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| Simulation on Process Optimization of Double-based Oblate Spherical Powder |
| Received:May 09, 2018 Revised:July 25, 2018 |
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| DOI：10.7643/ issn.1672-9242.2018.07.007 |
| KeyWord:double-based oblate spherical powder process optimization BP neural network pelletization quality |
| Author | Institution |
| WANG Dong-lei |
Institute of Chemical Materials, China Academy of Engineering Physics, Chengdu , China |
| ZHANG Zhi-yu |
State Key Laboratory of Mechanical Transmissions, College of Mechanical Engineering, Chongqing University, Chongqing , China |
| YIN Ai-jun |
State Key Laboratory of Mechanical Transmissions, College of Mechanical Engineering, Chongqing University, Chongqing , China |
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| Abstract: |
| Objective To optimize pelletization process parameters of double-based oblate spherical powder, and solve the problems of large deviation in diameter and web size of double-based oblate spherical powder after pelletization process caused by insufficient theoretical research, unclear control model and regulation of production process parameters depended on the artificial experience. Methods The ability of BP (back propagation) neural network in handling complex nonlinear mapping problems was applied to build model between pelletization process parameters and pelletization quality index, and train the model with pelletization process simulation data. The BP neural network model obtained in training was used to optimize the process parameters. At the same time, the simulation data was used to test the reliability of the model. Results After training, the mean square error of the BP neural network was 0.001, the error rate of the pelletization diameter was 1.27% with 2.08% for web size. The errors of the quality parameters were small. The technological requirements were met. Conclusion The BP neural network model has high precision and is suitable for the optimization of the energetic material process. The proposed palletization process optimization method can effectively reduce the trial cost of the palletization and shorten the production cycle. |
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