| LI Wen-long,CHEN Yan,ZHAO Yang,LYU Yi-ni.Optimizing Technological Parameters of Magnetite Grinding TC4 Elbow by Neural Network and Genetic Algorithms[J],49(6):330-336 |
| Optimizing Technological Parameters of Magnetite Grinding TC4 Elbow by Neural Network and Genetic Algorithms |
| Received:May 30, 2019 Revised:June 20, 2020 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2020.06.040 |
| KeyWord:magnetic particle grinding elbow inner surface surface roughness BP neural network genetic algorithm TC4 titanium alloy |
| Author | Institution |
| LI Wen-long |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan , China |
| CHEN Yan |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan , China |
| ZHAO Yang |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan , China |
| LYU Yi-ni |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan , China |
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| Abstract: |
| The work aims to optimize the process parameters of magnetic abrasive finishing to improve the magnetic abrasive finishing quality and processing efficiency of the inner surface of the TC4 elbow. Firstly, the optimum surface quality was set as an optimization target. Secondly, the four main process parameters affecting the inner surface quality of the magnetic abrasive finishing were taken as an optimization object, the number of nodes in the hidden layers of the neural network to be set up was tested to select optimal value. Thirdly, a nonlinear mapping model that reflects the internal surface roughness and main process parameters of the TC4 elbow was establish. Finally, by using genetic algorithm, the optimal surface roughness of TC4 elbow and the optimal technological parameter combination of magnetic abrasive finishing for TC4 elbow was obtained, and the accuracy of the prediction results was verified by experiments. By establishing a BP neural network with a structure of 4-5-1 and predicting with genetic algorithm, the optimal process parameter configuration of the TC4 elbow for magnetic abrasive finishing was obtained as follows: the magnetic pole speed was 570 r/min, the machining gap was 2.0 mm, the diameter of the abrasive was 178 μm (80 meshes) and the feed rate was 80 mm/min. The mapping model created by the BP neural network to reflect the surface roughness of the inner surface of the TC4 elbow and the process parameters of the inner surface of the TC4 elbow has good precision, and the optimum process parameter is obtained by global optimization with genetic algorithm. It is a new method with high accuracy to optimize the processing parameters of the inner surface of TC4 elbow of magnetic abrasive finishing. |
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