| YANG Zhen-kai,WANG Hai-jun,LIU Ming,WANG Jing-chen.Optimization of Spraying Process Parameters for Fe-based Alloy Based on BP Neural Network Model[J],44(9):1-6 |
| Optimization of Spraying Process Parameters for Fe-based Alloy Based on BP Neural Network Model |
| Received:June 23, 2015 Revised:September 20, 2015 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2015.09.001 |
| KeyWord:supersonic plasma spray Fe base alloy powder BP neural network nonlinear fitting output prediction parameters optimization |
| Author | Institution |
| YANG Zhen-kai |
Academy of Armored Forces Engineering, Beijing , China |
| WANG Hai-jun |
Academy of Armored Forces Engineering, Beijing , China |
| LIU Ming |
Academy of Armored Forces Engineering, Beijing , China |
| WANG Jing-chen |
Hebei University of Technology, Tianjin , China |
|
| Hits: |
| Download times: |
| Abstract: |
| Objective BP neural network has the capability of self-learning, self training and output prediction, which could be a powerful tool to research the parameter optimization problem in thermal spraying process. Methods Relying on the high-efficiency supersonic plasma spray system (HEPJet) platform, using Fe-based alloy powder as the spraying material, the flow rate of main gas, spraying power and distance were set as the inputs of the model, while the coating deposition rate and hardness were set as model outputs. Through continuous adjustment of the number of hidden layer nodes, the BP neural network with a 3-7-2 network structure was eventually built to optimize the process parameters. The optimized parameters were then used to obtain the Fe-based alloy coating, test its performance and calculate the error. Results The optimized parameters according to the neural network optimized were: main gas flow 96 L / min, electric power 56 kW, spraying distance 95 mm. After the experiment, the coating hardness and deposition rate of coating were measured. Its average increment of coating thickness was 360 μm, and the average increment of coating hardnessis was 672HV0. 3, while the model predicted values were 332 μm and 611HV0. 3, respectively. Comparing with the predicted values, the errors were 7. 8% and 9. 1% , respectively. Conclusion According to the results of simulation and experiment, the accuracy of the BP neural network for the optimization of plasma spray parameters was relatively high, and the error was acceptable. It is scientific and reliable to use BP neural network to deal with the problems of thermal spraying parameters optimization |
| Close |
|
|
|