| SONG Zhi-long,LYU Bing-hai,KE Ming-feng,YANG Yi-bin,SHAO Qi,YUAN Ju-long,Duc-nam Nguyen.Removal Rate Model of Deterministic Shear Thickening Polishing Material Based on BP Neural Network[J],49(11):320-325, 357 |
| Removal Rate Model of Deterministic Shear Thickening Polishing Material Based on BP Neural Network |
| Received:January 07, 2020 Revised:April 15, 2020 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2020.11.037 |
| KeyWord:shear thickening polishing BP neural networks deterministic polishing removal rate removal rate model |
| Author | Institution |
| SONG Zhi-long |
Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, Hangzhou , China |
| LYU Bing-hai |
Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, Hangzhou , China |
| KE Ming-feng |
Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, Hangzhou , China |
| YANG Yi-bin |
Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, Hangzhou , China |
| SHAO Qi |
Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, Hangzhou , China |
| YUAN Ju-long |
Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology of the Ministry of Education, Zhejiang University of Technology, Hangzhou , China |
| Duc-nam Nguyen |
Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City , Vietnam |
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| Abstract: |
| The work aims to establish the removal rate model of deterministic shear thickening polishing (DSTP) based on BP neutral network by training the data of DSTP under different conditions, to provide basis for the deterministic removal control of material at polishing point. DSTP orthogonal experiment was conducted on BK7 flat glass, and the results were analyzed to compare the weight of each factor’s (polishing head speed, polishing gap, concentration of polishing slurry) impact on material removal rate of polishing point, to determine the input parameters of BP neural network. The number of hidden layer nodes was determined by experimental equation initially, and then the whole structure of network model was constructed by comparing the performance of the model under different hidden layer nodes. The final material removal rate model was established based on the experimental data designed network trained by the model. The comparison between the model prediction results and the experimental results showed that the relative error was less than 6.8% between the output of the established BP neural network prediction model and the measured results, proving the accuracy of the prediction model. The traditional theoretical model is difficult to accurately describe the complicated nonlinear mapping relationship between the process parameters of DSTP and the peak removal rate. The self-learning and adaptive ability of BP neural network can overcome this problem and provide a novel strategy for building the removal rate model of the deterministic shear thickening polishing process. |
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