Research on cutting tool edge geometry design based on SVR-PSO
In order to optimize the design of the tool edge, an intelligent method was used for modeling and optimization. The tool edge design method based on support vector regression (SVR) and particle swarm optimization (PSO) was proposed. By combining tool edge parameters and processing condition paramete...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2024-04, Vol.131 (9-10), p.5047-5059 |
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| container_issue | 9-10 |
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| container_title | International journal of advanced manufacturing technology |
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| creator | Jiang, Yimin Huang, Wei Tian, Yu Yang, Mingyang Xu, Wenwu An, Yanjie Li, Jing Li, Junqi Zhou, Ming |
| description | In order to optimize the design of the tool edge, an intelligent method was used for modeling and optimization. The tool edge design method based on support vector regression (SVR) and particle swarm optimization (PSO) was proposed. By combining tool edge parameters and processing condition parameters, and learning from empirical data, a functional model was established between tool life and edge parameters and processing condition parameters. Taking the tool life as the objective function, the optimal edge profile design parameters were solved under different processing condition parameters. The T-shape tool was taken as a case for verification. The SVR-PSO function model was established and solved based on the processing condition parameters, and the optimized edge design parameters and predicted tool life were obtained. The results showed that the deviation between the calculated and actual tool life was less than 6.4%. This method was feasible and practical and has been applied in the design department of tool manufacturing companies. |
| doi_str_mv | 10.1007/s00170-024-13096-8 |
| format | Article |
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The tool edge design method based on support vector regression (SVR) and particle swarm optimization (PSO) was proposed. By combining tool edge parameters and processing condition parameters, and learning from empirical data, a functional model was established between tool life and edge parameters and processing condition parameters. Taking the tool life as the objective function, the optimal edge profile design parameters were solved under different processing condition parameters. The T-shape tool was taken as a case for verification. The SVR-PSO function model was established and solved based on the processing condition parameters, and the optimized edge design parameters and predicted tool life were obtained. The results showed that the deviation between the calculated and actual tool life was less than 6.4%. This method was feasible and practical and has been applied in the design department of tool manufacturing companies.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-024-13096-8</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Advanced manufacturing technologies ; Algorithms ; Artificial intelligence ; CAE) and Design ; Computer-Aided Engineering (CAD ; Cutting tools ; Design optimization ; Design parameters ; Design techniques ; Energy consumption ; Engineering ; Geometry ; Industrial and Production Engineering ; Manufacturing ; Mathematical models ; Mechanical Engineering ; Media Management ; Methods ; Original Article ; Particle swarm optimization ; Regression analysis ; Support vector machines ; Tool life</subject><ispartof>International journal of advanced manufacturing technology, 2024-04, Vol.131 (9-10), p.5047-5059</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. 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The tool edge design method based on support vector regression (SVR) and particle swarm optimization (PSO) was proposed. By combining tool edge parameters and processing condition parameters, and learning from empirical data, a functional model was established between tool life and edge parameters and processing condition parameters. Taking the tool life as the objective function, the optimal edge profile design parameters were solved under different processing condition parameters. The T-shape tool was taken as a case for verification. The SVR-PSO function model was established and solved based on the processing condition parameters, and the optimized edge design parameters and predicted tool life were obtained. The results showed that the deviation between the calculated and actual tool life was less than 6.4%. 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The tool edge design method based on support vector regression (SVR) and particle swarm optimization (PSO) was proposed. By combining tool edge parameters and processing condition parameters, and learning from empirical data, a functional model was established between tool life and edge parameters and processing condition parameters. Taking the tool life as the objective function, the optimal edge profile design parameters were solved under different processing condition parameters. The T-shape tool was taken as a case for verification. The SVR-PSO function model was established and solved based on the processing condition parameters, and the optimized edge design parameters and predicted tool life were obtained. The results showed that the deviation between the calculated and actual tool life was less than 6.4%. 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| subjects | Advanced manufacturing technologies Algorithms Artificial intelligence CAE) and Design Computer-Aided Engineering (CAD Cutting tools Design optimization Design parameters Design techniques Energy consumption Engineering Geometry Industrial and Production Engineering Manufacturing Mathematical models Mechanical Engineering Media Management Methods Original Article Particle swarm optimization Regression analysis Support vector machines Tool life |
| title | Research on cutting tool edge geometry design based on SVR-PSO |
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