| PENG Bin-bin,YAN Xian-guo,DU Juan.Surface Quality Prediction Based on BP and RBF Neural Networks[J],49(10):324-328 |
| Surface Quality Prediction Based on BP and RBF Neural Networks |
| Received:August 30, 2019 Revised:October 20, 2020 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2020.10.038 |
| KeyWord:milling surface roughness RBF neural network quality prediction BP neural network |
| Author | Institution |
| PENG Bin-bin |
School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan , China |
| YAN Xian-guo |
School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan , China |
| DU Juan |
School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan , China |
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| Abstract: |
| The work aims to study the role of RBF and BP neural networks in milling, so as to realize the prediction of milling quality and improve the milling performance. Firstly, the circular milling cutter was compared with the commonly used spherical milling cutter, and then based on the MATLAB platform, an RBF neural network model was established with the milling speed, feed and milling depth as input parameters and surface roughness as output parameters. The RBF neural network model was trained through a large amount of experimental data, and then the trained RBF neural network model was used to predict the surface roughness, and the predicted value was compared with the measured value to verify the prediction performance of the RBF neural network. After the BP neural network model was trained in the same way, the prediction results of the model established with the RBF neural network were compared. The absolute value of the surface roughness relative error by the RBF method did not exceed 6%, the maximum error was 0.056 098, the average error was 0.022 277, while the maximum error of the BP method was 0.074 947, and the average error was 0.036 578. The processing quality of circular milling cutter is better. RBF neural network model has better prediction ability than BP neural network, and has shorter modeling time, higher convergence speed, stable training process and faster learning speed, and can effectively predict the milling quality. |
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