile stress along radial and compressive stresses along tangential and axial directions, which promotes the radial flowing of the material and is beneficial the tooth filling of SMTSPs; the tooth saturation increases obviously under pulse current comparing without pulse current; the BPNN combined with the NSGA-II algorithm can reliably optimize the process parameters of the CASS, improving the forming quality of SMTSPs; experiments verified the feasibility of the process and the accuracy of the predictive model based on the optimization results.https://mfr.edp-open.org/articles/mfreview/full_html/2024/01/mfreview240057/mfreview240057.htmlsmall module tooth-shaped partscurrent-assisted splitting spinningtooth fillingbp neural networknsga-ii algorithmmulti-objective optimization |
| spellingShingle | Zhou Haoyang Xia Qinxiang Xiao Gangfeng Chen Can Multi-objective optimization of current-assisted splitting spinning of small module tooth-shaped part based on the combination of BP neural network and NSGA-II algorithm Manufacturing Review small module tooth-shaped parts current-assisted splitting spinning tooth filling bp neural network nsga-ii algorithm multi-objective optimization |
| title | Multi-objective optimization of current-assisted splitting spinning of small module tooth-shaped part based on the combination of BP neural network and NSGA-II algorithm |
| title_full | Multi-objective optimization of current-assisted splitting spinning of small module tooth-shaped part based on the combination of BP neural network and NSGA-II algorithm |
| title_fullStr | Multi-objective optimization of current-assisted splitting spinning of small module tooth-shaped part based on the combination of BP neural network and NSGA-II algorithm |
| title_full_unstemmed | Multi-objective optimization of current-assisted splitting spinning of small module tooth-shaped part based on the combination of BP neural network and NSGA-II algorithm |
| title_short | Multi-objective optimization of current-assisted splitting spinning of small module tooth-shaped part based on the combination of BP neural network and NSGA-II algorithm |
| title_sort | multi objective optimization of current assisted splitting spinning of small module tooth shaped part based on the combination of bp neural network and nsga ii algorithm |
| topic | small module tooth-shaped parts current-assisted splitting spinning tooth filling bp neural network nsga-ii algorithm multi-objective optimization |
| url | https://mfr.edp-open.org/articles/mfreview/full_html/2024/01/mfreview240057/mfreview240057.html |
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