| ZHAO Chuan-ying,ZHAO Yu-gang,LIU Ning,SONG Pan-pan,GAO Yue-wu,ZHANG Yong,LIU Guang-xin.Optimization of Process Parameters of Magnetic Abrasive Finishing TC4 Material Based on Neural Network and Genetic Algorithm[J],49(2):316-321 |
| Optimization of Process Parameters of Magnetic Abrasive Finishing TC4 Material Based on Neural Network and Genetic Algorithm |
| Received:June 05, 2019 Revised:February 20, 2020 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2020.02.040 |
| KeyWord:magnetic abrasive finishing TC4 orthogonal experiment neural network genetic algorithm roughness |
| Author | Institution |
| ZHAO Chuan-ying |
School of Mechanical Engineering, Shandong University of Technology, Zibo , China |
| ZHAO Yu-gang |
School of Mechanical Engineering, Shandong University of Technology, Zibo , China |
| LIU Ning |
School of Mechanical Engineering, Shandong University of Technology, Zibo , China |
| SONG Pan-pan |
School of Mechanical Engineering, Shandong University of Technology, Zibo , China |
| GAO Yue-wu |
School of Mechanical Engineering, Shandong University of Technology, Zibo , China |
| ZHANG Yong |
School of Mechanical Engineering, Shandong University of Technology, Zibo , China |
| LIU Guang-xin |
School of Mechanical Engineering, Shandong University of Technology, Zibo , China |
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| Abstract: |
| The work aims to improve the surface quality of TC4 materials by magnetic abrasive finishing, establish the relationship between processing parameters and roughness by BP neural network, and find the optimal combination of process parameters by genetic algorithm. The diamond magnetic abrasive prepared by gas-solid two-phase double-stage atomization and rapid solidification was used to perform L9(34) orthogonal test on TC4 material workpiece. BP neural network with the structure of 4-12-1 was established by Matlab software. BP was trained according to orthogonal test results to explore the relationship between the spindle speed n, working gap δ, feed rate v, abrasive size D and roughness Ra. The BP neural network training results were evaluated by coefficient of determination R2. Based on the trained BP neutral networks, genetic algorithms were used to globally optimize process parameters. The calculated optimized process parameters were used to conduct experiment and measure surface roughness and then compare such roughness with the calculated roughness Ra. The prediction error of BP neural network was less than 1.5%, the model optimized by coefficient of determination R2 could make effective and reliable prediction under the condition of fewer samples. The results of genetic algorithm optimization: the optimum roughness was 0.0951 μm at spindle speed of 1021.26 r/min, machining gap of 1.52 mm, feed rate of 1.04 mm/min, and abrasive size of 197.91 μm. The adjusted process parameters were: spindle speed of 1020 r/min, machining gap of 1.50 mm, feed rate of 1.0 mm/min and abrasive size of 196 μm. The test roughness was 0.093 μm, and the error from the calculated optimal surface roughness was 2.21%. The combination of magnetic abrasive finishing and optimization parameters can effectively improve the surface quality of TC4 material after processing. |
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